Assessing generalisability by location in trial-based cost-effectiveness analysis: the use of multilevel models

A cost-effectiveness analysis of early detection and bundled treatment of postpartum hemorrhage alongside the E-MOTIVE trial

Timely detection and treatment of postpartum hemorrhage (PPH) are crucial to prevent complications or death. A calibrated blood-collection drape can help provide objective, accurate and early diagnosis of PPH, and a treatment bundle can address delays or inconsistencies in the use of effective interventions. Here we conducted an economic evaluation alongside the E-MOTIVE trial, an international, parallel cluster-randomized trial with a baseline control phase involving 210,132 women undergoing vaginal delivery across 78 secondary-level hospitals in Kenya, Nigeria, South Africa and Tanzania. We aimed to assess the cost-effectiveness of the E-MOTIVE intervention, which included a calibrated blood-collection drape for early detection of PPH and a bundle of first-response treatments (uterine massage, oxytocic drugs, tranexamic acid, intravenous fluids, examination and escalation), compared with usual care. We used multilevel modeling to estimate incremental cost-effectiveness ratios from the perspective of the public healthcare system for outcomes of cost per severe PPH (blood loss ≥1,000 ml) avoided and cost per disability-adjusted life-year averted. Our findings suggest that the use of a calibrated blood-collection drape for early detection of PPH and bundled first-response treatment is cost-effective and should be perceived by decision-makers as a worthwhile use of healthcare budgets. ClinicalTrials.gov identifier: NCT04341662 .

A review of heath economic evaluation practice in the Netherlands: are we moving forward?

Economic evaluations have been increasingly conducted in different countries to aid national decision-making bodies in resource allocation problems based on current and prospective evidence on costs and effects data for a set of competing health care interventions. In 2016, the Dutch National Health Care Institute issued new guidelines that aggregated and updated previous recommendations on key elements for conducting economic evaluation. However, the impact on standard practice after the introduction of the guidelines in terms of design, methodology and reporting choices, is still uncertain. To assess this impact, we examine and compare key analysis components of economic evaluations conducted in the Netherlands before (2010-2015) and after (2016-2020) the introduction of the recent guidelines. We specifically focus on two aspects of the analysis that are crucial in determining the plausibility of the results: statistical methodology and missing data handling. Our review shows how, over the last period, many components of economic evaluations have changed in accordance with the new recommendations towards more transparent and advanced analytic approaches. However, potential limitations are identified in terms of the use of less advanced statistical software together with rarely satisfactory information to support the choice of missing data methods, especially in sensitivity analysis.

Linear mixed models to handle missing at random data in trial-based economic evaluations

Trial-based cost-effectiveness analyses (CEAs) are an important source of evidence in the assessment of health interventions. In these studies, cost and effectiveness outcomes are commonly measured at multiple time points, but some observations may be missing. Restricting the analysis to the participants with complete data can lead to biased and inefficient estimates. Methods, such as multiple imputation, have been recommended as they make better use of the data available and are valid under less restrictive Missing At Random (MAR) assumption. Linear mixed effects models (LMMs) offer a simple alternative to handle missing data under MAR without requiring imputations, and have not been very well explored in the CEA context. In this manuscript, we aim to familiarize readers with LMMs and demonstrate their implementation in CEA. We illustrate the approach on a randomized trial of antidepressants, and provide the implementation code in R and Stata. We hope that the more familiar statistical framework associated with LMMs, compared to other missing data approaches, will encourage their implementation and move practitioners away from inadequate methods.

Cost-effectiveness of a structured medication review approach for multimorbid older adults: Within-trial analysis of the OPERAM study

Background

Inappropriate polypharmacy has been linked with adverse outcomes in older, multimorbid adults. OPERAM is a European cluster-randomized trial aimed at testing the effect of a structured pharmacotherapy optimization intervention on preventable drug-related hospital admissions in multimorbid adults with polypharmacy aged 70 years or older. Clinical results of the trial showed a pattern of reduced drug-related hospital admissions, but without statistical significance. In this study we assessed the cost-effectiveness of the pharmacotherapy optimisation intervention.

Methods

We performed a pre-planned within-trial cost-effectiveness analysis (CEA) of the OPERAM intervention, from a healthcare system perspective. All data were collected within the trial apart from unit costs. QALYs were computed by applying the crosswalk German valuation algorithm to EQ-5D-5L-based quality of life data. Considering the clustered structure of the data and between-country heterogeneity, we applied Generalized Structural Equation Models (GSEMs) on a multiple imputed sample to estimate costs and QALYs. We also performed analyses by country and subgroup analyses by patient and morbidity characteristics.

Results

Trial-wide, the intervention was numerically dominant, with a potential cost-saving of CHF 3’588 (95% confidence interval (CI): -7’716; 540) and gain of 0.025 QALYs (CI: -0.002; 0.052) per patient. Robustness analyses confirmed the validity of the GSEM model. Subgroup analyses suggested stronger effects in people at higher risk.

Conclusion

We observed a pattern towards dominance, potentially resulting from an accumulation of multiple small positive intervention effects. Our methodological approaches may inform other CEAs of multi-country, cluster-randomized trials facing presence of missing values and heterogeneity between centres/countries.

Economic Outcomes of Rehabilitation Therapy in Older Patients With Acute Heart Failure in the REHAB-HF Trial: A Secondary Analysis of a Randomized Clinical Trial

Importance

In the Rehabilitation Therapy in Older Acute Heart Failure Patients (REHAB-HF) trial, a novel 12-week rehabilitation intervention demonstrated significant improvements in validated measures of physical function, quality of life, and depression, but no significant reductions in rehospitalizations or mortality compared with a control condition during the 6-month follow up. The economic implications of these results are important given the increasing pressures for cost containment in health care.

Objective

To report the economic outcomes of the REHAB-HF trial and estimate the potential cost-effectiveness of the intervention.

Design, Setting, Participants

The multicenter REHAB-HF trial randomized 349 patients 60 years or older who were hospitalized for acute decompensated heart failure to rehabilitation intervention or a control group; patients were enrolled from September 17, 2014, through September 19, 2019. For this preplanned secondary analysis of the economic outcomes, data on medical resource use and quality of life (via the 5-level EuroQol 5-Dimension scores converted to health utilities) were collected. Medical resource use and medication costs were estimated using 2019 US Medicare payments and the Federal Supply Schedule, respectively. Cost-effectiveness was estimated using the validated Tools for Economic Analysis of Patient Management Interventions in Heart Failure Cost-Effectiveness Model, which uses an individual-patient simulation model informed by the prospectively collected trial data. Data were analyzed from March 24, 2019, to December 1, 2020.

Interventions

Rehabilitation intervention or control.

Main Outcomes and Measures

Costs, quality-adjusted life-years (QALYs), and the lifetime estimated cost per QALY gained (incremental cost-effectiveness ratio).

Results

Among the 349 patients included in the analysis (183 women [52.4%]; mean [SD] age, 72.7 [8.1] years; 176 non-White [50.4%] and 173 White [49.6%]), mean (SD) cumulative costs per patient were $26 421 ($38 955) in the intervention group (excluding intervention costs) and $27 650 ($30 712) in the control group (difference, -$1229; 95% CI, -$8159 to $6394; P = .80). The mean (SD) cost of the intervention was $4204 ($2059). Quality of life gains were significantly greater in the intervention vs control group during 6 months (mean utility difference, 0.074; P = .001) and sustained beyond the 12-week intervention. Incremental cost-effectiveness ratios were estimated at $58 409 and $35 600 per QALY gained for the full cohort and in patients with preserved ejection fraction, respectively.

