Estimating multiparameter partial expected value of perfect information from a probabilistic sensitivity analysis sample: a nonparametric regression approach

Modeling First-Line Daratumumab Use for Newly Diagnosed, Transplant-Ineligible, Multiple Myeloma: A Cost-Effectiveness and Risk Analysis for Healthcare Payers

BACKGROUND AND OBJECTIVE

This study aimed to assess the cost-effectiveness of two regimens regarded as the standard of care for the treatment of newly diagnosed, transplant-ineligible multiple myeloma in Singapore: (1) daratumumab, lenalidomide, and dexamethasone and (2) bortezomib, lenalidomide, and dexamethasone. Additionally, it aimed to explore potential strategies to manage decision uncertainty and mitigate financial risk.

METHODS

A cost-effectiveness analysis from the healthcare system perspective was conducted using a partitioned survival model to estimate lifetime costs and quality-adjusted life years (QALYs) associated with daratumumab-based treatment and the bortezomib-based regimen. The analysis used data from the MAIA and SWOG S0777 trials and incorporated local real-world data where available. Sensitivity analyses were performed to evaluate the robustness of the findings, and a risk analysis was conducted to analyze various payer strategies in terms of their payer strategy and uncertainty burden (P-SUB), which account for the decision uncertainty and the additional cost of choosing a suboptimal intervention.

RESULTS

The incremental cost-effectiveness ratio (ICER) for daratumumab, lenalidomide, and dexamethasone (DRd) compared with bortezomib, lenalidomide, and dexamethasone (VRd) was US $90,364 per QALY gained. The results were sensitive to variations in survival for DRd, postprogression treatment costs, cost of hospice care, and hazard ratio for progression-free survival. The scenarios explored indicated that structural assumptions, such as the time horizon of the analysis, significantly influenced the results due to uncertainties arising from immature trial data and treatment efficacy over time. Among the various payer strategies compared, an upfront price discount for daratumumab emerged as the best approach with the lowest P-SUB at US $14,708.

CONCLUSION

In conclusion, this study finds that daratumumab as a first-line treatment for myeloma exceeds the cost-effectiveness threshold considered in this evaluation. An upfront price reduction is the recommended strategy to manage uncertainties and mitigate financial risks. These findings highlight the importance of targeted payer strategies to address specific types and sources of uncertainty.

Cost-utility analysis of prenatal supplementation with long-chain n-3 fatty acids to reduce the incidence of wheezing and asthma in neonates

INTRODUCTION

Recent evidence indicates that Maternal Supplementation with Long-Chain n-3 Fatty Acids During Pregnancy Substantially Mitigates Offspring's Asthma. Adding information regarding its cost-utility will undoubtedly allow its adoption, or not, in clinical practice guidelines. This research aimed to determine the cost-utility of LCPUFA supplementation in the third trimester of pregnancy to reduce the risk of wheezing and asthma in infants in Colombia.

METHODS

A Markov model was formulated to estimate the cost and quality-adjusted life-years (QALYs) attributed to individuals with severe asthma in Colombia, with a time horizon of five years and a cycle length of two weeks. Probabilistic sensitivity analysis and a value of information (VOI) analysis were conducted to evaluate the uncertainties in the case base. Cost-utility was assessed at a willingness-to-pay (WTP) value of US$5180. All costs were adjusted to 2021 with a 5% annual discounting rate for cost and QALYs.

RESULTS

The mean incremental cost of LCPUFA supplementation versus no supplementation was US-43.65. The mean incremental benefit of LCPUFA supplementation versus no supplementation was 0.074 QALY. The incremental cost-utility ratio was estimated at US$590.68 per QALY. The outcomes derived from our primary analysis remained robust when subjected to variations in all underlying assumptions and parameter values.

CONCLUSION

Supplementation strategy supplementation with long-chain n-3 fatty acids during pregnancy is cost-effective in reducing the risk of developing asthma during childhood in Colombia.

Impact of Structural Differences on the Modeled Cost-Effectiveness of Noninvasive Prenatal Testing

BACKGROUND

Noninvasive prenatal testing (NIPT) was developed to improve the accuracy of prenatal screening to detect chromosomal abnormalities. Published economic analyses have yielded different incremental cost-effective ratios (ICERs), leading to conclusions of NIPT being dominant, cost-effective, and cost-ineffective. These analyses have used different model structures, and the extent to which these structural variations have contributed to differences in ICERs is unclear.

AIM

To assess the impact of different model structures on the cost-effectiveness of NIPT for the detection of trisomy 21 (T21; Down syndrome).

METHODS

A systematic review identified economic models comparing NIPT to conventional screening. The key variations in identified model structures were the number of health states and modeling approach. New models with different structures were developed in TreeAge and populated with consistent parameters to enable a comparison of the impact of selected structural variations on results.

RESULTS

The review identified 34 economic models. Based on these findings, demonstration models were developed: 1) a decision tree with 3 health states, 2) a decision tree with 5 health states, 3) a microsimulation with 3 health states, and 4) a microsimulation with 5 health states. The base-case ICER from each model was 1) USD$34,474 (2023)/quality-adjusted life-year (QALY), 2) USD$14,990 (2023)/QALY, (3) USD$54,983 (2023)/QALY, and (4) NIPT was dominated.

CONCLUSION

Model-structuring choices can have a large impact on the ICER and conclusions regarding cost-effectiveness, which may inadvertently affect policy decisions to support or not support funding for NIPT. The use of reference models could improve international consistency in health policy decision making for prenatal screening.

HIGHLIGHTS

NIPT is a clinical area in which a variety of modeling approaches have been published, with wide variation in reported cost-effectiveness.This study shows that when broader contextual factors are held constant, varying the model structure yields results that range from NIPT being less effective and more expensive than conventional screening (i.e., NIPT was dominated) through to NIPT being more effective and more expensive than conventional screening with an ICER of USD$54,983 (2023)/QALY.Model-structuring choices may inadvertently affect policy decisions to support or not support funding of NIPT. Reference models could improve international consistency in health policy decision making for prenatal screening.

Integrating decision modeling and machine learning to inform treatment stratification

There is increasing interest in moving away from "one size fits all (OSFA)" approaches toward stratifying treatment decisions. Understanding how expected effectiveness and cost-effectiveness varies with patient covariates is a key aspect of stratified decision making. Recently proposed machine learning (ML) methods can learn heterogeneity in outcomes without pre-specifying subgroups or functional forms, enabling the construction of decision rules ('policies') that map individual covariates into a treatment decision. However, these methods do not yet integrate ML estimates into a decision modeling framework in order to reflect long-term policy-relevant outcomes and synthesize information from multiple sources. In this paper, we propose a method to integrate ML and decision modeling, when individual patient data is available to estimate treatment-specific survival time. We also propose a novel implementation of policy tree algorithms to define subgroups using decision model output. We demonstrate these methods using the SPRINT (Systolic Blood Pressure Intervention Trial), comparing outcomes for "standard" and "intensive" blood pressure targets. We find that including ML into a decision model can impact the estimate of incremental net health benefit (INHB) for OSFA policies. We also find evidence that stratifying treatment using subgroups defined by a tree-based algorithm can increase the estimates of the INHB.

Temporary treatment cessation compared with continuation of tyrosine kinase inhibitors for adults with renal cancer: the STAR non-inferiority RCT

Background

There is interest in using treatment breaks in oncology, to reduce toxicity without compromising efficacy.

Trial design

A Phase II/III multicentre, open-label, parallel-group, randomised controlled non-inferiority trial assessing treatment breaks in patients with renal cell carcinoma.

Methods

Patients with locally advanced or metastatic renal cell carcinoma, starting tyrosine kinase inhibitor as first-line treatment at United Kingdom National Health Service hospitals.

Interventions

At trial entry, patients were randomised (1 : 1) to a drug-free interval strategy or a conventional continuation strategy. After 24 weeks of treatment with sunitinib/pazopanib, drug-free interval strategy patients took up a treatment break until disease progression with additional breaks dependent on disease response and patient choice. Conventional continuation strategy patients continued on treatment. Both trial strategies continued until treatment intolerance, disease progression on treatment, withdrawal or death.

Objective

To determine if a drug-free interval strategy is non-inferior to a conventional continuation strategy in terms of the co-primary outcomes of overall survival and quality-adjusted life-years.

Co-primary outcomes

For non-inferiority to be concluded, a margin of ≤ 7.5% in overall survival and ≤ 10% in quality-adjusted life-years was required in both intention-to-treat and per-protocol analyses. This equated to the 95% confidence interval of the estimates being above 0.812 and -0.156, respectively. Quality-adjusted life-years were calculated using the utility index of the EuroQol-5 Dimensions questionnaire.

Results

Nine hundred and twenty patients were randomised (461 conventional continuation strategy vs. 459 drug-free interval strategy) from 13 January 2012 to 12 September 2017. Trial treatment and follow-up stopped on 31 December 2020. Four hundred and eighty-eight (53.0%) patients [240 (52.1%) vs. 248 (54.0%)] continued on trial post week 24. The median treatment-break length was 87 days. Nine hundred and nineteen patients were included in the intention-to-treat analysis (461 vs. 458) and 871 patients in the per-protocol analysis (453 vs. 418). For overall survival, non-inferiority was concluded in the intention-to-treat analysis but not in the per-protocol analysis [hazard ratio (95% confidence interval) intention to treat 0.97 (0.83 to 1.12); per-protocol 0.94 (0.80 to 1.09) non-inferiority margin: 95% confidence interval ≥ 0.812, intention to treat: 0.83 > 0.812 non-inferior, per-protocol: 0.80 < 0.812 not non-inferior]. Therefore, a drug-free interval strategy was not concluded to be non-inferior to a conventional continuation strategy in terms of overall survival. For quality-adjusted life-years, non-inferiority was concluded in both the intention-to-treat and per-protocol analyses [marginal effect (95% confidence interval) intention to treat -0.05 (-0.15 to 0.05); per-protocol 0.04 (-0.14 to 0.21) non-inferiority margin: 95% confidence interval ≥ -0.156]. Therefore, a drug-free interval strategy was concluded to be non-inferior to a conventional continuation strategy in terms of quality-adjusted life-years.

Limitations

The main limitation of the study is the fewer than expected overall survival events, resulting in lower power for the non-inferiority comparison.

Future work

Future studies should investigate treatment breaks with more contemporary treatments for renal cell carcinoma.

Conclusions

Non-inferiority was shown for the quality-adjusted life-year end point but not for overall survival as pre-defined. Nevertheless, despite not meeting the primary end point of non-inferiority as per protocol, the study suggested that a treatment-break strategy may not meaningfully reduce life expectancy, does not reduce quality of life and has economic benefits. Although the treating clinicians' perspectives were not formally collected, the fact that clinicians recruited a large number of patients over a long period suggests support for the study and provides clear evidence that a treatment-break strategy for patients with renal cell carcinoma receiving tyrosine kinase inhibitor therapy is feasible.

Trial registration

This trial is registered as ISRCTN06473203.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment Programme (NIHR award ref: 09/91/21) and is published in full in Health Technology Assessment; Vol. 28, No. 45. See the NIHR Funding and Awards website for further award information.

Gynaecological cancer surveillance for women with Lynch syndrome: systematic review and cost-effectiveness evaluation

Background

Lynch syndrome is an inherited condition which leads to an increased risk of colorectal, endometrial and ovarian cancer. Risk-reducing surgery is generally recommended to manage the risk of gynaecological cancer once childbearing is completed. The value of gynaecological colonoscopic surveillance as an interim measure or instead of risk-reducing surgery is uncertain. We aimed to determine whether gynaecological surveillance was effective and cost-effective in Lynch syndrome.

Methods

We conducted systematic reviews of the effectiveness and cost-effectiveness of gynaecological cancer surveillance in Lynch syndrome, as well as a systematic review of health utility values relating to cancer and gynaecological risk reduction. Study identification included bibliographic database searching and citation chasing (searches updated 3 August 2021). Screening and assessment of eligibility for inclusion were conducted by independent researchers. Outcomes were prespecified and were informed by clinical experts and patient involvement. Data extraction and quality appraisal were conducted and results were synthesised narratively. We also developed a whole-disease economic model for Lynch syndrome using discrete event simulation methodology, including natural history components for colorectal, endometrial and ovarian cancer, and we used this model to conduct a cost-utility analysis of gynaecological risk management strategies, including surveillance, risk-reducing surgery and doing nothing.

Results

We found 30 studies in the review of clinical effectiveness, of which 20 were non-comparative (single-arm) studies. There were no high-quality studies providing precise outcome estimates at low risk of bias. There is some evidence that mortality rate is higher for surveillance than for risk-reducing surgery but mortality is also higher for no surveillance than for surveillance. Some asymptomatic cancers were detected through surveillance but some cancers were also missed. There was a wide range of pain experiences, including some individuals feeling no pain and some feeling severe pain. The use of pain relief (e.g. ibuprofen) was common, and some women underwent general anaesthetic for surveillance. Existing economic evaluations clearly found that risk-reducing surgery leads to the best lifetime health (measured using quality-adjusted life-years) and is cost-effective, while surveillance is not cost-effective in comparison. Our economic evaluation found that a strategy of surveillance alone or offering surveillance and risk-reducing surgery was cost-effective, except for path_PMS2 Lynch syndrome. Offering only risk-reducing surgery was less effective than offering surveillance with or without surgery.

Limitations

Firm conclusions about clinical effectiveness could not be reached because of the lack of high-quality research. We did not assume that women would immediately take up risk-reducing surgery if offered, and it is possible that risk-reducing surgery would be more effective and cost-effective if it was taken up when offered.

Conclusions

There is insufficient evidence to recommend for or against gynaecological cancer surveillance in Lynch syndrome on clinical grounds, but modelling suggests that surveillance could be cost-effective. Further research is needed but it must be rigorously designed and well reported to be of benefit.

Study registration

This study is registered as PROSPERO CRD42020171098.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR129713) and is published in full in Health Technology Assessment; Vol. 28, No. 41. See the NIHR Funding and Awards website for further award information.

Methods to Quantify the Importance of Parameters for Model Updating and Distributional Adaptation

PURPOSE

Decision models are time-consuming to develop; therefore, adapting previously developed models for new purposes may be advantageous. We provide methods to prioritize efforts to 1) update parameter values in existing models and 2) adapt existing models for distributional cost-effectiveness analysis (DCEA).

METHODS

Methods exist to assess the influence of different input parameters on the results of a decision models, including value of information (VOI) and 1-way sensitivity analysis (OWSA). We apply 1) VOI to prioritize searches for additional information to update parameter values and 2) OWSA to prioritize searches for parameters that may vary by socioeconomic characteristics. We highlight the assumptions required and propose metrics that quantify the extent to which parameters in a model have been updated or adapted. We provide R code to quickly carry out the analysis given inputs from a probabilistic sensitivity analysis (PSA) and demonstrate our methods using an oncology case study.

RESULTS

In our case study, updating 2 of 21 probabilistic model parameters addressed 71.5% of the total VOI and updating 3 addressed approximately 100% of the uncertainty. Our proposed approach suggests that these are the 3 parameters that should be prioritized. For model adaptation for DCEA, 46.3% of the total OWSA variation came from a single parameter, while the top 10 input parameters were found to account for more than 95% of the total variation, suggesting efforts should be aimed toward these.

CONCLUSIONS

These methods offer a systematic approach to guide research efforts in updating models with new data or adapting models to undertake DCEA. The case study demonstrated only very small gains from updating more than 3 parameters or adapting more than 10 parameters.

HIGHLIGHTS

It can require considerable analyst time to search for evidence to update a model or to adapt a model to take account of equity concerns.In this article, we provide a quantitative method to prioritze parameters to 1) update existing models to reflect potential new evidence and 2) adapt existing models to estimate distributional outcomes.We define metrics that quantify the extent to which the parameters in a model have been updated or adapted.We provide R code that can quickly rank parameter importance and calculate quality metrics using only the results of a standard probabilistic sensitivity analysis.

Making Drug Approval Decisions in the Face of Uncertainty: Cumulative Evidence versus Value of Information

BACKGROUND

The COVID-19 pandemic underscored the criticality and complexity of decision making for novel treatment approval and further research. Our study aims to assess potential decision-making methodologies, an evaluation vital for refining future public health crisis responses.

METHODS

We compared 4 decision-making approaches to drug approval and research: the Food and Drug Administration's policy decisions, cumulative meta-analysis, a prospective value-of-information (VOI) approach (using information available at the time of decision), and a reference standard (retrospective VOI analysis using information available in hindsight). Possible decisions were to reject, accept, provide emergency use authorization, or allow access to new therapies only in research settings. We used monoclonal antibodies provided to hospitalized COVID-19 patients as a case study, examining the evidence from September 2020 to December 2021 and focusing on each method's capacity to optimize health outcomes and resource allocation.

RESULTS

Our findings indicate a notable discrepancy between policy decisions and the reference standard retrospective VOI approach with expected losses up to $269 billion USD, suggesting suboptimal resource use during the wait for emergency use authorization. Relying solely on cumulative meta-analysis for decision making results in the largest expected loss, while the policy approach showed a loss up to $16 billion and the prospective VOI approach presented the least loss (up to $2 billion).

CONCLUSION

Our research suggests that incorporating VOI analysis may be particularly useful for research prioritization and treatment implementation decisions during pandemics. While the prospective VOI approach was favored in this case study, further studies should validate the ideal decision-making method across various contexts. This study's findings not only enhance our understanding of decision-making strategies during a health crisis but also provide a potential framework for future pandemic responses.

HIGHLIGHTS

This study reviews discrepancies between a reference standard (retrospective VOI, using hindsight information) and 3 conceivable real-time approaches to research-treatment decisions during a pandemic, suggesting suboptimal use of resources.Of all prospective decision-making approaches considered, VOI closely mirrored the reference standard, yielding the least expected value loss across our study timeline.This study illustrates the possible benefit of VOI results and the need for evidence accumulation accompanied by modeling in health technology assessment for emerging therapies.

Exploring Structural Uncertainty in Cost-Effectiveness Modeling of Gestational Diabetes Screening: An Application Example from Norway

BACKGROUND

Screening pregnant women for gestational diabetes mellitus (GDM) has recently been expanded in Norway, although screening eligibility criteria continue to be debated. We aimed to compare the cost-effectiveness of alternative GDM screening strategies and explored structural uncertainty and the value of future research in determining the most cost-effective eligibility criteria for GDM screening in Norway.

DESIGN

We developed a probabilistic decision tree to estimate the total costs and health benefits (i.e., quality-adjusted life-years; QALYs) associated with 4 GDM screening strategies (universal, current guidelines, high-risk, and no screening). We identified the most cost-effective strategy as the strategy with the highest incremental cost-effectiveness ratio below a Norwegian benchmark for cost-effectiveness ($28,400/QALY). We excluded inconclusive evidence on the effects of screening on later maternal type 2 diabetes mellitus (T2DM) in the primary analysis but included this outcome in a secondary analysis using 2 different sources of evidence (i.e., Cochrane or US Preventive Services Task Force). To quantify decision uncertainty, we conducted scenario analysis and value-of-information analyses.

RESULTS

Current screening recommendations were considered inefficient in all analyses, while universal screening was most cost-effective in our primary analysis ($26,014/QALY gained) and remained most cost-effective when we assumed a preventive effect of GDM treatment on T2DM. When we assumed no preventive effect, high-risk screening was preferred ($19,115/QALY gained). When we assumed GDM screening does not prevent perinatal death in scenario analysis, all strategies except no screening exceeded the cost-effectiveness benchmark. In most analyses, decision uncertainty was high.

CONCLUSIONS

The most cost-effective screening strategy, ranging from no screening to universal screening, depended on the source and inclusion of GDM treatment effects on perinatal death and T2DM. Further research on these long-term outcomes could reduce decision uncertainty.

HIGHLIGHTS

This article analyses the cost-effectiveness of 4 alternative gestational diabetes mellitus (GDM) screening strategies in Norway: universal screening, current (broad) screening, high-risk screening, and no screening.The current Norwegian screening recommendations were considered inefficient under all analyses.The most cost-effective screening strategy ranged from no screening to universal screening depending on the source and inclusion of GDM treatment effects on later maternal diabetes and perinatal death.The parameters related to later maternal diabetes and perinatal death accounted for most of the decision uncertainty.

Estimating the value of future research into thromboprophylaxis for women during pregnancy and after delivery: a value of information analysis

BACKGROUND

Risk assessment models (RAMs) are used to select women at increased risk of venous thromboembolism (VTE) during pregnancy and the puerperium for thromboprophylaxis.

OBJECTIVES

To estimate the value of potential future studies that would reduce the decision uncertainty associated with offering thromboprophylaxis according to available RAMs in the following groups: high-risk antepartum women (eg, prior VTE), unselected postpartum women, and postpartum women with risk factors (obesity or cesarean delivery).

METHODS

A decision-analytic model was developed to simulate clinical outcomes, lifetime costs, and quality-adjusted life-years for different thromboprophylaxis strategies, including thromboprophylaxis for all, thromboprophylaxis for none, and RAM-based thromboprophylaxis. The expected value of perfect information analysis was used to determine which factors are associated with high decision uncertainty. The value of future research studies was estimated using expected value of sample information analysis. Costs were assessed from a health and social services perspective.

RESULTS

The expected value of perfect information analysis identified high decision uncertainty for high-risk antepartum women (£21.8 million) and obese postpartum women (£13.4 million), which was largely attributable to uncertainty regarding the effectiveness of thromboprophylaxis in reducing VTE. A randomized controlled trial of thromboprophylaxis compared with none in obese postpartum women is likely to have substantial value (£2.8 million; 300 participants per arm). A trial in women with previous VTE would have higher value but would be less acceptable.

CONCLUSION

Future research should focus on estimating the effectiveness of thromboprophylaxis in obese postpartum women with additional risk factors who have not had a previous VTE.

Bisphosphonate alternative regimens for the prevention of osteoporotic fragility fractures: BLAST-OFF, a mixed-methods study

Background

Bisphosphonates are a class of medication commonly used to treat osteoporosis. Alendronate is recommended as the first-line treatment; however, long-term adherence (both treatment compliance and persistence) is poor. Alternative bisphosphonates are available, which can be given intravenously and have been shown to improve long-term adherence. However, the most clinically effective and cost-effective alternative bisphosphonate regimen remains unclear. What is the most cost-effective bisphosphonate in clinical trials may not be the most cost-effective or acceptable to patients in everyday clinical practice.

Objectives

1. Explore patient, clinician and stakeholder views, experiences and preferences of alendronate compared to alternative bisphosphonates. 2. Update and refine the 2016 systematic review and cost-effectiveness analysis of bisphosphonates, and estimate the value of further research into their benefits. 3. Undertake stakeholder/consensus engagement to identify important research questions and further rank research priorities.