Conclusions and Relevance

These analyses suggest that longer-term benefits of this novel rehabilitation intervention, particularly in the subgroup of patients with preserved ejection fraction, may yield good value to the health care system. However, long-term cost-effectiveness is currently uncertain and dependent on the assumption that benefits are sustained beyond study follow-up, which needs to be corroborated in future trials in this patient population.

Cost-effectiveness analysis of domiciliary topical sevoflurane for painful leg ulcers

Objectives

The general anesthetic sevoflurane is being repurposed as a topical analgesic for painful chronic wounds. We conducted a Bayesian cost-effectiveness analysis (CEA) comparing the addition of domiciliary topical sevoflurane to conventional analgesics (SEVOFLURANE, n = 38) versus conventional analgesics alone (CONVENTIONAL, n = 26) for the treatment of nonrevascularizable painful leg ulcers in an outpatient Pain Clinic of a Spanish tertiary hospital.

Methods

We used real-world data collected from charts to conduct this CEA from a public healthcare perspective and with a one-year time horizon. Costs of analgesics, visits and admissions were considered, expressed in €2016. Analgesic effectiveness was measured with SPID (Sum of Pain Intensity Difference). A Bayesian regression model was constructed, including “treatment” and baseline characteristics for patients (“arterial hypertension”) and ulcers (“duration”, “number”, “depth”, “pain”) as covariates. The findings were summarized as a cost-effectiveness plane and a cost-effectiveness acceptability curve. One-way sensitivity analyses, a re-analysis excluding those patients who died or suffered from leg amputation, and an extreme scenario analysis were conducted to reduce uncertainty.

Results

Compared to CONVENTIONAL, SEVOFLURANE was associated with a 46% reduction in costs, and the mean incremental effectiveness (28.15±3.70 effectiveness units) was favorable to SEVOFLURANE. The estimated probability for SEVOFLURANE being dominant was 99%. The regression model showed that costs were barely influenced by any covariate, whereas effectiveness was noticeably influenced by “treatment”. All sensitivity analyses showed the robustness of the model, even in the extreme scenario analysis against SEVOFLURANE.

Conclusions

SEVOFLURANE was dominant over CONVENTIONAL as it was less expensive and much more effective.

Multivariate Generalized Linear Mixed-Effects Models for the Analysis of Clinical Trial-Based Cost-Effectiveness Data

Economic evaluations conducted alongside randomized controlled trials are a popular vehicle for generating high-quality evidence on the incremental cost-effectiveness of competing health care interventions. Typically, in these studies, resource use (and by extension, economic costs) and clinical (or preference-based health) outcomes data are collected prospectively for trial participants to estimate the joint distribution of incremental costs and incremental benefits associated with the intervention. In this article, we extend the generalized linear mixed-model framework to enable simultaneous modeling of multiple outcomes of mixed data types, such as those typically encountered in trial-based economic evaluations, taking into account correlation of outcomes due to repeated measurements on the same individual and other clustering effects. We provide new wrapper functions to estimate the models in Stata and R by maximum and restricted maximum quasi-likelihood and compare the performance of the new routines with alternative implementations across a range of statistical programming packages. Empirical applications using observed and simulated data from clinical trials suggest that the new methods produce broadly similar results as compared with Stata’s merlin and gsem commands and a Bayesian implementation in WinBUGS. We highlight that, although these empirical applications primarily focus on trial-based economic evaluations, the new methods presented can be generalized to other health economic investigations characterized by multivariate hierarchical data structures.

On the Use of Markov Models in Pharmacoeconomics: Pros and Cons and Implications for Policy Makers

We present an overview of the main methodological features and the goals of pharmacoeconomic models that are classified in three major categories: regression models, decision trees, and Markov models. In particular, we focus on Markov models and define a semi-Markov model on the cost utility of a vaccine for Dengue fever discussing the key components of the model and the interpretation of its results. Next, we identify some criticalities of the decision rule arising from a possible incorrect interpretation of the model outcomes. Specifically, we focus on the difference between median and mean ICER and on handling the willingness-to-pay thresholds. We also show that the life span of the model and an incorrect hypothesis specification can lead to very different outcomes. Finally, we analyse the limit of Markov model when a large number of states is considered and focus on the implementation of tools that can bypass the lack of memory condition of Markov models. We conclude that decision makers should interpret the results of these models with extreme caution before deciding to fund any health care policy and give some recommendations about the appropriate use of these models.

Taking the Analysis of Trial-Based Economic Evaluations to the Next Level: The Importance of Accounting for Clustering

OBJECTIVES

The aim of this study was to assess the performance and impact of multilevel modelling (MLM) compared with ordinary least squares (OLS) regression in trial-based economic evaluations with clustered data.

METHODS

Three thousand datasets with balanced and unbalanced clusters were simulated with correlation coefficients between costs and effects of - 0.5, 0, and 0.5, and intraclass correlation coefficients (ICCs) varying between 0.05 and 0.30. Each scenario was analyzed using both MLM and OLS. Statistical uncertainty around MLM and OLS estimates was estimated using bootstrapping. Performance measures were estimated and compared between approaches, including bias, root mean squared error (RMSE) and coverage probability. Cost and effect differences, and their corresponding confidence intervals and standard errors, incremental cost-effectiveness ratios, incremental net-monetary benefits and cost-effectiveness acceptability curves were compared.

RESULTS

Cost-effectiveness outcomes were similar between OLS and MLM. MLM produced larger statistical uncertainty and coverage probabilities closer to nominal levels than OLS. The higher the ICC, the larger the effect on statistical uncertainty between MLM and OLS. Significant cost-effectiveness outcomes as estimated by OLS became non-significant when estimated by MLM. At all ICCs, MLM resulted in lower probabilities of cost effectiveness than OLS, and this difference became larger with increasing ICCs. Performance measures and cost-effectiveness outcomes were similar across scenarios with varying correlation coefficients between costs and effects.

CONCLUSIONS

Although OLS produced similar cost-effectiveness outcomes, it substantially underestimated the amount of variation in the data compared with MLM. To prevent suboptimal conclusions and a possible waste of scarce resources, it is important to use MLM in trial-based economic evaluations when data are clustered.

The clinical and cost effectiveness of surgical interventions for stones in the lower pole of the kidney: the percutaneous nephrolithotomy, flexible ureterorenoscopy and extracorporeal shockwave lithotripsy for lower pole kidney stones randomised controlled trial (PUrE RCT) protocol

INTRODUCTION

Renal stones are common, with a lifetime prevalence of 10% in adults. Global incidence is increasing due to increases in obesity and diabetes, with these patient populations being more likely to suffer renal stone disease. Flank pain from stones (renal colic) is the most common cause of emergency admission to UK urology departments. Stones most commonly develop in the lower pole of the kidney (in ~35% of cases) and here are least likely to pass without intervention. Currently there are three technologies available within the UK National Health Service to remove lower pole kidney stones: extracorporeal shockwave lithotripsy (ESWL), percutaneous nephrolithotomy (PCNL) and flexible ureterorenoscopy (FURS) with laser lithotripsy. Current evidence indicates there is uncertainty regarding the management of lower pole stones, and each treatment has advantages and disadvantages. The aim of this trial is to determine the clinical and cost effectiveness of FURS compared with ESWL or PCNL in the treatment of lower pole kidney stones.

METHODS

The PUrE (PCNL, FURS and ESWL for lower pole kidney stones) trial is a multi-centre, randomised controlled trial (RCT) evaluating FURS versus ESWL or PCNL for lower pole kidney stones. Patients aged ≥16 years with a stone(s) in the lower pole of either kidney confirmed by non-contrast computed tomography of the kidney, ureter and bladder (CTKUB) and requiring treatment for a stone ≤10 mm will be randomised to receive FURS or ESWL (RCT1), and those requiring treatment for a stone >10 mm to ≤25 mm will be randomised to receive FURS or PCNL (RCT2). Participants will undergo follow-up by questionnaires every week up to 12 weeks post-intervention and at 12 months post-randomisation. The primary clinical outcome is health status measured by the area under the curve calculated from multiple measurements of the EuroQol five dimensions five-level version (EQ-5D-5L) questionnaire up to 12 weeks post-intervention. The primary economic outcome is the incremental cost per quality-adjusted life year gained at 12 months post-randomisation.