Methods

The study was conducted in two stages, stages 1A and 1B in parallel, followed by stage 2: • Stage 1A - we elicited patient and healthcare experiences to understand their preferences of bisphosphonates for the treatment of osteoporosis. This was undertaken by performing a systematic review and framework synthesis of qualitative studies, followed by semistructured qualitative interviews with participants. • Stage 1B - we updated and expanded the existing Health Technology Assessment systematic review and clinical and cost-effectiveness model, incorporating a more comprehensive review of treatment efficacy, safety, side effects, compliance and long-term persistence. • Stage 2 - we identified and ranked further research questions that need to be answered about the effectiveness and acceptability of bisphosphonates.

Results

Patients and healthcare professionals identified a number of challenges in adhering to bisphosphonate medication, balancing the potential for long-term risk reduction against the work involved in adhering to oral alendronate. Intravenous zoledronate treatment was generally more acceptable, with such regimens perceived to be more straightforward to engage in, although a portion of patients taking alendronate were satisfied with their current treatment. Intravenous zoledronate was found to be the most effective, with higher adherence rates compared to the other bisphosphonates, for reducing the risk of fragility fracture. However, oral bisphosphonates are more cost-effective than intravenous zoledronate due to the high cost of zoledronate administration in hospital. The importance of including patients and healthcare professionals when setting research priorities is recognised. Important areas for research were related to patient factors influencing treatment selection and effectiveness, how to optimise long-term care and the cost-effectiveness of delivering zoledronate in an alternative, non-hospital setting.

Conclusions

Intravenous zoledronate treatment was generally more acceptable to patients and found to be the most effective bisphosphonate and with greater adherence; however, the cost-effectiveness relative to oral alendronate is limited by its higher zoledronate hospital administration costs.

Future work

Further research is needed to support people to make decisions influencing treatment selection, effectiveness and optimal long-term care, together with the clinical and cost-effectiveness of intravenous zoledronate administered in a non-hospital (community) setting.

Limitations

Lack of clarity and limitations in the many studies included in the systematic review may have under-interpreted some of the findings relating to effects of bisphosphonates.

Trial registration

This trial is registered as ISRCTN10491361.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR127550) and is published in full in Health Technology Assessment; Vol. 28, No. 21. See the NIHR Funding and Awards website for further award information.

Value of information dynamics in Disease X vaccine clinical trials

BACKGROUND

Solutions have been proposed to accelerate the development and rollout of vaccines against a hypothetical disease with epidemic or pandemic potential called Disease X. This may involve resolving uncertainties regarding the disease and the new vaccine. However the value for public health of collecting this information will depend on the time needed to perform research, but also on the time needed to produce vaccine doses. We explore this interplay, and its effect on the decision on whether or not to perform research.

METHOD

We simulate numerically the emergence and transmission of a disease in a population using a susceptible-infected-recovered (SIR) compartmental model with vaccination. Uncertainties regarding the disease and the vaccine are represented by parameter prior distributions. We vary the date at which vaccine doses are available, and the date at which information about parameters becomes available. We use the expected value of perfect information (EVPI) and the expected value of partially perfect information (EVPPI) to measure the value of information.

RESULTS

As expected, information has less or no value if it comes too late, or (equivalently) if it can only be used too late. However we also find non trivial dynamics for shorter durations of vaccine development. In this parameter area, it can be optimal to implement vaccination without waiting for information depending on the respective durations of dose production and of clinical research.

CONCLUSION

We illustrate the value of information dynamics in a Disease X outbreak scenario, and present a general approach to properly take into account uncertainties and transmission dynamics when planning clinical research in this scenario. Our method is based on numerical simulation and allows us to highlight non trivial effects that cannot otherwise be investigated.

The cost-effectiveness of germline BRCA testing-guided olaparib treatment in metastatic castration resistant prostate cancer

BACKGROUND

Olaparib targets the DNA repair pathways and has revolutionized the management of metastatic castration resistant prostate cancer (mCRPC). Treatment with the drug should be guided by genetic testing; however, published economic evaluations did not consider olaparib and genetic testing as codependent technologies. This study aims to assess the cost-effectiveness of BRCA germline testing to inform olaparib treatment in mCRPC.

METHODS

We conducted a cost-utility analysis of germline BRCA testing-guided olaparib treatment compared to standard care without testing from an Australian health payer perspective. The analysis applied a decision tree to indicate the germline testing or no testing strategy. A Markov multi-state transition approach was used for patients within each strategy. The model had a time horizon of 5 years. Costs and outcomes were discounted at an annual rate of 5 percent. Decision uncertainty was characterized using probabilistic and scenario analyses.

RESULTS

Compared to standard care, BRCA testing-guided olaparib treatment was associated with an incremental cost of AU$7,841 and a gain of 0.06 quality-adjusted life-years (QALYs). The incremental cost-effectiveness ratio (ICER) was AU$143,613 per QALY. The probability of BRCA testing-guided treatment being cost effective at a willingness-to-pay threshold of AU$100,000 per QALY was around 2 percent; however, the likelihood for cost-effectiveness increased to 66 percent if the price of olaparib was reduced by 30 percent.

CONCLUSION

This is the first study to evaluate germline genetic testing and olaparib treatment as codependent technologies in mCRPC. Genetic testing-guided olaparib treatment may be cost-effective with significant discounts on olaparib pricing.

The feasibility and acceptability of an app-based intervention with brief behavioural support (APPROACH) to promote brisk walking in people diagnosed with breast, prostate and colorectal cancer in the UK

INTRODUCTION

Increased moderate to vigorous physical activity (MVPA) can improve clinical and psychosocial outcomes for people living with and beyond cancer (LWBC). This study aimed to assess the feasibility and acceptability of trial procedures in a pilot randomised controlled trial (RCT) of a theory-driven app-based intervention with behavioural support focused on promoting brisk walking (a form of MVPA) in people LWBC (APPROACH).

METHODS

Participants diagnosed with breast, prostate or colorectal cancer were recruited from a single UK hospital site. Assessments at baseline and 3 months included online questionnaires, device-measured brisk walking (activPAL accelerometer) and self-reported weight and height. Participants were randomised to intervention or control (care as usual). The intervention comprised a non-cancer-specific app to promote brisk walking (National Health Service 'Active 10') augmented with print information about habit formation, a walking planner and two behavioural support telephone calls. Feasibility and acceptability of trial procedures were explored. Initial estimates for physical activity informed a power calculation for a phase III RCT. A preliminary health economics analysis was conducted.

RESULTS

Of those medically eligible, 369/577 (64%) were willing to answer further eligibility questions and 90/148 (61%) of those eligible were enrolled. Feasibility outcomes, including retention (97%), assessment completion rates (>86%) and app download rates in the intervention group (96%), suggest that the trial procedures are acceptable and that the intervention is feasible. The phase III RCT will require 472 participants to be randomised. As expected, the preliminary health economic analyses indicate a high level of uncertainty around the cost-effectiveness of the intervention.

CONCLUSIONS

This pilot study demonstrates that a large trial of the brisk walking intervention with behavioural support is both feasible and acceptable to people LWBC. The results support progression onto a confirmatory phase III trial to determine the efficacy and cost-effectiveness of the intervention.

Thromboprophylaxis during pregnancy and the puerperium: a systematic review and economic evaluation to estimate the value of future research

Background

Pharmacological prophylaxis to prevent venous thromboembolism is currently recommended for women assessed as being at high risk of venous thromboembolism during pregnancy or in the 6 weeks after delivery (the puerperium). The decision to provide thromboprophylaxis involves weighing the benefits, harms and costs, which vary according to the individual's venous thromboembolism risk. It is unclear whether the United Kingdom's current risk stratification approach could be improved by further research.

Objectives

To quantify the current decision uncertainty associated with selecting women who are pregnant or in the puerperium for thromboprophylaxis and to estimate the value of one or more potential future studies that would reduce that uncertainty, while being feasible and acceptable to patients and clinicians.

Methods

A decision-analytic model was developed which was informed by a systematic review of risk assessment models to predict venous thromboembolism in women who are pregnant or in the puerperium. Expected value of perfect information analysis was used to determine which factors are associated with high decision uncertainty and should be the target of future research. To find out whether future studies would be acceptable and feasible, we held workshops with women who have experienced a blood clot or have been offered blood-thinning drugs and surveyed healthcare professionals. Expected value of sample information analysis was used to estimate the value of potential future research studies.

Results

The systematic review included 17 studies, comprising 19 unique externally validated risk assessment models and 1 internally validated model. Estimates of sensitivity and specificity were highly variable ranging from 0% to 100% and 5% to 100%, respectively. Most studies had unclear or high risk of bias and applicability concerns. The decision analysis found that there is substantial decision uncertainty regarding the use of risk assessment models to select high-risk women for antepartum prophylaxis and obese postpartum women for postpartum prophylaxis. The main source of decision uncertainty was uncertainty around the effectiveness of thromboprophylaxis for preventing venous thromboembolism in women who are pregnant or in the puerperium. We found that a randomised controlled trial of thromboprophylaxis in obese postpartum women is likely to have substantial value and is more likely to be acceptable and feasible than a trial recruiting women who have had a previous venous thromboembolism. In unselected postpartum women and women following caesarean section, the poor performance of risk assessment models meant that offering prophylaxis based on these models had less favourable cost effectiveness with lower decision uncertainty.

Limitations

The performance of the risk assessment model for obese postpartum women has not been externally validated.

Conclusions

Future research should focus on estimating the efficacy of pharmacological thromboprophylaxis in pregnancy and the puerperium, and clinical trials would be more acceptable in women who have not had a previous venous thromboembolism.

Study registration

This study is registered as PROSPERO CRD42020221094.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR131021) and is published in full in Health Technology Assessment; Vol. 28, No. 9. See the NIHR Funding and Awards website for further award information.

Identifying the Optimum Strategy for Identifying Adults and Children With Celiac Disease: A Cost-Effectiveness and Value of Information Analysis

OBJECTIVES

Celiac disease (CD) is thought to affect around 1% of people in the United Kingdom, but only approximately 30% are diagnosed. The aim of this work was to assess the cost-effectiveness of strategies for identifying adults and children with CD in terms of who to test and which tests to use.

METHODS

A decision tree and Markov model were used to describe testing strategies and model long-term consequences of CD. The analysis compared a selection of pre-test probabilities of CD above which patients should be screened, as well as the use of different serological tests, with or without genetic testing. Value of information analysis was used to prioritize parameters for future research.

RESULTS

Using serological testing alone in adults, immunoglobulin A (IgA) tissue transglutaminase (tTG) at a 1% pre-test probability (equivalent to population screening) was most cost-effective. If combining serological testing with genetic testing, human leukocyte antigen combined with IgA tTG at a 5% pre-test probability was most cost-effective. In children, the most cost-effective strategy was a 10% pre-test probability with human leukocyte antigen plus IgA tTG. Value of information analysis highlighted the probability of late diagnosis of CD and the accuracy of serological tests as important parameters. The analysis also suggested prioritizing research in adult women over adult men or children.

CONCLUSIONS

For adults, these cost-effectiveness results suggest UK National Screening Committee Criteria for population-based screening for CD should be explored. Substantial uncertainty in the results indicate a high value in conducting further research.

Cost-effectiveness of linezolid to ventilator-associated pneumonia in Colombia

INTRODUCTION

Ventilator-associated pneumonia (VAP) is a prominent cause of morbidity and mortality in intensive care unit (ICU) patients. Due to the increase in Methicillin resistant Staphylococcus aureus infection, it is important to consider other more effective and safer alternatives compared to vancomycin. This motivates evaluating whether the use of an apparently more expensive drug such as linezolid can be cost-effective in Colombia.