DISCUSSION

The PUrE trial aims to provide robust evidence on health status, quality of life, clinical outcomes and resource use to directly inform choice and National Health Service provision of the three treatment options.

TRIAL REGISTRATION

ISRCTN: ISRCTN98970319. Registered on 11 November 2015.

Bayesian Regression Model for a Cost-Utility and Cost-Effectiveness Analysis Comparing Punch Grafting Versus Usual Care for the Treatment of Chronic Wounds

Punch grafting is a traditional technique used to promote epithelialization of hard-to-heal wounds. The main purpose of this observational study was to conduct a cost-utility analysis (CUA) and a cost-effectiveness analysis (CEA) comparing punch grafting (n = 46) with usual care (n = 34) for the treatment of chronic wounds in an outpatient specialized wound clinic from a public healthcare system perspective (Spanish National Health system) with a three-month time horizon. CUA outcome was quality-adjusted life years (QALYs) calculated from EuroQoL-5D, whereas CEA outcome was wound-free period. One-way sensitivity analyses, extreme scenario analysis, and re-analysis by subgroups were conducted to fight against uncertainty. Bayesian regression models were built to explore whether differences between groups in costs, wound-free period, and QALYs could be explained by other variables different to treatment. As main results, punch grafting was associated with a reduction of 37% in costs compared to usual care, whereas mean incremental utility (0.02 ± 0.03 QALYs) and mean incremental effectiveness (7.18 ± 5.30 days free of wound) were favorable to punch grafting. All sensitivity analyses proved the robustness of our models. To conclude, punch grafting is the dominant alternative over usual care because it is cheaper and its utility and effectiveness are greater.

Antibiotic prophylaxis in the surgical management of miscarriage in low-income countries: a cost-effectiveness analysis of the AIMS trial

BACKGROUND

There is ongoing debate on the clinical benefits of antibiotic prophylaxis for reducing pelvic infection after miscarriage surgery. We aimed to study the cost-effectiveness of antibiotic prophylaxis in the surgical management of miscarriage in low-income countries.

METHODS

We did an incremental cost-effectiveness analysis using data from 3412 women recruited to the AIMS trial, a randomised, double-blind, placebo-controlled trial designed to evaluate the effectiveness of antibiotic prophylaxis in the surgical management of miscarriage in Malawi, Pakistan, Tanzania, and Uganda. Economic evaluation was done from a health-care-provider perspective on the basis of the outcome of cost per pelvic infection avoided within 2 weeks of surgery. Pelvic infection was broadly defined by the presence of clinical features or the clinically identified need to administer antibiotics. We used non-parametric bootstrapping and multilevel random effects models to estimate incremental mean costs and outcomes. Decision uncertainty was shown via cost-effectiveness acceptability frontiers. The AIMS trial is registered with the ISRCTN registry, number ISRCTN97143849.

FINDINGS

Between June 2, 2014, and April 26, 2017, 3412 women were assigned to receive either antibiotic prophylaxis (1705 [50%] of 3412) or placebo (1707 [50%] of 3412) in the AIMS trial. 158 (5%) of 3412 women developed pelvic infection within 2 weeks of surgery, of whom 68 (43%) were in the antibiotic prophylaxis group and 90 (57%) in the placebo group. There is 97-98% probability that antibiotic prophylaxis is a cost-effective intervention at expected thresholds of willingness-to-pay per additional pelvic infection avoided. In terms of post-surgery antibiotics, the antibiotic prophylaxis group was US$0·27 (95% CI -0·49 to -0·05) less expensive per woman than the placebo group. A secondary analysis, a sensitivity analysis, and all subgroup analyses supported these findings. Antibiotic prophylaxis, if implemented routinely before miscarriage surgery, could translate to an annual total cost saving of up to $1·4 million across the four participating countries and up to $8·5 million across the two regions of sub-Saharan Africa and south Asia.

INTERPRETATION

Antibiotic prophylaxis is more effective and less expensive than no antibiotic prophylaxis. Policy makers in various settings should be confident that antibiotic prophylaxis in miscarriage surgery is cost-effective.

FUNDING

UK Medical Research Council, Wellcome Trust, and the UK Department for International Development.

Cost-effectiveness of PoNDER health visitor training for mothers at lower risk of depression: findings on prevention of postnatal depression from a cluster-randomised controlled trial

BACKGROUND

There is evidence for the cost-effectiveness of health visitor (HV) training to assess postnatal depression (PND) and deliver psychological approaches to women at risk of depression. Whether this approach is cost-effective for lower-risk women is unknown. There is a need to know the cost of HV-delivered universal provision, and how much it might cost to improve health-related quality of life for postnatal women. A sub-study of a cluster-randomised controlled trial in the former Trent region (England) previously investigated the effectiveness of PoNDER HV training in mothers at lower risk of PND. We conducted a parallel cost-effectiveness analysis at 6-months postnatal for all mothers with lower-risk status attributed to an Edinburgh Postnatal Depression Scale (EPDS) score <12 at 6-weeks postnatal.

METHODS

Intervention HVs were trained in assessment and cognitive behavioural or person-centred psychological support techniques to prevent depression. Outcomes examined: quality-adjusted life-year (QALY) gains over the period between 6 weeks and 6 months derived from SF-6D (from SF-36); risk-of-depression at 6 months (dichotomising 6-month EPDS scores into lower risk (<12) and at-risk (⩾12).

RESULTS

In lower-risk women, 1474 intervention (63 clusters) and 767 control participants (37 clusters) had valid 6-week and 6-month EPDS scores. Costs and outcomes data were available for 1459 participants. 6-month adjusted costs were £82 lower in intervention than control groups, with 0.002 additional QALY gained. The probability of cost-effectiveness at £20 000 was very high (99%).

CONCLUSIONS

PoNDER HV training was highly cost-effective in preventing symptoms of PND in a population of lower-risk women and cost-reducing over 6 months.

Cost-effectiveness of preventive case management for parents with a mental illness: a randomized controlled trial from three economic perspectives

Background

The children of parents with a mental illness (COPMI) are at increased risk for developing costly psychiatric disorders because of multiple risk factors which threaten parenting quality and thereby child development. Preventive basic care management (PBCM) is an intervention aimed at reducing risk factors and addressing the needs of COPMI-families in different domains. The intervention may lead to financial consequences in the healthcare sector and in other sectors, also known as inter-sectoral costs and benefits (ICBs). The objective of this study was to assess the cost-effectiveness of PBCM from three perspectives: a narrow healthcare perspective, a social care perspective (including childcare costs) and a broad societal perspective (including all ICBs).

Methods

Effects on parenting quality (as measured by the HOME) and costs during an 18-month period were studied in in a randomized controlled trial. Families received PBCM (n = 49) or care as usual (CAU) (n = 50). For all three perspectives, incremental cost-effectiveness ratios (ICERs) were calculated. Stochastic uncertainty in the data was dealt with using non-parametric bootstraps. Sensitivity analyses included calculating ICERs excluding cost outliers, and making an adjustment for baseline cost differences.

Results

Parenting quality improved in the PBCM group and declined in the CAU group, and PBCM was shown to be more costly than CAU. ICERs differ from 461 Euros (healthcare perspective) to 215 Euros (social care perspective) to 175 Euros (societal perspective) per one point improvement on the HOME T-score. The results of the sensitivity analyses, based on complete cases and excluding cost outliers, support the finding that the ICER is lower when adopting a broader perspective. The subgroup analysis and the analysis with baseline adjustments resulted in higher ICERs.

Conclusions

This study is the first economic evaluation of family-focused preventive basic care management for COPMI in psychiatric and family services. The effects of the chosen perspective on determining the cost-effectiveness of PBCM underscore the importance of economic studies of interdepartmental policies. Future studies focusing on the cost-effectiveness of programs like PBCM in other sites and studies with more power are encouraged as this may improve the quality of information used in supporting decision making.

Trial registration

NTR2569, date of registration 2010-10-12.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-016-1498-z) contains supplementary material, which is available to authorized users.

Is the choice of the statistical model relevant in the cost estimation of patients with chronic diseases? An empirical approach by the Piedmont Diabetes Registry

BACKGROUND

Chronic diseases impose large economic burdens. Cost analysis is not straightforward, particularly when the goal is to relate costs to specific patterns of covariates, and to compare costs between diseased and healthy populations. Using different statistical methods this study describes the impact on results and conclusions of analyzing health care costs in a population with diabetes.

METHODS

Direct health care costs of people living in Turin were estimated from administrative databases of the Regional Health System. Patients with diabetes were identified through the Piedmont Diabetes Registry. The effect of diabetes on mean annual expenditure was analyzed using the following multivariable models: 1) an ordinary least squares regression (OLS); 2) a lognormal linear regression model; 3) a generalized linear model (GLM) with gamma distribution. Presence of zero cost observation was handled by means of a two part model.

RESULTS

The OLS provides the effect of covariates in terms of absolute additive costs due to the presence of diabetes (€ 1,832). Lognormal and GLM provide relative estimates of the effect: the cost for diabetes would be six fold that for non diabetes patients calculated with the lognormal. The same data give a 2.6-fold increase if calculated with the GLM. Different methods provide quite different estimated costs for patients with and without diabetes, and different costs ratios between them, ranging from 3.2 to 5.6.

CONCLUSIONS

Costs estimates of a chronic disease vary considerably depending on the statistical method employed; therefore a careful choice of methods to analyze data is required before inferring results.

The effectiveness and cost effectiveness of the PAtient-Centred Team (PACT) model: study protocol of a prospective matched control before-and-after study

BACKGROUND

The present study protocol describes the evaluation of a comprehensive integrated care model implemented at two hospital sites at the University Hospital of North Norway (UNN). The PAtient Centred Team (PACT) model includes proactive, patient-centred interdisciplinary teams that aim to improve the continuum and quality of care of frail elderly patients and reduce health care costs. The main objectives of the evaluation are to analyse the effectiveness and cost effectiveness of using patient-centred teams as part of routine service provision for this patient group. The evaluation will analyse the effect on patient health and functional status, patient experiences and hospital utilisation, and it will conduct an economic evaluation. This paper describes the PACT model and the rationale for and design of the planned effectiveness and cost-effectiveness study.

METHODS/DESIGN

This is a prospective, non-randomised matched control before-and-after intervention study. Patients in the intervention group will be recruited from the hospital sites that have implemented the PACT model. The controls will be recruited from two hospitals without the model. The control patients and the index patients will be matched according to sex, age and number of long-term conditions. The study aims to include 600 patients in each group, which will provide sufficient power to detect a clinical change in the primary outcome. The primary outcome is the physical dimension of the Short Form Health Survey (SF-36). Secondary outcomes are the Patient Generated Index (PGI), the Patient Activation Measure (PAM), the Patient Assessment of Chronic Illness Care (PACIC), hospitalisation and length of stay. The cost-effectiveness study takes a health provider perspective and calculates the cost per quality-adjusted life-years (QALYs) gained. The data will be collected at baseline, 6 and 12 months. The data will be analysed using techniques and models that recognise the lack of randomisation and the correlation of cost and effect data.

DISCUSSION

The study results will provide knowledge about whether the integrated care model implemented at UNN improves the quality of care for the frail elderly with multiple conditions. The study will establish whether the PAC. T model improves health and functional status and is cost effective compared to the usual care for this patient group.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT02541474.

Economic Evaluation alongside Multinational Studies: A Systematic Review of Empirical Studies

Purpose of the study

This study seeks to explore methods for conducting economic evaluations alongside multinational trials by conducting a systematic review of the methods used in practice and the challenges that are typically faced by the researchers who conducted the economic evaluations.

Methods

A review was conducted for the period 2002 to 2012, with potentially relevant articles identified by searching the Medline, Embase and NHS EED databases. Studies were included if they were full economic evaluations conducted alongside a multinational trial.

Results

A total of 44 studies out of a possible 2667 met the inclusion criteria. Methods used for the analyses varied between studies, indicating a lack of consensus on how economic evaluation alongside multinational studies should be carried out. The most common challenge appeared to be related to addressing differences between countries, which potentially hinders the generalisability and transferability of results. Other challenges reported included inadequate sample sizes and choosing cost-effectiveness thresholds.

Conclusions

It is recommended that additional guidelines be developed to aid researchers in this area and that these be based on an understanding of the challenges associated with multinational trials and the strengths and limitations of alternative approaches. Guidelines should focus on ensuring that results will aid decision makers in their individual countries.

Cost-effectiveness of integrated care in frail elderly using the ICECAP-O and EQ-5D: does choice of instrument matter?

Economic evaluations likely undervalue the benefits of interventions in populations receiving both health and social services, such as frail elderly, by measuring only health-related quality of life. For this reason, alternative preference-based instruments have been developed for economic evaluations in the elderly, such as the ICECAP-O. The aim of this paper is twofold: (1) to evaluate the cost-effectiveness using a short run time frame for an integrated care model for frail elderly, and (2) to investigate whether using a broader measure of (capability) wellbeing in an economic evaluation leads to a different outcome in terms of cost-effectiveness. We performed univariate and multivariate analyses on costs and outcomes separately. We also performed incremental net monetary benefit regressions using quality adjusted life years (QALYs) based on the ICECAP-O and EQ-5D. In terms of QALYs as measured with the EQ-5D and the ICECAP-O, there were small and insignificant differences between the instruments, due to negligible effect size. Therefore, widespread implementation of the Walcheren integrated care model would be premature based on these results. All results suggest that, using the ICECAP-O, the intervention has a higher probability of cost-effectiveness than with the EQ-5D at the same level of WTP. In case an intervention's health and wellbeing effects are not significant, as in this study, using the ICECAP-O will not lead to a false claim of cost-effectiveness of the intervention. On the other hand, if differences in capability QALYs are meaningful and significant, the ICECAP-O may have the potential to measure broader outcomes and be more sensitive to differences between intervention and comparators.

Mind the Gap! A Multilevel Analysis of Factors Related to Variation in Published Cost-Effectiveness Estimates within and between Countries

Background. Published cost-effectiveness estimates can vary considerably, both within and between countries. Despite extensive discussion, little is known empirically about factors relating to these variations. Objectives. To use multilevel statistical modeling to integrate cost-effectiveness estimates from published economic evaluations to investigate potential causes of variation. Methods. Cost-effectiveness studies of statins for cardiovascular disease prevention were identified by systematic review. Estimates of incremental costs and effects were extracted from reported base case, sensitivity, and subgroup analyses, with estimates grouped in studies and in countries. Three bivariate models were developed: a cross-classified model to accommodate data from multinational studies, a hierarchical model with multinational data allocated to a single category at country level, and a hierarchical model excluding multinational data. Covariates at different levels were drawn from a long list of factors suggested in the literature. Results. We found 67 studies reporting 2094 cost-effectiveness estimates relating to 23 countries (6 studies reporting for more than 1 country). Data and study-level covariates included patient characteristics, intervention and comparator cost, and some study methods (e.g., discount rates and time horizon). After adjusting for these factors, the proportion of variation attributable to countries was negligible in the cross-classified model but moderate in the hierarchical models (14%−19% of total variance). Country-level variables that improved the fit of the hierarchical models included measures of income and health care finance, health care resources, and population risks. Conclusions. Our analysis suggested that variability in published cost-effectiveness estimates is related more to differences in study methods than to differences in national context. Multinational studies were associated with much lower country-level variation than single-country studies. These findings are for a single clinical question and may be atypical.