METHODS

A decision tree was used to simulate the results in terms of the cost and proportion of cured patients. In the simulation, patients can receive antibiotic treatment with linezolid (LZD 600 mg IV/12 h) or vancomycin (VCM 15 mg/kg iv/12 h) for 7 days, patients they can experience events adverse (renal failure and thrombocytopenia). The model was analyzed probabilistically, and a value of information analysis was conducted to inform the value of conducting further research to reduce current uncertainties in the evidence base. Cost-effectiveness was evaluated at a willingness-to-pay (WTP) value of US$5180.

RESULTS

The mean incremental cost of LZD versus VCM is US$-517. This suggests that LZD is less costly. The proportion of patients cured when treated with LZD compared with VCM is 53 vs. 43%, respectively. The mean incremental benefit of LZD versus VCM is 10 This position of absolute dominance (LZD has lower costs and higher proportion of clinical cure than no supplementation) is unnecessary to estimate the incremental cost-effectiveness ratio. There is uncertainty with a 0.999 probability that LZD is more cost-effective than VCM. Our base-case results were robust to variations in all assumptions and parameters.

CONCLUSION

LNZ is a cost-effective strategy for patients, ≥ 18 years of age, with VAP in Colombia- Our study provides evidence that can be used by decision-makers to improve clinical practice guidelines.

Reflecting Parameter Uncertainty in Addition to Variability in Constrained Healthcare Resource Discrete Event Simulations: Worth Going the Extra Mile or a Road to Nowhere?

OBJECTIVES

Probabilistic sensitivity analysis (PSA) has been shown to reduce bias in outcomes of health economic models. However, only 1 existing study has been identified that incorporates PSA within a resource-constrained discrete event simulation (DES) model. This article aims to assess whether it is feasible and appropriate to use PSA to characterize parameter uncertainty in DES models that are primarily constructed to explore the impact of constrained resources.

METHODS

PSA is incorporated into a new case study of an Emergency Department DES. Structured expert elicitation is used to derive the variability and uncertainty input distributions associated with length of time taken to complete key activities within the Emergency Department. Potential challenges of implementation and analysis are explored.

RESULTS

The results of a trial of the model, which used the best estimates of the elicited means and variability around the time taken to complete activities, provided a reasonable fit to the data for length of time within the Emergency Department. However, there was substantial and skewed uncertainty around the activity times estimated from the elicitation exercise. This led to patients taking almost 3 weeks to leave the Emergency Department in some PSA runs, which would not occur in practice.

CONCLUSIONS

Structured expert elicitation can be used to derive plausible estimates of activity times and their variability, but experts' uncertainty can be substantial. For parameters that have an impact on interactions within a resource-constrained simulation model, PSA can lead to implausible model outputs; hence, other methods may be needed.

Home Gardening of Yellow Cassava and Orange Maize for the Prevention of Nutritional Blindness in Children: An Economic Evaluation and Value of Information Analysis

OBJECTIVES

Vitamin A deficiency is the leading cause of childhood blindness worldwide, affecting mostly Sub-Saharan Africa. We aimed to predict the cost-effectiveness of home gardening (HG) of yellow cassava and orange maize to prevent nutritional blindness in children below 5 years and to assess the likely value of obtaining additional information in reducing uncertainty surrounding its cost-effectiveness.

METHODS

We developed a Markov model and carried out probabilistic sensitivity analysis with a value of information analysis. We costed resources from a societal perspective and outcomes were measured in disability-adjusted life years (DALYs).

RESULTS

HG was estimated to cost an additional Intl$395.00 per DALY averted, with a 72.27% likelihood of being cost-effective at a threshold of Intl$2800 per DALY. The expected value of information was estimated to be Intl$29 843.50 for 1 child or Intl$925 billion for 31 million Nigerian children affected by the decision. Further research is only worthwhile for 1 parameter (relative risk of low serum retinol; expected value of perfect parameter information Intl$29 854.53 per child and Intl$925 billion for 31 million children).

CONCLUSION

HG of yellow cassava and orange maize is expected to be highly cost-effective in preventing nutritional blindness in Nigerian children. Worthwhile further research includes a cost analysis of the intervention and a high-quality randomized trial to assess the effectiveness of HG on serum retinol levels in young children.

Consolidated Health Economic Evaluation Reporting Standards - Value of Information (CHEERS-VOI): Explanation and Elaboration

OBJECTIVES

Although the ISPOR Value of Information (VOI) Task Force's reports outline VOI concepts and provide good-practice recommendations, there is no guidance for reporting VOI analyses. VOI analyses are usually performed alongside economic evaluations for which the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 Statement provides reporting guidelines. Thus, we developed the CHEERS-VOI checklist to provide reporting guidance and checklist to support the transparent, reproducible, and high-quality reporting of VOI analyses.

METHODS

A comprehensive literature review generated a list of 26 candidate reporting items. These candidate items underwent a Delphi procedure with Delphi participants through 3 survey rounds. Participants rated each item on a 9-point Likert scale to indicate its relevance when reporting the minimal, essential information about VOI methods and provided comments. The Delphi results were reviewed at 2-day consensus meetings and the checklist was finalized using anonymous voting.

RESULTS

We had 30, 25, and 24 Delphi respondents in rounds 1, 2, and 3, respectively. After incorporating revisions recommended by the Delphi participants, all 26 candidate items proceeded to the 2-day consensus meetings. The final CHEERS-VOI checklist includes all CHEERS items, but 7 items require elaboration when reporting VOI. Further, 6 new items were added to report information relevant only to VOI (eg, VOI methods applied).

CONCLUSIONS

The CHEERS-VOI checklist should be used when a VOI analysis is performed alongside economic evaluations. The CHEERS-VOI checklist will help decision makers, analysts and peer reviewers in the assessment and interpretation of VOI analyses and thereby increase transparency and rigor in decision making.

Vitamin D supplementation for children with mild to moderate asthma: an economic evaluation

INTRODUCTION

A large proportion of asthma patients remain uncontrolled despite using inhaled corticosteroids. Some add-on therapies such as vitamin D supplements have been recommended for this subgroup of patients. The purpose of this study was to assess the cost-utility of vitamin D supplementation in children with mild to moderate persistent asthma in Colombia.

METHODS

A probabilistic Markov model was created to estimate the cost and quality-adjusted life-years (QALYs) of patients with severe asthma in Colombia. The model was analyzed probabilistically, and a value of information (VOI) analysis was conducted to inform the value of conducting further research to reduce current uncertainties in the evidence base. Cost-effectiveness was evaluated at a willingness-to-pay (WTP) value of US$5180.

RESULTS

The mean incremental cost of vitamin D supplementation versus no supplementation is USD $44.60. The mean incremental benefit of vitamin D supplementation versus no supplementation is 0.05 QALY. This position of absolute dominance (vitamin D supplementation has lower costs and higher QALYs than no supplementation) is unnecessary to estimate the incremental cost-effectiveness ratio. Our base-case results were robust to variations in all assumptions and parameters.

CONCLUSION

Add-on therapy with vitamin D supplementation is a cost-effective strategy for patients between 6 and 17 years of age with mild to moderate asthma in Colombia.

General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations

BACKGROUND

Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty by collecting additional data. EVSI calculations require simulating plausible data sets, typically achieved by evaluating quantile functions at random uniform numbers using standard inverse transform sampling (ITS). This is straightforward when closed-form expressions for the quantile function are available, such as for standard parametric survival models, but these are often unavailable when assuming treatment effect waning and for flexible survival models. In these circumstances, the standard ITS method could be implemented by numerically evaluating the quantile functions at each iteration in a probabilistic analysis, but this greatly increases the computational burden. Thus, our study aims to develop general-purpose methods that standardize and reduce the computational burden of the EVSI data-simulation step for survival data.

METHODS

We developed a discrete sampling method and an interpolated ITS method for simulating survival data from a probabilistic sample of survival probabilities over discrete time units. We compared the general-purpose and standard ITS methods using an illustrative partitioned survival model with and without adjustment for treatment effect waning.

RESULTS

The discrete sampling and interpolated ITS methods agree closely with the standard ITS method, with the added benefit of a greatly reduced computational cost in the scenario with adjustment for treatment effect waning.

CONCLUSIONS

We present general-purpose methods for simulating survival data from a probabilistic sample of survival probabilities that greatly reduce the computational burden of the EVSI data-simulation step when we assume treatment effect waning or use flexible survival models. The implementation of our data-simulation methods is identical across all possible survival models and can easily be automated from standard probabilistic decision analyses.

HIGHLIGHTS

Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty through a given data collection exercise, such as a randomized clinical trial. In this article, we address the problem of computing EVSI when we assume treatment effect waning or use flexible survival models, by developing general-purpose methods that standardize and reduce the computational burden of the EVSI data-generation step for survival data.We developed 2 methods for simulating survival data from a probabilistic sample of survival probabilities over discrete time units, a discrete sampling method and an interpolated inverse transform sampling method, which can be combined with a recently proposed nonparametric EVSI method to accurately estimate EVSI for collecting survival data.Our general-purpose data-simulation methods greatly reduce the computational burden of the EVSI data-simulation step when we assume treatment effect waning or use flexible survival models. The implementation of our data-simulation methods is identical across all possible survival models and can therefore easily be automated from standard probabilistic decision analyses.

Phosphodiesterase 5 Inhibitor for Pediatric Pulmonary Arterial Hypertension: A Cost-Utility Analysis

OBJECTIVES

Despite the growing evidence of efficacy, scarce information exists regarding the cost of tadalafil to improve the functional classes of pediatric patients with pulmonary arterial hypertension. This study aims to determine the cost-utility of tadalafil compared sildenafil to treat pediatric patients with pulmonary arterial hypertension in Colombia.

METHODS

A Markov model was developed to compare expected costs, outcomes, and quality-adjusted life-years of sildenafil and tadalafil in pediatric patients with pulmonary arterial hypertension. The model was analyzed probabilistically, and a value of information analysis was conducted to inform the value of conducting further research to reduce current uncertainties in the evidence base. Cost-effectiveness was evaluated at a willingness-to-pay value of US $5180.

RESULTS

The mean incremental cost of tadalafil versus sildenafil is US $15 270. The 95% credible interval for the incremental cost ranges from US $28 033.65 to US $5940.86. The mean incremental benefit of tadalafil versus sildenafil is 1.00 quality-adjusted life-years (QALY). The 95% credible interval for the incremental benefit ranges from 1.88 to 0.31 QALY. The expected incremental cost per QALY is estimated at US $15 286. There is a probability less than 1% that tadalafil is more cost-effective than sildenafil at a threshold of US $5180 per QALY. Form the value of information analysis, the theoretical upper bound on the value of further research was US $9.298 for Colombia.

CONCLUSION

Our economic evaluation shows that tadalafil is not cost-effective regarding sildenafil to treat pediatric patients with pulmonary arterial hypertension in Colombia. Our study provides evidence that should be used by decision-makers to improve clinical practice guidelines.

Cost-Effectiveness of Venetoclax Plus Obinutuzumab Versus Chlorambucil Plus Obinutuzumab for the First-Line Treatment of Adult Patients With Chronic Lymphocytic Leukemia: An Extended Societal View

OBJECTIVES

Efficacy of venetoclax plus obinutuzumab (VenO) compared with chlorambucil plus obinutuzumab (ClbO) for treatment-naïve adult patients with chronic lymphocytic leukemia (CLL) with coexisting medical conditions was investigated in CLL14 (NCT02242942). Our aim was to evaluate the cost-effectiveness of VenO versus ClbO for these patients from a Dutch societal perspective.