Rational centre selection for RCTs with a parallel economic evaluation--the next step towards increased generalisability?

The paper discusses the impact of centre selection on the generalisability of randomised controlled trial (RCT)-based economic evaluations and suggests a future research agenda. The first section briefly reviews the current methods for addressing generalisability. We argue that these methods make no verifiable assumptions about how representative the recruiting centres are to the population of centres in the jurisdiction. The second section uses data from a multicentre RCT to illustrate that cost-effectiveness estimates can be influenced by the sample of recruiting centres. Finally, we propose two concepts that may advance generalisability research. First, we distinguish between the 'research space' and the 'policy space' and argue that policy makers are interested in the latter, while current methods describe the former. Second, we propose a centre-specific generalisability index used at RCT design stage to address generalisability. We conclude that future research should focus on generalisability at RCT design stage rather than on post hoc analyses.

Optimal and maximin sample sizes for multicentre cost-effectiveness trials

This paper deals with the optimal sample sizes for a multicentre trial in which the cost-effectiveness of two treatments in terms of net monetary benefit is studied. A bivariate random-effects model, with the treatment-by-centre interaction effect being random and the main effect of centres fixed or random, is assumed to describe both costs and effects. The optimal sample sizes concern the number of centres and the number of individuals per centre in each of the treatment conditions. These numbers maximize the efficiency or power for given research costs or minimize the research costs at a desired level of efficiency or power. Information on model parameters and sampling costs are required to calculate these optimal sample sizes. In case of limited information on relevant model parameters, sample size formulas are derived for so-called maximin sample sizes which guarantee a power level at the lowest study costs. Four different maximin sample sizes are derived based on the signs of the lower bounds of two model parameters, with one case being worst compared to others. We numerically evaluate the efficiency of the worst case instead of using others. Finally, an expression is derived for calculating optimal and maximin sample sizes that yield sufficient power to test the cost-effectiveness of two treatments.

Sample size calculation in cost-effectiveness cluster randomized trials: optimal and maximin approaches

In this paper, the optimal sample sizes at the cluster and person levels for each of two treatment arms are obtained for cluster randomized trials where the cost-effectiveness of treatments on a continuous scale is studied. The optimal sample sizes maximize the efficiency or power for a given budget or minimize the budget for a given efficiency or power. Optimal sample sizes require information on the intra-cluster correlations (ICCs) for effects and costs, the correlations between costs and effects at individual and cluster levels, the ratio of the variance of effects translated into costs to the variance of the costs (the variance ratio), sampling and measuring costs, and the budget. When planning, a study information on the model parameters usually is not available. To overcome this local optimality problem, the current paper also presents maximin sample sizes. The maximin sample sizes turn out to be rather robust against misspecifying the correlation between costs and effects at the cluster and individual levels but may lose much efficiency when misspecifying the variance ratio. The robustness of the maximin sample sizes against misspecifying the ICCs depends on the variance ratio. The maximin sample sizes are robust under misspecification of the ICC for costs for realistic values of the variance ratio greater than one but not robust under misspecification of the ICC for effects. Finally, we show how to calculate optimal or maximin sample sizes that yield sufficient power for a test on the cost-effectiveness of an intervention.

Transferability of health cost evaluation across locations in oncology: cluster and principal component analysis as an explorative tool

Background

The transferability of economic evaluation in health care is of increasing interest in today’s globalized environment. Here, we propose a methodology for assessing the variability of data elements in cost evaluations in oncology. This method was tested in the context of the European Network of Excellence “Connective Tissues Cancers Network”.

Methods

Using a database that was previously aimed at exploring sarcoma management practices in Rhône-Alpes (France) and Veneto (Italy), we developed a model to assess the transferability of health cost evaluation across different locations. A nested data structure with 60 final factors of variability (e.g., unit cost of chest radiograph) within 16 variability areas (e.g., unit cost of imaging) within 12 objects (e.g., diagnoses) was produced in Italy and France, separately. Distances between objects were measured by Euclidean distance, Mahalanobis distance, and city-block metric. A hierarchical structure using cluster analysis (CA) was constructed. The objects were also represented by their projections and area of variability through correlation studies using principal component analysis (PCA). Finally, a hierarchical clustering based on principal components was performed.

Results

CA suggested four clusters of objects: chemotherapy in France; follow-up with relapse in Italy; diagnosis, surgery, radiotherapy, chemotherapy, and follow-up without relapse in Italy; and diagnosis, surgery, and follow-up with or without relapse in France. The variability between clusters was high, suggesting a lower transferability of results. Also, PCA showed a high variability (i.e. lower transferability) for diagnosis between both countries with regard to the quantities and unit costs of biopsies.

Conclusion

CA and PCA were found to be useful for assessing the variability of cost evaluations across countries. In future studies, regression methods could be applied after these methods to elucidate the determinants of the differences found in these analyses.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-014-0537-x) contains supplementary material, which is available to authorized users.

The road not taken: transferability issues in multinational trials

BACKGROUND

National regulatory agencies often have to use cost-effectiveness (CE) data from multinational randomized controlled trials (RCTs) for national decision making on reimbursement of new drugs. We need to make the best use of these patient-level data to obtain estimates of country-specific CE. Several methods, ranging from simple to statistically complex, have existed for years. We investigated which of these methods are used to estimate CE ratios in economic evaluations performed alongside recent, multinational RCTs that enrolled at least 500 patients.

METHODS

In this systematic literature review, studies were classified based on whether resource use, unit costs, health outcomes and utility value sets were obtained from all countries, a subset of countries or one country. We recorded if the study presented trial-wide and country-specific CE results and reported the statistical analyses that were used to estimate them.

RESULTS

We included 21 studies, of which the majority used measurements of health care utilization and health outcomes from all countries to estimate CE. Thirteen studies used a one-country valuation of health care utilization; six used a multi-country valuation. Despite the availability of country-specific utility value sets, none of the studies that presented quality-adjusted life-years (QALYs) used multi-country valuation. Valuation of health care utilization and health outcomes was not always consistent within a study: three studies combined a multi-country valuation of health care utilization, with a one-country valuation of health outcomes. Most studies calculated trial-wide CE estimates, while 11 studies calculated country- or region-specific estimates. Thirteen studies used relatively simple methods, which do not take the possible interaction between the country and treatment effect on health care utilization and health outcomes into account. Eight studies used more advanced statistical methods. Three of them used a fixed-effects modeling approach. Five studies explicitly took the hierarchical structure of the data into account, which leads to more appropriate estimates of population average results and associated standard errors. In this way, they help improve transferability of the published results.

CONCLUSION

Based on this systematic review, we concluded that the uptake of more advanced statistical methods has been relatively slow, while simpler naïve methods are still routinely employed.

Centre selection for clinical trials and the generalisability of results: a mixed methods study

BACKGROUND

The rationale for centre selection in randomised controlled trials (RCTs) is often unclear but may have important implications for the generalisability of trial results. The aims of this study were to evaluate the factors which currently influence centre selection in RCTs and consider how generalisability considerations inform current and optimal practice.

METHODS AND FINDINGS

Mixed methods approach consisting of a systematic review and meta-summary of centre selection criteria reported in RCT protocols funded by the UK National Institute of Health Research (NIHR) initiated between January 2005-January 2012; and an online survey on the topic of current and optimal centre selection, distributed to professionals in the 48 UK Clinical Trials Units and 10 NIHR Research Design Services. The survey design was informed by the systematic review and by two focus groups conducted with trialists at the Birmingham Centre for Clinical Trials. 129 trial protocols were included in the systematic review, with a total target sample size in excess of 317,000 participants. The meta-summary identified 53 unique centre selection criteria. 78 protocols (60%) provided at least one criterion for centre selection, but only 31 (24%) protocols explicitly acknowledged generalisability. This is consistent with the survey findings (n = 70), where less than a third of participants reported generalisability as a key driver of centre selection in current practice. This contrasts with trialists' views on optimal practice, where generalisability in terms of clinical practice, population characteristics and economic results were prime considerations for 60% (n = 42), 57% (n = 40) and 46% (n = 32) of respondents, respectively.

CONCLUSIONS

Centres are rarely enrolled in RCTs with an explicit view to external validity, although trialists acknowledge that incorporating generalisability in centre selection should ideally be more prominent. There is a need to operationalize 'generalisability' and incorporate it at the design stage of RCTs so that results are readily transferable to 'real world' practice.

Resource use and costs of treating acute cough/lower respiratory tract infections in 13 European countries: results and challenges

The objectives of this study were to estimate the resource use and cost of treating acute cough/lower respiratory tract infection (acute cough/LRTI) in 13 European countries, to explore reasons for differences in cost and to document the challenges that researchers face when collecting information on cost alongside multinational studies. Data on resource use and cost were collected alongside an observational study in 14 primary care networks across 13 European countries and a mean cost was generated for each network. The results show that the mean cost (standard deviation) of treating acute cough/LRTI in Europe ranged from euro23.88 (34.67) in Balatonfüred (Hungary) to euro116.47 (34.29) in Jonkoping (Sweden). The observed differences in costs were statistically significant (P < 0.01). Major cost drivers include general practitioner visits and drug costs in all networks, whilst differences in health systems and regional factors could account for differences in cost between networks. The major barrier to conducting multinational cost studies are barriers associated with identifying cost information.

Preventing disease through opportunistic, rapid engagement by primary care teams using behaviour change counselling (PRE-EMPT): protocol for a general practice-based cluster randomised trial

Background

Smoking, excessive alcohol consumption, lack of exercise and an unhealthy diet are the key modifiable factors contributing to premature morbidity and mortality in the developed world. Brief interventions in health care consultations can be effective in changing single health behaviours. General Practice holds considerable potential for primary prevention through modifying patients' multiple risk behaviours, but feasible, acceptable and effective interventions are poorly developed, and uptake by practitioners is low. Through a process of theoretical development, modeling and exploratory trials, we have developed an intervention called Behaviour Change Counselling (BCC) derived from Motivational Interviewing (MI). This paper describes the protocol for an evaluation of a training intervention (the Talking Lifestyles Programme) which will enable practitioners to routinely use BCC during consultations for the above four risk behaviours.

Methods/Design

This cluster randomised controlled efficacy trial (RCT) will evaluate the outcomes and costs of this training intervention for General Practitioners (GPs) and nurses. Training methods will include: a practice-based seminar, online self-directed learning, and reflecting on video recorded and simulated consultations. The intervention will be evaluated in 29 practices in Wales, UK; two clinicians will take part (one GP and one nurse) from each practice. In intervention practices both clinicians will receive training. The aim is to recruit 2000 patients into the study with an expected 30% drop out. The primary outcome will be the proportion of patients making changes in one or more of the four behaviours at three months. Results will be compared for patients seeing clinicians trained in BCC with patients seeing non-BCC trained clinicians. Economic and process evaluations will also be conducted.

Discussion

Opportunistic engagement by health professionals potentially represents a cost effective medical intervention. This study integrates an existing, innovative intervention method with an innovative training model to enable clinicians to routinely use BCC, providing them with new tools to encourage and support people to make healthier choices. This trial will evaluate effectiveness in primary care and determine costs of the intervention.

Trial Registration

ISRCTN22495456

Improving costing methods in multicentre economic evaluation: the use of multiple imputation for unit costs

Economic evaluations must use appropriate costing methods. However, in multicentre cost-effectiveness analyses (CEA) a fundamental issue of how best to measure and analyse unit costs has been neglected. Multicentre CEA commonly take the mean unit cost from a national database, such as NHS reference costs. This approach does not recognise that unit costs vary across centres and are unavailable in some centres. This paper proposes the use of multiple imputation (MI) to predict those centre-specific unit costs that are not available, while recognising the statistical uncertainty surrounding this imputation.We illustrate MI with a CEA of a multicentre randomised trial (1014 patients, 60 centres), implemented using multilevel modelling. We use MI to derive centre-specific unit costs, based on centre characteristics including average casemix, and compare this to using mean NHS reference costs. In this case study, using MI unit costs rather than mean reference costs led to less heterogeneity across centres, more precise estimates of incremental cost, but similar estimates of incremental cost-effectiveness.We conclude that using MI to predict unit costs can preserve correlations, maximise the use of available data, and, when combined with multilevel modelling is an appropriate method for recognising the statistical uncertainty in multicentre CEA.

Bayesian variable selection in cost-effectiveness analysis

Linear regression models are often used to represent the cost and effectiveness of medical treatment. The covariates used may include sociodemographic variables, such as age, gender or race; clinical variables, such as initial health status, years of treatment or the existence of concomitant illnesses; and a binary variable indicating the treatment received. However, most studies estimate only one model, which usually includes all the covariates. This procedure ignores the question of uncertainty in model selection. In this paper, we examine four alternative Bayesian variable selection methods that have been proposed. In this analysis, we estimate the inclusion probability of each covariate in the real model conditional on the data. Variable selection can be useful for estimating incremental effectiveness and incremental cost, through Bayesian model averaging, as well as for subgroup analysis.

Flexibility of Bayesian generalized linear mixed models for oral health research

Many outcome variables in oral research are characterized by positive values and heavy skewness in the right tail. Examples are provided by many distributions of dental variables such as DMF (decayed, missing, filled teeth) scores, oral health impact profile score, gingival index scores, and microbiologic counts. Moreover, heterogeneity in data arises when more than one tooth is studied for each patient, due to the clusterization.Over the past decade, linear mixed models (LMEs) have become a common statistical tool to account for within-subject correlation in data with repeated measures. When a normal error is reasonably assumed, estimates of LMEs are supported by many statistical packages. Such is not the case for skewed data, where generalized linear mixed models (GLMMs) are required. However, the current software available supports only special cases of GLMMs or relies on crude Laplace-type approximation of integrals. In this study, a Bayesian approach is taken to estimate GLMMs for clustered skewed dental data. A Gamma GLMM and a log-normal model are employed to allow for heterogeneity across clusters, deriving from the patient-operator-tooth susceptibility typical of this clinical context. A comparison to the frequentist framework is also provided. In our case, Gamma GLMM fits data better than the log-normal distribution, while providing more precise estimates compared with the likelihood approach. A key advantage of the Bayesian framework is its ability to readily provide a flexible approach for implementation while simultaneously providing a formal procedure for solving inference problems.

The analysis of multinational cost-effectiveness data for reimbursement decisions: a critical appraisal of recent methodological developments

Evidence produced by multinational trial-based cost-effectiveness studies is often used to inform decisions concerning the adoption of new healthcare technologies. A key issue relating to the use of this type of evidence is the extent to which trial-wide economic results are applicable to every single country participating in the study. We consider what role cost-effectiveness analysis alongside multinational trials should have in assisting reimbursement decisions at jurisdiction and national levels. Using the proposed framework as a benchmark to evaluate their relative pros and cons, we then describe and review the statistical approaches used in the multinational trial-based cost-effectiveness literature. The results of the review are used to define the desirable characteristics a statistical method for the analysis of data collected from different jurisdictions should have in order to be consistent with the proposed framework. It is argued that Bayesian hierarchical models that use both patient- and country-level information are the most appropriate tool to analyse multinational trial-based cost-effectiveness data and facilitate the between-country generalizability assessment of the study findings. The merits of each approach are discussed, highlighting problems and limitations, in order to identify areas of future research.