METHODS

A 3-state partitioned survival model was constructed to evaluate the cost-effectiveness of VenO. The outcome of the analysis was the incremental cost-effectiveness ratio (ICER) with effectiveness measured in quality-adjusted life-years (QALYs) gained. Uncertainty was explored through deterministic and probabilistic sensitivity analyses, scenario analyses, and value of information analysis (VOI).

RESULTS

The base case resulted in a discounted ICER -49 928 EUR/QALY gained (with incremental negative costs and positive effects). None of the ICERs resulted from deterministic sensitivity and scenario analyses exceeded the chosen willingness-to-pay threshold of 20 000 EUR/QALY, and > 99% of the iterations in the probabilistic sensitivity analysis were cost-effective. VOI analyses showed a maximum expected value of eliminating all model parameter uncertainty of 183 591 EUR.

CONCLUSIONS

Our study demonstrated VenO being dominant over ClbO in treatment-naïve adult patients with CLL assuming a Dutch societal perspective. We concluded that our results are robust as tested through sensitivity and scenario analyses. Additionally, the VOI analyses confirmed that our current evidence base is strong enough to generate reliable results for our study. Nevertheless, further research based on real-world data or longer follow-up period could further contribute to the robustness of the current study's conclusions.

Optimal Epidemic Control under Uncertainty: Tradeoffs between Information Collection and Other Actions

BACKGROUND

Recent epidemics and measures taken to control them-through vaccination or other actions-have highlighted the role and importance of uncertainty in public health. There is generally a tradeoff between information collection and other uses of resources. Whether this tradeoff is solved explicitly or implicitly, the concept of value of information is central to inform policy makers in an uncertain environment.

METHOD

We use a deterministic SIR (susceptible, infectious, recovered) disease emergence and transmission model with vaccination that can be administered as 1 or 2 doses. The disease parameters and vaccine characteristics are uncertain. We study the tradeoffs between information acquisition and 2 other measures: bringing vaccination forward and acquiring more vaccine doses. To do this, we quantify the expected value of perfect information (EVPI) under different constraints faced by public health authorities (i.e., the time of the vaccination campaign implementation and the number of vaccine doses available).

RESULTS

We discuss the appropriateness of different responses under uncertainty. We show that, in some cases, vaccinating later or with less vaccine doses but more information about the epidemic, and the efficacy of control strategies may bring better results than vaccinating earlier or with more doses and less information, respectively.

CONCLUSION

In the present methodological article, we show in an abstract setting how clearly defining and treating the tradeoff between information acquisition and the relaxation of constraints can improve public health decision making.

HIGHLIGHTS

Uncertainties can seriously hinder epidemic control, but resolving them is costly. Thus, there are tradeoffs between information collection and alternative uses of resources.We use a generic SIR model with vaccination and a value-of-information framework to explore these tradeoffs.We show in which cases vaccinating later with more information about the epidemic and the efficacy of control measures may be better-or not-than vaccinating earlier with less information.We show in which cases vaccinating with fewer vaccine doses and more information about the epidemic and the efficacy of control measures may be better-or not-than vaccinating with more doses and less information.

Cost-Utility of Continuous Positive Airway Pressure for Respiratory Distress in Preterm Infants in a Middle-Income Country

OBJECTIVES

Despite the increased popularity of continuous positive airway pressure (CPAP) for preterm infants with respiratory distress, there is still uncertainty about whether the additional costs of this device justify the clinical benefits provided. This study aims to evaluate the cost-utility of CPAP in spontaneously breathing preterm infants with respiratory distress.

METHODS

Using a decision tree model, we estimated the cost and quality-adjusted life-years (QALYs) associated with CPAP and supplemental oxygen alone by headbox or low-flow nasal cannula (SO). The model was analyzed probabilistically, and a value of information analysis was conducted to inform the value of conducting further research to reduce current uncertainties in the evidence base. Cost-effectiveness was evaluated at a willingness-to-pay value of US$5180.

RESULTS

The mean incremental cost of CPAP versus SO was US$600. The mean incremental benefit of CPAP versus SO was 0.04 QALY. The expected incremental cost per QALY was estimated at US$13 172. The mean incremental net monetary benefit was US$-324 with a 95% credible interval of US$-536 to US$-201. The overall expected value of perfect information per person affected by the decision was estimated to be US$2346.

CONCLUSIONS

Compared with SO, the use of CPAP in spontaneously breathing preterm infants with respiratory distress is not cost-effective in Colombia. Evidence should continue to be generated with real-life effectiveness data and economic evaluations in other countries to confirm our findings.

Cost-Effectiveness and Budget Impact of Future Developments With Whole-Genome Sequencing for Patients With Lung Cancer

OBJECTIVES

This study aimed to investigate the cost-effectiveness, budget impact (BI), and impact of uncertainty of future developments concerning whole-genome sequencing (WGS) as a clinical diagnostic test compared with standard of care (SoC) in patients with locally advanced and metastatic non-small cell lung cancer.

METHODS

A total of 3 likely scenarios to take place within 5 years (according to experts) were simulated using a previously developed, peer reviewed, and published decision model. The scenarios concerned "WGS results used for treatment selection" (scenario 1), "WGS-based biomarker for immunotherapy" (scenario 2), and "off-label drug approval for WGS results" (scenario 3). Two diagnostic strategies of the original model, "SoC" and "WGS as a diagnostic test" (base model), were used to compare our scenarios with. Outcomes were reported for the base model, all scenarios separately, combined (combined unweighted), and weighted by likelihood (combined weighted). Cost-effectiveness, BI, and value of information analyses were performed for WGS compared with SoC.

RESULTS

Total costs and quality-adjusted life-years for SoC in metastatic non-small cell lung cancer were €149 698 and 1.235. Incremental outcomes of WGS were €1529/0.002(base model), -€222/0.020(scenario 1), -€2576/0.023(scenario 2), €388/0.024(scenario 3), -€5041/0.060(combined unweighted), and -€1715/0.029(combined weighted). The annual BI for adopting WGS for this population in The Netherlands ranged between €682 million (combined unweighted) and €714 million (base model). The consequences of uncertainty amounted to €3.4 million for all scenarios (combined weighted) and to €699 000 for the diagnostic yield of WGS alone (combined weighted).

CONCLUSIONS

Our findings suggest that it is likely for WGS to become cost-effective within the near future if it identifies more patients with actionable targets and show the impact of uncertainty regarding its diagnostic yield. Modeling future scenarios can be useful to consider early adoption of WGS while timely anticipating on unforeseen developments before final conclusions are reached.

Universal ophthalmia neonatorum prophylaxis in Ontario: a cost-effectiveness analysis

BACKGROUND

Although prophylaxis for ophthalmia neonatorum at birth is required by law in Ontario, declining prevalence of disease and efficacy of prophylaxis have called this practice into question. The objective of this modelling study was to assess the cost-effectiveness of universal prophylaxis for ophthalmia neonatorum to inform decision-makers on the potential impact of a change in this policy.

METHODS

We compared the cost-effectiveness of prophylaxis for ophthalmia neonatorum with no prophylaxis through cost-utility analysis with a lifetime time horizon, considering a provincial government payer, for a hypothetical population of newborns in Ontario. We assessed both the mean incremental costs of prophylaxis and its mean incremental effectiveness using a hybrid (part decision tree, part Markov) model. We used a scenario analysis to evaluate alternative time horizons and discount rates. We conducted a threshold analysis to evaluate the impact of variations in the cost of prophylaxis and in the prevalence of sexually transmitted infections (gonorrhea and chlamydia).

RESULTS

In our model, prophylaxis for ophthalmia neonatorum did not meet a willingness-to-pay threshold of Can$50 000 per quality-adjusted life-year (QALY). Although prophylaxis was effective in reducing morbidity associated with ophthalmia neonatorum, the number needed to treat to prevent 1 case of ophthalmia neonatorum blindness was 500 000, with an associated cost of more than Can$4 000 000. When compared with no prophylaxis, prophylaxis had an incremental cost of Can$355 798 per long-term QALY gained (incremental cost-effectiveness ratio).

INTERPRETATION

We found that prophylaxis for ophthalmia neonatorum, although individually inexpensive, leads to very high costs on a population level. These findings contribute to the discussion on mandatory prophylaxis currently underway in several jurisdictions.

Optimal Cost-Effective Screening Strategy for Unruptured Intracranial Aneurysms in Female Smokers

BACKGROUND

The prevalence of intracranial aneurysms among female cigarette smokers was shown to be high in previous studies, yet the cost-effectiveness of screening them has never been explored.

OBJECTIVE

To explore the most cost-effective screening strategy for female smokers.

METHODS

A decision analytical study was performed with a Markov model to compare different screening strategies with no screening and to explore the most optimal screening strategy for female smokers. Input data for the model were extracted from literature. A single screening at different ages and multiple screening every 15 years, 10 years, 5 years, and 2 years were performed for female smokers in different age ranges. Deterministic and probabilistic sensitivity analyses were performed to evaluate the robustness of the model. Finally, value of information analysis was performed to investigate the value of collecting additional data.

RESULTS

Screening female smokers for unruptured intracranial aneurysm is cost-effective. All screening strategies yield extra quality-adjusted life years. Screening at younger age brings more health benefit at lower cost. Frequent screening strategies decrease rupture rate of aneurysms more with higher costs per quality-adjusted life year. Screening after age 70 years and frequent screening (every 2 years) after age 60 years is not optimal. Among all the parameters in the model, collecting additional data on utility of the unscreened population would be most valuable.

CONCLUSION

Screening female smokers for intracranial aneurysms once at younger age is most optimal. However, in clinical practice, the duration and intensity of exposure to cigarettes should be taken into consideration.

Extensions of Health Economic Evaluations in R for Microsoft Excel Users: A Tutorial for Incorporating Heterogeneity and Conducting Value of Information Analyses

Advanced health economic analysis techniques currently performed in Microsoft Excel, such as incorporating heterogeneity, time-dependent transitions and a value of information analysis, can be easily transferred to R. Often the outputs of survival analyses (such as Weibull regression models) will estimate the impacts of correlated patient characteristics on patient outcomes, and are utilised directly as inputs for health economic decision models. This tutorial provides a step-by-step guide of how to conduct such analyses with a Markov model developed in R, and offers a comparison with established analyses performed in Microsoft Excel. This is done through the conversion of a previously published Microsoft Excel case study of a hip replacement surgery cost-effectiveness model. We hope that this paper can act as a facilitator in switching decision models from Microsoft Excel to R for complex health economic analyses, providing open-access code and data, suitable for future adaptation.

Cost-effectiveness of proton radiotherapy versus photon radiotherapy for non-small cell lung cancer patients: Exploring the model-based approach

INTRODUCTION

Proton radiotherapy (PT) is a promising but more expensive strategy than photon radiotherapy (XRT) for the treatment of non-small cell lung cancer (NSCLC). PT is probably not cost-effective for all patients. Therefore, patients can be selected using normal tissue complication probability (NTCP) models with predefined criteria. This study aimed to explore the cost-effectiveness of three treatment strategies for patients with stage III NSCLC: 1. photon radiotherapy for all patients (XRT_All); 2. PT for all patients (PT_All); 3. PT for selected patients (PT_{Individualized}).

METHODS

A decision-analytical model was constructed to estimate and compare costs and QALYs of all strategies. Three radiation-related toxicities were included: dyspnea, dysphagia and cardiotoxicity. Costs and QALY's were incorporated for grade 2 and ≥ 3 toxicities separately. Incremental Cost-Effectiven Ratios (ICERs) were calculated and compared to a threshold value of €80,000. Additionally, scenario, sensitivity and value of information analyses were performed.

RESULTS

PT_All yielded most QALYs, but was also most expensive. XRT_All was the least effective and least expensive strategy, and the most cost-effective strategy. For thresholds higher than €163,467 per QALY gained, PT_{Individualized} was cost-effective. When assuming equal minutes per fraction (15 minutes) for PT and XRT, PT_{Individualized} was considered the most cost-effective strategy (ICER: €76,299).

CONCLUSION

Currently, PT is not cost-effective for all patients, nor for patient selected on the current NTCP models used in the Dutch indication protocol. However, with improved clinical experience, personnel and treatment costs of PT can decrease over time, which potentially leads to PT_{Individualized}, with optimal patient selection, will becoming a cost-effective strategy.

Cost-effectiveness of adalimumab for early-stage Dupuytren’s disease

Aims

To estimate the potential cost-effectiveness of adalimumab compared with standard care alone for the treatment of early-stage Dupuytren’s disease (DD) and the value of further research from an NHS perspective.

Methods

We used data from the Repurposing anti-TNF for Dupuytren’s disease (RIDD) randomized controlled trial of intranodular adalimumab injections in patients with early-stage progressive DD. RIDD found that intranodular adalimumab injections reduced nodule hardness and size in patients with early-stage DD, indicating the potential to control disease progression. A within-trial cost-utility analysis compared four adalimumab injections with no further treatment against standard care alone, taking a 12-month time horizon and using prospective data on EuroQol five-dimension five-level questionnaire (EQ-5D-5L) and resource use from the RIDD trial. We also developed a patient-level simulation model similar to a Markov model to extrapolate trial outcomes over a lifetime using data from the RIDD trial and a literature review. This also evaluated repeated courses of adalimumab each time the nodule reactivated (every three years) in patients who initially responded.

Results

The within-trial economic evaluation found that adalimumab plus standard care cost £503,410 per quality-adjusted life year (QALY) gained versus standard care alone over a 12-month time horizon. The model-based extrapolation suggested that, over a lifetime, repeated courses of adalimumab could cost £14,593 (95% confidence interval £7,534 to £42,698) per QALY gained versus standard care alone. If the NHS was willing to pay £20,000/QALY gained, there is a 77% probability that adalimumab with retreatment is the best value for money.

Conclusion

Repeated courses of adalimumab are likely to be a cost-effective treatment for progressive early-stage DD. The value of perfect parameter information that would eliminate all uncertainty around the parameters estimated in RIDD and the duration of quiescence was estimated to be £105 per patient or £272 million for all 2,584,411 prevalent cases in the UK. Cite this article: Bone Jt Open 2022;3(11):898–906.

Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds

Models of electrical excitation and recovery in the heart have become increasingly detailed, but have yet to be used routinely in the clinical setting to guide personalized intervention in patients. One of the main challenges is calibrating models from the limited measurements that can be made in a patient during a standard clinical procedure. In this work, we propose a novel framework for the probabilistic calibration of electrophysiology parameters on the left atrium of the heart using local measurements of cardiac excitability. Parameter fields are represented as Gaussian processes on manifolds and are linked to measurements via surrogate functions that map from local parameter values to measurements. The posterior distribution of parameter fields is then obtained. We show that our method can recover parameter fields used to generate localised synthetic measurements of effective refractory period. Our methodology is applicable to other measurement types collected with clinical protocols, and more generally for calibration where model parameters vary over a manifold.

Metamodeling for Policy Simulations with Multivariate Outcomes

PURPOSE

Metamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using metamodels for policy analysis arise when there are multiple correlated outputs of interest. We develop a framework for metamodeling with policy simulations to accommodate multivariate outcomes.

METHODS

We combine 2 algorithm adaptation methods-multitarget stacking and regression chain with maximum correlation-with different base learners including linear regression (LR), elastic net (EE) with second-order terms, Gaussian process regression (GPR), random forests (RFs), and neural networks. We optimize integrated models using variable selection and hyperparameter tuning. We compare the accuracy, efficiency, and interpretability of different approaches. As an example application, we develop metamodels to emulate a microsimulation model of testing and treatment strategies for hepatitis C in correctional settings.

RESULTS

Output variables from the simulation model were correlated (average ρ = 0.58). Without multioutput algorithm adaptation methods, in-sample fit (measured by R2) ranged from 0.881 for LR to 0.987 for GPR. The multioutput algorithm adaptation method increased R2 by an average 0.002 across base learners. Variable selection and hyperparameter tuning increased R2 by 0.009. Simpler models such as LR, EE, and RF required minimal training and prediction time. LR and EE had advantages in model interpretability, and we considered methods for improving the interpretability of other models.

CONCLUSIONS

In our example application, the choice of base learner had the largest impact on R2; multioutput algorithm adaptation and variable selection and hyperparameter tuning had a modest impact. Although advantages and disadvantages of specific learning algorithms may vary across different modeling applications, our framework for metamodeling in policy analyses with multivariate outcomes has broad applicability to decision analysis in health and medicine.

Defining the optimum strategy for identifying adults and children with coeliac disease: systematic review and economic modelling

BACKGROUND

Coeliac disease is an autoimmune disorder triggered by ingesting gluten. It affects approximately 1% of the UK population, but only one in three people is thought to have a diagnosis. Untreated coeliac disease may lead to malnutrition, anaemia, osteoporosis and lymphoma.

OBJECTIVES

The objectives were to define at-risk groups and determine the cost-effectiveness of active case-finding strategies in primary care.

DESIGN

(1) Systematic review of the accuracy of potential diagnostic indicators for coeliac disease. (2) Routine data analysis to develop prediction models for identification of people who may benefit from testing for coeliac disease. (3) Systematic review of the accuracy of diagnostic tests for coeliac disease. (4) Systematic review of the accuracy of genetic tests for coeliac disease (literature search conducted in April 2021). (5) Online survey to identify diagnostic thresholds for testing, starting treatment and referral for biopsy. (6) Economic modelling to identify the cost-effectiveness of different active case-finding strategies, informed by the findings from previous objectives.

DATA SOURCES

For the first systematic review, the following databases were searched from 1997 to April 2021: MEDLINE® (National Library of Medicine, Bethesda, MD, USA), Embase® (Elsevier, Amsterdam, the Netherlands), Cochrane Library, Web of Science™ (Clarivate™, Philadelphia, PA, USA), the World Health Organization International Clinical Trials Registry Platform ( WHO ICTRP ) and the National Institutes of Health Clinical Trials database. For the second systematic review, the following databases were searched from January 1990 to August 2020: MEDLINE, Embase, Cochrane Library, Web of Science, Kleijnen Systematic Reviews ( KSR ) Evidence, WHO ICTRP and the National Institutes of Health Clinical Trials database. For prediction model development, Clinical Practice Research Datalink GOLD, Clinical Practice Research Datalink Aurum and a subcohort of the Avon Longitudinal Study of Parents and Children were used; for estimates for the economic models, Clinical Practice Research Datalink Aurum was used.

REVIEW METHODS

For review 1, cohort and case-control studies reporting on a diagnostic indicator in a population with and a population without coeliac disease were eligible. For review 2, diagnostic cohort studies including patients presenting with coeliac disease symptoms who were tested with serological tests for coeliac disease and underwent a duodenal biopsy as reference standard were eligible. In both reviews, risk of bias was assessed using the quality assessment of diagnostic accuracy studies 2 tool. Bivariate random-effects meta-analyses were fitted, in which binomial likelihoods for the numbers of true positives and true negatives were assumed.

RESULTS

People with dermatitis herpetiformis, a family history of coeliac disease, migraine, anaemia, type 1 diabetes, osteoporosis or chronic liver disease are 1.5-2 times more likely than the general population to have coeliac disease; individual gastrointestinal symptoms were not useful for identifying coeliac disease. For children, women and men, prediction models included 24, 24 and 21 indicators of coeliac disease, respectively. The models showed good discrimination between patients with and patients without coeliac disease, but performed less well when externally validated. Serological tests were found to have good diagnostic accuracy for coeliac disease. Immunoglobulin A tissue transglutaminase had the highest sensitivity and endomysial antibody the highest specificity. There was little improvement when tests were used in combination. Survey respondents (n = 472) wanted to be 66% certain of the diagnosis from a blood test before starting a gluten-free diet if symptomatic, and 90% certain if asymptomatic. Cost-effectiveness analyses found that, among adults, and using serological testing alone, immunoglobulin A tissue transglutaminase was most cost-effective at a 1% pre-test probability (equivalent to population screening). Strategies using immunoglobulin A endomysial antibody plus human leucocyte antigen or human leucocyte antigen plus immunoglobulin A tissue transglutaminase with any pre-test probability had similar cost-effectiveness results, which were also similar to the cost-effectiveness results of immunoglobulin A tissue transglutaminase at a 1% pre-test probability. The most practical alternative for implementation within the NHS is likely to be a combination of human leucocyte antigen and immunoglobulin A tissue transglutaminase testing among those with a pre-test probability above 1.5%. Among children, the most cost-effective strategy was a 10% pre-test probability with human leucocyte antigen plus immunoglobulin A tissue transglutaminase, but there was uncertainty around the most cost-effective pre-test probability. There was substantial uncertainty in economic model results, which means that there would be great value in conducting further research.

LIMITATIONS

The interpretation of meta-analyses was limited by the substantial heterogeneity between the included studies, and most included studies were judged to be at high risk of bias. The main limitations of the prediction models were that we were restricted to diagnostic indicators that were recorded by general practitioners and that, because coeliac disease is underdiagnosed, it is also under-reported in health-care data. The cost-effectiveness model is a simplification of coeliac disease and modelled an average cohort rather than individuals. Evidence was weak on the probability of routine coeliac disease diagnosis, the accuracy of serological and genetic tests and the utility of a gluten-free diet.

CONCLUSIONS

Population screening with immunoglobulin A tissue transglutaminase (1% pre-test probability) and of immunoglobulin A endomysial antibody followed by human leucocyte antigen testing or human leucocyte antigen testing followed by immunoglobulin A tissue transglutaminase with any pre-test probability appear to have similar cost-effectiveness results. As decisions to implement population screening cannot be made based on our economic analysis alone, and given the practical challenges of identifying patients with higher pre-test probabilities, we recommend that human leucocyte antigen combined with immunoglobulin A tissue transglutaminase testing should be considered for adults with at least a 1.5% pre-test probability of coeliac disease, equivalent to having at least one predictor. A more targeted strategy of 10% pre-test probability is recommended for children (e.g. children with anaemia).

FUTURE WORK

Future work should consider whether or not population-based screening for coeliac disease could meet the UK National Screening Committee criteria and whether or not it necessitates a long-term randomised controlled trial of screening strategies. Large prospective cohort studies in which all participants receive accurate tests for coeliac disease are needed.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42019115506 and CRD42020170766.

FUNDING

This project was funded by the National Institute for Health and Care Research ( NIHR ) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 44. See the NIHR Journals Library website for further project information.

Early economic modeling of magnetic resonance image-guided high intensity focused ultrasound compared to radiotherapy for pain palliation of bone metastases

Introduction

Magnetic Resonance Image-guided High Intensity Focused Ultrasound (MR-HIFU) is a non-invasive treatment option for palliative patients with painful bone metastases. Early evidence suggests that MR-HIFU is associated with similar overall treatment response, but more rapid pain palliation compared to external beam radiotherapy (EBRT). This modelling study aimed to assess the cost-effectiveness of MR-HIFU as an alternative treatment option for painful bone metastases from the perspective of the German Statutory Health Insurance (SHI).