A pragmatic multi-centred randomised controlled trial of yoga for chronic low back pain: trial protocol

A systematic review revealed three small randomised controlled trials of yoga for low back pain, all of which showed effects on back pain that favoured the yoga group. To build on these studies a larger trial, with longer term follow-up, and a number of different yoga teachers delivering the intervention is required. This study protocol describes the details of a randomised controlled trial (RCT) to determine the effectiveness and cost-effectiveness of Yoga for chronic Low Back Pain, which is funded by Arthritis Research Campaign (arc) and is being conducted by the University of York. 262 patients will be recruited from GP practices in 5 centres in England. Patients will be randomised to receive usual care or 12 weekly classes of yoga. A yoga programme will be devised that can be delivered by yoga teachers of the two main national yoga organisations in the UK (British Wheel of Yoga and Iyengar Yoga Association (UK)).

Trial registration: Current controlled trials registry ISRCTN81079604 (date registered 30/03/2007).

Cost effectiveness of enoxaparin in acute ST-segment elevation myocardial infarction: the ExTRACT-TIMI 25 (Enoxaparin and Thrombolysis Reperfusion for Acute Myocardial Infarction Treatment-Thrombolysis In Myocardial Infarction 25) study

OBJECTIVES

We used a U.S. model of health care costs to examine the cost effectiveness of enoxaparin compared with unfractionated heparin (UFH) as adjunctive therapy for fibrinolysis in patients with ST-segment elevation myocardial infarction (STEMI).

BACKGROUND

The ExTRACT-TIMI 25 (Enoxaparin and Thrombolysis Reperfusion for Acute Myocardial Infarction Treatment-Thrombolysis In Myocardial Infarction 25) study, a large, randomized, multinational trial, demonstrated a reduction in death or nonfatal myocardial infarction when enoxaparin was used instead of UFH as adjunctive therapy for fibrinolysis in patients with STEMI.

METHODS

We used patient-level clinical outcomes and resource use from the ExTRACT-TIMI 25 trial and estimates of life expectancy gains as a result of the prevention of the clinical events on the basis of the Framingham Heart Study.

RESULTS

Index hospitalization costs trended lower by $126 in the enoxaparin group (95% confidence interval [CI]: -$295 to $49). Thirty-day costs trended higher by $102 for enoxaparin (95% CI: $108 to $314). Patients receiving enoxaparin gained an average of 0.12 life-years relative to patients given UFH. Estimated total lifetime costs were $1,207 higher in the enoxaparin group (95% CI: $491 to $1,923). The incremental cost-effectiveness ratio of enoxaparin compared with UFH was $5,700 per life-year gained, with 99.9% of bootstrap-derived estimates <$50,000 per life-year gained. Using a probabilistic sensitivity analysis, there is a 90% probability that enoxaparin is cost effective for lifetime, provided that the willingness-to-pay value exceeds $50,000.

CONCLUSIONS

Based on a U.S. model of health care economics, the strategy of using enoxaparin instead of UFH as adjunctive therapy for fibrinolysis in patients with STEMI is cost effective according to commonly used benchmarks.

The ISPOR Good Practices for Quality Improvement of Cost-Effectiveness Research Task Force Report

OBJECTIVES

The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Health Science Policy Council recommended and the ISPOR Board of Directors approved the formation of a Task Force to critically examine the major issues related to Quality Improvement in Cost-effectiveness Research (QICER). The Council's primary recommendation for this Task Force was that it should report on the quality of cost-effectiveness research and make recommendations to facilitate the improvement of pharmacoeconomics and health outcomes research and its use in stimulating better health care and policy. Task force members were knowledgeable and experienced in medicine, pharmacy, biostatistics, health policy and health-care decision-making, biomedical knowledge transfer, health economics, and pharmacoeconomics. They were drawn from industry, academia, consulting organizations, and advisors to governments and came from Japan, the Netherlands, Canada and the United States.

METHODS

Face-to-face meetings of the Task Force were held at ISPOR North American and European meetings and teleconferences occurred every few months. Literature reviews and surveys were conducted and the first preliminary findings presented at an open forum at the May 2008 ISPOR meeting in Toronto. The final draft report was circulated to the expert reviewer group and then to the entire membership for comment. The draft report was posted on the ISPOR Web site in April 2009. All formal comments received were posted to the association Web site and presented for discussion at the Task Force forum during the ISPOR 14th Annual International Meeting in May 2009. Comments and feedback from the forums, reviewers and membership were considered in the final report. Once Task Force consensus was reached, the article was submitted to Value in Health.

CONCLUSIONS

The QICER Task Force recommends that ISPOR implement the following: * With respect to CER guidelines, that ISPOR promote harmonization of guidelines, allowing for differences in application, regional needs and politics; evaluate available instruments or promote development of a new one that will allow standardized quantification of the impact of CER guidelines on the quality of CER studies; report periodically on those countries or regions that have developed guidelines; periodically evaluate the quality of published studies (those journals with CER guidances) or those submitted to decision-making bodies (as public transparency increases). * With respect to methodologies, that ISPOR promote publication of methodological guidelines in more applied journals in more easily understandable format to transfer knowledge to researchers who need to apply more rigorous methods; promote full availability of models in electronic format to combat space restrictions in hardcopy publications; promote consistency of methodological review for all CER studies; promote adoption of explicit best practices guidelines among regulatory and reimbursement authorities; periodically update all ISPOR Task Force reports; periodically review use of ISPOR Task Force guidelines; periodically report on statistical and methodological challenges in HE; evaluate periodically whether ISPOR's methodological guidelines lead to improved quality; and support training and knowledge transfer of rigorous CER methodologies to researchers and health care decision-makers. * With respect to publications, that ISPOR develop standard CER guidances to which journals will be able to refer their authors and their reviewers; lobby to establish these guidances within the International Committee for Medical Journal Editors (ICMJE) Requirements to which most journals refer in their Author Instructions; provide support in terms of additional reviewer expertise to those journals lacking appropriate reviewers; periodically report on journals publishing CER research; periodically report on the quality of CER publications; and support training and knowledge transfer of the use of these guidelines to researchers and reviewers. * With respect to evidence-based health-care decision-making, that ISPOR recognize at its annual meetings those countries/agencies/private companies/researchers using CER well, and those practitioners and researchers supporting good patient use of CER in decision-making; and promote public presentation of case studies of applied use of CER concepts or guidelines.

Bayesian Hierarchical Models for Cost-Effectiveness Analyses that Use Data from Cluster Randomized Trials

Cost-effectiveness analyses (CEA) may be undertaken alongside cluster randomized trials (CRTs) where randomization is at the level of the cluster (for example, the hospital or primary care provider) rather than the individual. Costs (and outcomes) within clusters may be correlated so that the assumption made by standard bivariate regression models, that observations are independent, is incorrect. This study develops a flexible modeling framework to acknowledge the clustering in CEA that use CRTs. The authors extend previous Bayesian bivariate models for CEA of multicenter trials to recognize the specific form of clustering in CRTs. They develop new Bayesian hierarchical models (BHMs) that allow mean costs and outcomes, and also variances, to differ across clusters. They illustrate how each model can be applied using data from a large (1732 cases, 70 primary care providers) CRT evaluating alternative interventions for reducing postnatal depression. The analyses compare cost-effectiveness estimates from BHMs with standard bivariate regression models that ignore the data hierarchy. The BHMs show high levels of cost heterogeneity across clusters (intracluster correlation coefficient, 0.17). Compared with standard regression models, the BHMs yield substantially increased uncertainty surrounding the cost-effectiveness estimates, and altered point estimates. The authors conclude that ignoring clustering can lead to incorrect inferences. The BHMs that they present offer a flexible modeling framework that can be applied more generally to CEA that use CRTs.