Materials and methods

A microsimulation model with lifelong time horizon and one-month cycle length was developed. To calculate the incremental cost-effectiveness ratio (ICER), strategy A (MR-HIFU as first-line treatment or as retreatment option in case of persistent pain or only partial pain relief after EBRT) was compared to strategy B (EBRT alone) for patients with bone metastases due to breast, prostate, or lung cancer. Input parameters used for the model were extracted from the literature. Results were expressed as EUR per quality-adjusted life years (QALYs) and EUR per pain response (i.e., months spent with complete or partial pain response). Deterministic and probabilistic sensitivity analyses (PSA) were performed to test the robustness of results, and a value of information analysis was conducted.

Results

Compared to strategy B, strategy A resulted in additional costs (EUR 399) and benefits (0.02 QALYs and 0.95 months with pain response). In the base case, the resulting ICERs (strategy A vs. strategy B) are EUR 19,845/QALY and EUR 421 per pain response. Offering all patients MR-HIFU as first-line treatment would increase the ICER by 50% (31,048 EUR/QALY). PSA showed that at a (hypothetical) willingness to pay of EUR 20,000/QALY, the probability of MR-HIFU being cost-effective was 52%. The expected value of perfect information (EVPI) for the benefit population in Germany is approximately EUR 190 Mio.

Conclusion

Although there is considerable uncertainty, the results demonstrate that introducing MR-HIFU as a treatment alternative for painful bone metastases might be cost-effective for the German SHI. The high EVPI indicate that further studies to reduce uncertainty would be worthwhile.

Challenges around quantifying uncertainty in a holistic approach to hard-to-heal wound management: Health economic perspective

Treatment of hard-to-heal wounds involves a holistic approach for choosing between available treatment options. However, evidence for informing these choices is sparse, introducing uncertainty into decisions about the optimum treatment pathways that reflect the vast heterogeneity in this patient population. This paper discusses the existing clinical and health economic literature in order to provide insight into sources of uncertainty in the evaluation of the holistic approach to management of the hard-to-heal wounds, and how this uncertainty can be appropriately reflected in research. We identified three key sources of uncertainty in the evaluation of chronic wound treatments, namely heterogeneity in aetiology and patient populations, heterogeneity in treatment pathways, and challenges around capturing all relevant outcomes. Reflecting these complexities requires sophisticated modelling of treatment sequencing and long-term outcomes. The paper discusses how the scope specification, scenario analyses, and sensitivity analyses can be used to fully characterise analytical uncertainty.

Tisagenlecleucel for relapsed/refractory acute lymphoblastic leukemia in the Irish healthcare setting: cost-effectiveness and value of information analysis

OBJECTIVES

This study evaluates the cost-effectiveness of tisagenlecleucel (a CAR T-cell therapy), versus blinatumomab, for the treatment of pediatric and young adult patients with relapsed/refractory acute lymphoblastic leukemia (R/R ALL) in the Irish healthcare setting. The value of conducting further research, to investigate the value of uncertainty associated with the decision problem, is assessed by means of expected value of perfect information (EVPI) and partial EVPI (EVPPI) analyses.

METHODS

A three-state partitioned survival model was developed. A short-term decision tree partitioned patients in the tisagenlecleucel arm according to infusion status. Survival was extrapolated to 60 months; general population mortality with a standardized mortality ratio was then applied. Estimated EVPI and EVPPI were scaled up to population according to the incidence of the decision.

RESULTS

At list prices, the incremental cost-effectiveness ratio was EUR 73,086 per quality-adjusted life year (QALY) (incremental costs EUR 156,928; incremental QALYs 2.15). The probability of cost-effectiveness, at the willingness-to-pay threshold of EUR 45,000 per QALY, was 16 percent. At this threshold, population EVPI was EUR 314,455; population EVPPI was below EUR 100,000 for each parameter category.

CONCLUSIONS

Tisagenlecleucel is not cost effective, versus blinatumomab, for the treatment of pediatric and young adult patients with R/R ALL in Ireland (at list prices). Further research to decrease decision (parameter) uncertainty, at the defined willingness-to-pay threshold, may not be of value. However, there is a high degree of uncertainty underpinning the analysis, which may not be captured by EVPI analysis.

A Conceptual Framework for Life-Cycle Health Technology Assessment

OBJECTIVES

Health technology assessment (HTA) uses evidence appraisal and synthesis with economic evaluation to inform adoption decisions. Standard HTA processes sometimes struggle to (1) support decisions that involve significant uncertainty and (2) encourage continued generation of and adaptation to new evidence. We propose the life-cycle (LC)-HTA framework, addressing these challenges by providing additional tools to decision makers and improving outcomes for all stakeholders.

METHODS

Under the LC-HTA framework, HTA processes align to LC management. LC-HTA introduces changes in HTA methods to minimize analytic time while optimizing decision certainty. Where decision uncertainty exists, we recommend risk-based pricing and research-oriented managed access (ROMA). Contractual procurement agreements define the terms of reassessment and provide additional decision options to HTA agencies. LC-HTA extends value-of-information methods to inform ROMA agreements, leveraging routine, administrative data, and registries to reduce uncertainty.

RESULTS

LC-HTA enables the adoption of high-value high-risk innovations while improving health system sustainability through risk-sharing and reducing uncertainty. Responsiveness to evolving evidence is improved through contractually embedded decision rules to simplify reassessment. ROMA allows conditional adoption to obtain additional information, with confidence that the net value of that adoption decision is positive.

CONCLUSIONS

The LC-HTA framework improves outcomes for patients, sponsors, and payers. Patients benefit through earlier access to new technologies. Payers increase the value of the technologies they invest in and gain mechanisms to review investments. Sponsors benefit through greater certainty in outcomes related to their investment, swifter access to markets, and greater opportunities to demonstrate value.

An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial

BACKGROUND

Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we develop new methods for computing the EVSI of extending an existing trial's follow-up, first for an assumed survival model and then extending to capture uncertainty about the true survival model.

METHODS

We developed a nested Markov Chain Monte Carlo procedure and a nonparametric regression-based method. We compared the methods by computing single-model and model-averaged EVSI for collecting additional follow-up data in 2 synthetic case studies.

RESULTS

There was good agreement between the 2 methods. The regression-based method was fast and straightforward to implement, and scales easily to include any number of candidate survival models in the model uncertainty case. The nested Monte Carlo procedure, on the other hand, was extremely computationally demanding when we included model uncertainty.

CONCLUSIONS

We present a straightforward regression-based method for computing the EVSI of extending an existing trial's follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. EVSI for ongoing trials can help decision makers determine whether early patient access to a new technology can be justified on the basis of the current evidence or whether more mature evidence is needed.

HIGHLIGHTS

Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life-expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial.In this article, we have developed new methods for computing the EVSI of extending a trial's follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. We extend a previously described nonparametric regression-based method for computing EVSI, which we demonstrate in synthetic case studies is fast, straightforward to implement, and scales easily to include any number of candidate survival models in the EVSI calculations.The EVSI methods that we present in this article can quantify the need for collecting additional follow-up data before making an adoption decision given any decision-making context.

Personalization of Medical Treatment Decisions: Simplifying Complex Models while Maintaining Patient Health Outcomes

BACKGROUND

Personalizing medical treatments based on patient-specific risks and preferences can improve patient health. However, models to support personalized treatment decisions are often complex and difficult to interpret, limiting their clinical application.

METHODS

We present a new method, using machine learning to create meta-models, for simplifying complex models for personalizing medical treatment decisions. We consider simple interpretable models, interpretable ensemble models, and noninterpretable ensemble models. We use variable selection with a penalty for patient-specific risks and/or preferences that are difficult, risky, or costly to obtain. We interpret the meta-models to the extent permitted by their model architectures. We illustrate our method by applying it to simplify a previously developed model for personalized selection of antipsychotic drugs for patients with schizophrenia.

RESULTS

The best simplified interpretable, interpretable ensemble, and noninterpretable ensemble models contained at most half the number of patient-specific risks and preferences compared with the original model. The simplified models achieved 60.5% (95% credible interval [crI]: 55.2-65.4), 60.8% (95% crI: 55.5-65.7), and 83.8% (95% crI: 80.8-86.6), respectively, of the net health benefit of the original model (quality-adjusted life-years gained). Important variables in all models were similar and made intuitive sense. Computation time for the meta-models was orders of magnitude less than for the original model.

LIMITATIONS

The simplified models share the limitations of the original model (e.g., potential biases).

CONCLUSIONS

Our meta-modeling method is disease- and model- agnostic and can be used to simplify complex models for personalization, allowing for variable selection in addition to improved model interpretability and computational performance. Simplified models may be more likely to be adopted in clinical settings and can help improve equity in patient outcomes.

Cost Effectiveness of Digital Interventions for Generalised Anxiety Disorder: A Model-Based Analysis

BACKGROUND

Digital interventions (DIs) are increasingly being used in mental health care, despite limited evidence regarding their value for money. This study aimed to evaluate the cost effectiveness of DIs for generalised anxiety disorder (GAD), in comparison with alternative care options, from the perspective of the UK health care system.

METHODS

An open-source decision analytic cohort model was used to extrapolate the results of a network meta-analysis over a patient's lifetime and estimate the costs and outcomes (quality-adjusted life-years) of DIs and their comparators. The net monetary benefit (NMB) and probability of cost effectiveness was estimated for each comparator, and we conducted a Value of Information analysis to evaluate the scale and drivers of uncertainty.

RESULTS

DIs were associated with lower NMB compared with medication and with group therapy, but greater NMB compared with non-therapeutic controls and with usual care. DIs that were supported by a clinician, an assistant or a lay person had higher delivery costs than purely patient-self-directed DIs, yielding a greater NMB when opportunity cost was above £3000/QALY. There was considerable uncertainty in the findings driven largely by uncertainty in the estimated treatment effects. The value of further research to establish the effectiveness of DIs for GAD was substantial, at least £12.9 billion.

CONCLUSIONS

The high uncertainty about these results does not allow for recommendations based on the cost effectiveness of DIs. However, the analysis highlights areas for future research, and demonstrates that apparent cost savings associated with DIs can be offset by reduced effectiveness.

Uptake Rates of Risk-Reducing Surgeries for Women at Increased Risk of Hereditary Breast and Ovarian Cancer Applied to Cost-Effectiveness Analyses: A Scoping Systematic Review

The cost-effectiveness of genetic screen-and-treat strategies for women at increased risk for breast and ovarian cancer often depends on the women's willingness to make use of risk-reducing mastectomy (RRM) or salpingo-oophorectomy (RRSO). To explore the uptake rates of RRM and RRSO applied in health economic modeling studies and the impact of uptake rates on the incremental cost-effectiveness ratios (ICER), we conducted a scoping literature review. In addition, using our own model, we conducted a value of information (VOI) analysis. Among the 19 models included in the review, the uptake rates of RRM ranged from 6% to 47% (RRSO: 10% to 88%). Fifty-seven percent of the models applied retrospective data obtained from registries, hospital records, or questionnaires. According to the models' deterministic sensitivity analyses, there is a clear trend that a lower uptake rate increased the ICER and vice versa. Our VOI analysis showed high decision uncertainty associated with the uptake rates. In the future, uptake rates should be given more attention in the conceptualization of health economic modeling studies. Prospective studies are recommended to reflect regional and national variations in women's preferences for preventive surgery.

Value of Information Analysis in Models to Inform Health Policy

Value of information (VoI) is a decision-theoretic approach to estimating the expected benefits from collecting further information of different kinds, in scientific problems based on combining one or more sources of data. VoI methods can assess the sensitivity of models to different sources of uncertainty and help to set priorities for further data collection. They have been widely applied in healthcare policy making, but the ideas are general to a range of evidence synthesis and decision problems. This article gives a broad overview of VoI methods, explaining the principles behind them, the range of problems that can be tackled with them, and how they can be implemented, and discusses the ongoing challenges in the area.

Calculating Expected Value of Sample Information Adjusting for Imperfect Implementation

BACKGROUND

The expected value of sample information (EVSI) calculates the value of collecting additional information through a research study with a given design. However, standard EVSI analyses do not account for the slow and often incomplete implementation of the treatment recommendations that follow research. Thus, standard EVSI analyses do not correctly capture the value of the study. Previous research has developed measures to calculate the research value while adjusting for implementation challenges, but estimating these measures is a challenge.

METHODS

Based on a method that assumes the implementation level is related to the strength of evidence in favor of the treatment, 2 implementation-adjusted EVSI calculation methods are developed. These novel methods circumvent the need for analytical calculations, which were restricted to settings in which normality could be assumed. The first method developed in this article uses computationally demanding nested simulations, based on the definition of the implementation-adjusted EVSI. The second method is based on adapting the moment matching method, a recently developed efficient EVSI computation method, to adjust for imperfect implementation. The implementation-adjusted EVSI is then calculated with the 2 methods across 3 examples.

RESULTS

The maximum difference between the 2 methods is at most 6% in all examples. The efficient computation method is between 6 and 60 times faster than the nested simulation method in this case study and could be used in practice.

CONCLUSIONS

This article permits the calculation of an implementation-adjusted EVSI using realistic assumptions. The efficient estimation method is accurate and can estimate the implementation-adjusted EVSI in practice. By adapting standard EVSI estimation methods, adjustments for imperfect implementation can be made with the same computational cost as a standard EVSI analysis.

HIGHLIGHTS

Standard expected value of sample information (EVSI) analyses do not account for the fact that treatment implementation following research is often slow and incomplete, meaning they incorrectly capture the value of the study.Two methods, based on nested Monte Carlo sampling and the moment matching EVSI calculation method, are developed to adjust EVSI calculations for imperfect implementation when the speed and level of the implementation of a new treatment depends on the strength of evidence in favor of the treatment.The 2 methods we develop provide similar estimates for the implementation-adjusted EVSI.Our methods extend current EVSI calculation algorithms and thus require limited additional computational complexity.

Optimal Surveillance Strategies for Early-Stage Cutaneous Melanoma Post Primary Tumor Excision: An Economic Evaluation

Background. Consensus on standardized active surveillance or follow-up care by clinicians is lacking leading to considerable variation in practice across countries. An important structural modelling consideration is that self-examination by patients and their partners can detect melanoma recurrence outside of active surveillance regimes. Objectives. To identify candidate melanoma surveillance strategies for American Joint Committee on Cancer (AJCC) stage I disease and compare them with the current recommended practice in a cost-utility analysis framework. Methods. In consultation with UK clinical experts, a microsimulation model was built in TreeAge Pro 2019 R1.0 (Williamstown, MA, USA) to evaluate surveillance strategies for AJCC stage IA and IB melanoma patients separately. The model incorporated patient behaviors such as self-detection and emergency visits to examine suspicious lesions. A National Health Service (NHS) perspective was taken. Model input parameters were taken from the literature and where data were not available, local expert opinion was sought. Probabilistic sensitivity analysis, one-way sensitivity analysis on pertinent parameters and value of information was performed. Results. In the base-case probabilistic sensitivity analysis, less intensive surveillance strategies for AJCC stage IA and IB had lower total lifetime costs than the current National Institute for Health and Care Excellence (NICE) recommended strategy with similar effectiveness in terms of quality-adjusted life years and thereby likely to be cost-effective. Many strategies had similar effectiveness due to the relatively low chance of recurrence and the high rate of self-detection. Sensitivity and scenario analyses did not change these findings. Conclusions. Our model findings suggest that less resource intensive surveillance may be cost-effective compared with the current NICE surveillance guidelines. However, to advocate convincingly for changes, better evidence is required.

Prioritizing Research in an Era of Personalized Medicine: The Potential Value of Unexplained Heterogeneity

Background

Clinical care is moving from a “one size fits all” approach to a setting in which treatment decisions are based on individual treatment response, needs, preferences, and risk. Research into personalized treatment strategies aims to discover currently unknown markers that identify individuals who would benefit from treatments that are nonoptimal at the population level. Before investing in research to identify these markers, it is important to assess whether such research has the potential to generate value. Thus, this article aims to develop a framework to prioritize research into the development of new personalized treatment strategies by creating a set of measures that assess the value of personalizing care based on a set of unknown patient characteristics.

Methods

Generalizing ideas from the value of heterogeneity framework, we demonstrate 3 measures that assess the value of developing personalized treatment strategies. The first measure identifies the potential value of personalizing medicine within a given disease area. The next 2 measures highlight specific research priorities and subgroup structures that would lead to improved patient outcomes from the personalization of treatment decisions.

Results

We graphically present the 3 measures to perform sensitivity analyses around the key drivers of value, in particular, the correlation between the individual treatment benefits across the available treatment options. We illustrate these 3 measures using a previously published decision model and discuss how they can direct research in personalized medicine.

Conclusion

We discuss 3 measures that form the basis of a novel framework to prioritize research into novel personalized treatment strategies. Our novel framework ensures that research targets personalized treatment strategies that have high potential to improve patient outcomes and health system efficiency.

Highlights

Modelling approaches for histology-independent cancer drugs to inform NICE appraisals: a systematic review and decision-framework

BACKGROUND

The first histology-independent marketing authorisation in Europe was granted in 2019. This was the first time that a cancer treatment was approved based on a common biomarker rather than the location in the body at which the tumour originated. This research aims to explore the implications for National Institute for Health and Care Excellence appraisals.

METHODS

Targeted reviews were undertaken to determine the type of evidence that is likely to be available at the point of marketing authorisation and the analyses required to support National Institute for Health and Care Excellence appraisals. Several challenges were identified concerning the design and conduct of trials for histology-independent products, the greater levels of heterogeneity within the licensed population and the use of surrogate end points. We identified approaches to address these challenges by reviewing key statistical literature that focuses on the design and analysis of histology-independent trials and by undertaking a systematic review to evaluate the use of response end points as surrogate outcomes for survival end points. We developed a decision framework to help to inform approval and research policies for histology-independent products. The framework explored the uncertainties and risks associated with different approval policies, including the role of further data collection, pricing schemes and stratified decision-making.

RESULTS

We found that the potential for heterogeneity in treatment effects, across tumour types or other characteristics, is likely to be a central issue for National Institute for Health and Care Excellence appraisals. Bayesian hierarchical methods may serve as a useful vehicle to assess the level of heterogeneity across tumours and to estimate the pooled treatment effects for each tumour, which can inform whether or not the assumption of homogeneity is reasonable. Our review suggests that response end points may not be reliable surrogates for survival end points. However, a surrogate-based modelling approach, which captures all relevant uncertainty, may be preferable to the use of immature survival data. Several additional sources of heterogeneity were identified as presenting potential challenges to National Institute for Health and Care Excellence appraisal, including the cost of testing, baseline risk, quality of life and routine management costs. We concluded that a range of alternative approaches will be required to address different sources of heterogeneity to support National Institute for Health and Care Excellence appraisals. An exemplar case study was developed to illustrate the nature of the assessments that may be required.

CONCLUSIONS

Adequately designed and analysed basket studies that assess the homogeneity of outcomes and allow borrowing of information across baskets, where appropriate, are recommended. Where there is evidence of heterogeneity in treatment effects and estimates of cost-effectiveness, consideration should be given to optimised recommendations. Routine presentation of the scale of the consequences of heterogeneity and decision uncertainty may provide an important additional approach to the assessments specified in the current National Institute for Health and Care Excellence methods guide.

FURTHER RESEARCH

Further exploration of Bayesian hierarchical methods could help to inform decision-makers on whether or not there is sufficient evidence of homogeneity to support pooled analyses. Further research is also required to determine the appropriate basis for apportioning genomic testing costs where there are multiple targets and to address the challenges of uncontrolled Phase II studies, including the role and use of surrogate end points.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Evidence Synthesis programme and will be published in full in Health Technology Assessment; Vol. 25, No. 76. See the NIHR Journals Library website for further project information.

Digital interventions in mental health: evidence syntheses and economic modelling

BACKGROUND

Economic evaluations provide evidence on whether or not digital interventions offer value for money, based on their costs and outcomes relative to the costs and outcomes of alternatives.

OBJECTIVES

(1) Evaluate and summarise published economic studies about digital interventions across different technologies, therapies, comparators and mental health conditions; (2) synthesise clinical evidence about digital interventions for an exemplar mental health condition; (3) construct an economic model for the same exemplar mental health condition using the previously synthesised clinical evidence; and (4) consult with stakeholders about how they understand and assess the value of digital interventions.

METHODS

We completed four work packages: (1) a systematic review and quality assessment of economic studies about digital interventions; (2) a systematic review and network meta-analysis of randomised controlled trials on digital interventions for generalised anxiety disorder; (3) an economic model and value-of-information analysis on digital interventions for generalised anxiety disorder; and (4) a series of knowledge exchange face-to-face and digital seminars with stakeholders.

RESULTS

In work package 1, we reviewed 76 economic evaluations: 11 economic models and 65 within-trial analyses. Although the results of the studies are not directly comparable because they used different methods, the overall picture suggests that digital interventions are likely to be cost-effective, compared with no intervention and non-therapeutic controls, whereas the value of digital interventions compared with face-to-face therapy or printed manuals is unclear. In work package 2, we carried out two network meta-analyses of 20 randomised controlled trials of digital interventions for generalised anxiety disorder with a total of 2350 participants. The results were used to inform our economic model, but when considered on their own they were inconclusive because of the very wide confidence intervals. In work package 3, our decision-analytic model found that digital interventions for generalised anxiety disorder were associated with lower net monetary benefit than medication and face-to-face therapy, but greater net monetary benefit than non-therapeutic controls and no intervention. Value for money was driven by clinical outcomes rather than by intervention costs, and a value-of-information analysis suggested that uncertainty in the treatment effect had the greatest value (£12.9B). In work package 4, stakeholders identified several areas of benefits and costs of digital interventions that are important to them, including safety, sustainability and reducing waiting times. Four factors may influence their decisions to use digital interventions, other than costs and outcomes: increasing patient choice, reaching underserved populations, enabling continuous care and accepting the 'inevitability of going digital'.

LIMITATIONS

There was substantial uncertainty around effect estimates of digital interventions compared with alternatives. This uncertainty was driven by the small number of studies informing most comparisons, the small samples in some of these studies and the studies' high risk of bias.

CONCLUSIONS

Digital interventions may offer good value for money as an alternative to 'doing nothing' or 'doing something non-therapeutic' (e.g. monitoring or having a general discussion), but their added value compared with medication, face-to-face therapy and printed manuals is uncertain. Clinical outcomes rather than intervention costs drive 'value for money'.

FUTURE WORK

There is a need to develop digital interventions that are more effective, rather than just cheaper, than their alternatives.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42018105837.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 1. See the NIHR Journals Library website for further project information.