Improving efficiency and value in palliative care with net benefit regression: an introduction to a simple method for cost-effectiveness analysis with person-level data

The objective of this article is to illustrate how to do cost-effectiveness analysis (CEA) using net-benefit regression and to explain how this method provides all of the benefits CEA can provide for improving efficiency and value in palliative care. We use a hypothetical data set with person-level data to demonstrate the net-benefit regression framework. Cost and effect data are combined with assumptions about willingness to pay to produce a net-benefit variable for each study participant. This net-benefit variable is the dependent variable in a net-benefit regression. In the simplest formulation, the regression coefficient on the treatment indicator variable estimates the difference in value between extra benefits and extra costs. The estimate and its confidence interval provide policy-relevant information. Net-benefit regression can be used with data from clinical trials or from administrative data sets. The results can be used to help develop policy, with an aim toward improving efficiency and value in health care.

Transferability indices for health economic evaluations: methods and applications

In this paper, we have elaborated an index in two phases to measure the degree of transferability of the results of the economic evaluation of health technologies. In the first phase, we have considered the objective factors (critical and non-critical) to derive a general transferability index, which can be used to measure this internal property of the studies of economic evaluation applied to health technologies. In the second phase, with a more specific index, we have measured the degree of applicability of the results of a given study to a different setting. Both indices have been combined (arithmetic and geometric mean) to obtain a global transferability index. We have applied the global index to a sample of 27 Spanish studies on infectious diseases. We have obtained an average value for the index of 0.54, quite far from the maximum theoretical value of 1. We also found that 11 studies lacked some critical factor and were directly deemed as not transferable.

Transferability of economic evaluations across jurisdictions: ISPOR Good Research Practices Task Force report

ABSTRACT A growing number of jurisdictions now request economic data in support of their decision-making procedures for the pricing and/or reimbursement of health technologies. Because more jurisdictions request economic data, the burden on study sponsors and researchers increases. There are many reasons why the cost-effectiveness of health technologies might vary from place to place. Therefore, this report of an ISPOR Good Practices Task Force reviews what national guidelines for economic evaluation say about transferability, discusses which elements of data could potentially vary from place to place, and recommends good research practices for dealing with aspects of transferability, including strategies based on the analysis of individual patient data and based on decision-analytic modeling.

Shoulder acute pain in primary health care: is retraining GPs effective? The SAPPHIRE randomized trial: a cost-effectiveness analysis

OBJECTIVES

To assess the cost-effectiveness of providing practical training to general practitioners (GPs) in shoulder problems, and administering a local anaesthetic (lignocaine) vs steroidal (cortisone) injection.

METHODS

A cost-effectiveness analysis conducted alongside a cluster randomized trial with a factorial design, in general practices across five centres within the UK. A total of 155 participant GPs were randomized to receive training or no training with 200 participants randomized to either lignocaine or cortisone. Health care costs, quality-adjusted life years (QALYs) and incremental cost per QALY gained over 1 year estimated from a health system and a societal perspective were the main outcomes measured.

RESULTS

Over 1 year, training GPs costs on average an additional pound sterling 211 (95% credibility interval - pound sterling 237, pound sterling 661) than no training and produces higher mean QALYs (0.075; -0.004, 0.154) per patient, yielding an incremental cost-effectiveness ratio of pound sterling 2813 per QALY gained for trained GPs. Over the same period of 1 year, lignocaine costs an average of pound sterling 122 more (- pound sterling 232, pound sterling 476) than cortisone and produces virtually no differential gain in mean QALYs (0.001; -0.068, 0.070), yielding an incremental cost per QALY gained of pound sterling 122,000 for lignocaine compared with cortisone. Across a range of cost-effectiveness thresholds, cortisone is as cost effective to inject as lignocaine. The probability that training is cost effective is above 0.95 at thresholds above pound sterling 20,000.

CONCLUSIONS

Providing practical training to GPs about shoulder problems is cost effective and there is little uncertainty regarding this decision. The choice between lignocaine and cortisone is more uncertain and it is likely that there is significant value of further research to reduce this uncertainty.

TRIAL REGISTRATION

The International Standard Randomised Controlled Trial Number is 58 537 244.

Information on center characteristics as costs' determinants in multicenter clinical trials: is modeling center effect worth the effort?

OBJECTIVES

Several methodological problems arise when health outcomes and resource utilization are collected at different sites. To avoid misleading conclusions in multi-center economic evaluations the center effect needs to be taken into adequate consideration. The aim of this article is to compare several models, which make use of a different amount of information about the enrolling center.

METHODS

To model the association of total medical costs with the levels of two sets of covariates, one at patient and one at center level, we considered four statistical models, based on the Gamma model in the class of the Generalized Linear Models with a log link, which use different amount of information on the enrolling centers. Models were applied to Cost of Strategies after Myocardial Infarction data, an international randomized trial on costs of uncomplicated acute myocardial infarction (AMI).

RESULTS

The simple center effect adjustment based on a single random effect results in a more conservative estimation of the parameters as compared with approaches which make use of deeper information on the centers characteristics.

CONCLUSIONS

This study shows, with reference to a real multicenter trial, that center information cannot be neglected and should be collected and inserted in the analysis, better in combination with one or more random effect, taking into account in this way also the heterogeneity among centers because of unobserved centers characteristics.

Bayesian modelling of healthcare resource use in multinational randomized clinical trials

BACKGROUND

Most cost-effectiveness analyses conducted alongside multinational randomized clinical trials (RCT) are carried out applying the unit costs from the country of interest to trial-wide resource use items with the objective of estimating total healthcare costs by treatment group. However, this approach could confound 'price effects' with 'country effects'. An alternative approach is to use multilevel modelling techniques to analyse healthcare resource use (HCRU) from the trial, and obtain country-specific total costs by applying country-specific unit costs to corresponding shrinkage estimates of differential HCRU.

METHODS

To illustrate the feasibility of this approach, we analysed data from twin multinational RCTs, which enrolled approximately 2000 individuals into three treatment arms for the management of patients with chronic respiratory disease. The models were implemented using Bayesian multilevel models, to reflect the hierarchical structure of the data while controlling for co-variates at the patient and country level.

RESULTS

This analysis showed that directly modelling the level of HCRU is a promising approach to facilitate cost-effectiveness analyses conducted alongside multinational RCTs, offering several advantages compared with the modelling of direct costs.

CONCLUSIONS

It is argued that modelling the level of HCRU within the Bayesian framework avoids confounding the price effects with the country effects and facilitates the estimation of costs for several countries represented in the trial.

Decision-analytical modelling in health-care economic evaluations

OBJECTIVE

Decision-analytical modelling is widely used in health-care economic evaluations, especially in situations where evaluators lack clinical trial data, and in circumstances where such evaluations factor into reimbursement pricing decisions. This paper aims to improve the understanding and use of modelling techniques in this context, with particular emphasis on Markov modelling.

METHODS

We provide an overview, in this paper, of the principles and methodological details of decision-analytical modelling. We propose a common route for practicing modelling that accommodates any type of decision-analytical modelling techniques. We use the treatment of chronic hepatitis B as an example to indicate the process of development, presentation and analysis of the Markov model, and discuss the strengths, weaknesses and pitfalls of different approaches.

CONCLUSIONS

Good practice of modelling requires careful planning, conduct and analysis of the model, and needs input from modellers and users.