Extrapolation beyond the end of trials to estimate long term survival and cost effectiveness

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.

Assessing the Performance of Alternative Methods for Estimating Long-Term Survival Benefit of Immuno-oncology Therapies

OBJECTIVES

This study aimed to determine the accuracy and consistency of established methods of extrapolating mean survival for immuno-oncology (IO) therapies, the extent of any systematic biases in estimating long-term clinical benefit, what influences the magnitude of any bias, and the potential implications for health technology assessment.

METHODS

A targeted literature search was conducted to identify published long-term follow-up from clinical trials of immune-checkpoint inhibitors. Earlier published results were identified and Kaplan-Meier estimates for short- and long-term follow-up were digitized and converted to pseudo-individual patient data using an established algorithm. Six standard parametric, 5 flexible parametric, and 2 mixture-cure models (MCMs) were used to extrapolate long-term survival. Mean and restricted mean survival time (RMST) were estimated and compared between short- and long-term follow-up.

RESULTS

Predicted RMST from extrapolation of early data underestimated observed RMST in long-term follow-up for 184 of 271 extrapolations. All models except the MCMs frequently underestimated observed RMST. Mean survival estimates increased with longer follow-up in 196 of 270 extrapolations. The increase exceeded 20% in 122 extrapolations. Log-logistic and log-normal models showed the smallest change with additional follow-up. MCM performance varied substantially with functional form.

CONCLUSIONS

Standard and flexible parametric models frequently underestimate mean survival for IO treatments. Log-logistic and log-normal models may be the most pragmatic and parsimonious solutions for estimating IO mean survival from immature data. Flexible parametric models may be preferred when the data used in health technology assessment are more mature. MCMs fitted to immature data produce unreliable results and are not recommended.

Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments

With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving into their interplay and nuances that go beyond economic model structures. We synthesised a 'roadmap' of literature from key methodological studies, highlighting the evolution of recent advances and emerging research themes. These insights were compared against HTA guidelines to identify potential avenues for future research. Our findings reveal a spectrum of challenges in sequence evaluation, encompassing selecting appropriate decision-analytic modelling approaches and comparators, deriving appropriate clinical effectiveness evidence in the face of data scarcity, scrutinising effectiveness assumptions and statistical adjustments, considering treatment displacement, and optimising model computations. Integrating methodologies from diverse disciplines-statistics, epidemiology, causal inference, operational research and computer science-has demonstrated promise in addressing these challenges. An updated review of application studies is warranted to provide detailed insights into the extent and manner in which these methodologies have been implemented. Data scarcity on the effectiveness of treatment sequences emerged as a dominant concern, especially because treatment sequences are rarely compared in clinical trials. Real-world data (RWD) provide an alternative means for capturing evidence on effectiveness and future research should prioritise harnessing causal inference methods, particularly Target Trial Emulation, to evaluate treatment sequence effectiveness using RWD. This approach is also adaptable for analysing trials harbouring sequencing information and adjusting indirect comparisons when collating evidence from heterogeneous sources. Such investigative efforts could lend support to reviews of HTA recommendations and contribute to synthesising external control arms involving treatment sequences.

Comparing Survival Extrapolation within All-Cause and Relative Survival Frameworks by Standard Parametric Models and Flexible Parametric Spline Models Using the Swedish Cancer Registry

BACKGROUND

In health technology assessment, restricted mean survival time and life expectancy are commonly evaluated. Parametric models are typically used for extrapolation. Spline models using a relative survival framework have been shown to estimate life expectancy of cancer patients more reliably; however, more research is needed to assess spline models using an all-cause survival framework and standard parametric models using a relative survival framework.

AIM

To assess survival extrapolation using standard parametric models and spline models within relative survival and all-cause survival frameworks.

METHODS

From the Swedish Cancer Registry, we identified patients diagnosed with 5 types of cancer (colon, breast, melanoma, prostate, and chronic myeloid leukemia) between 1981 and 1990 with follow-up until 2020. Patients were categorized into 15 cancer cohorts by cancer and age group (18-59, 60-69, and 70-99 y). We right-censored the follow-up at 2, 3, 5, and 10 y and fitted the parametric models within an all-cause and a relative survival framework to extrapolate to 10 y and lifetime in comparison with the observed Kaplan-Meier survival estimates. All cohorts were modeled with 6 standard parametric models (exponential, Weibull, Gompertz, log-logistic, log-normal, and generalized gamma) and 3 spline models (on hazard, odds, and normal scales).

RESULTS

For predicting 10-y survival, spline models generally performed better than standard parametric models. However, using an all-cause or a relative survival framework did not show any distinct difference. For lifetime survival, extrapolating from a relative survival framework agreed better with the observed survival, particularly using spline models.

CONCLUSIONS

For extrapolation to 10 y, we recommend spline models. For extrapolation to lifetime, we suggest extrapolating in a relative survival framework, especially using spline models.

HIGHLIGHTS

For survival extrapolation to 10 y, spline models generally performed better than standard parametric models did. However, using an all-cause or a relative survival framework showed no distinct difference under the same parametric model.Survival extrapolation to lifetime within a relative survival framework agreed well with the observed data, especially using spline models.Extrapolating parametric models within an all-cause survival framework may overestimate survival proportions at lifetime; models for the relative survival approach may underestimate instead.

Digital Alcohol Interventions Could Be Part of the Societal Response to Harmful Consumption, but We Know Little About Their Long-Term Costs and Health Outcomes

Alcohol consumption causes both physical and psychological harm and is a leading risk factor for noncommunicable diseases. Digital alcohol interventions have been found to support those looking for help by giving them tools for change. However, whether digital interventions can help tackle the long-term societal consequences of harmful alcohol consumption in a cost-effective manner has not been adequately evaluated. In this Viewpoint, we propose that studies of digital alcohol interventions rarely evaluate the consequences of wider dissemination of the intervention under study, and that when they do, they do not take advantage of modeling techniques that allow for appropriately studying consequences over a longer time horizon than the study period when the intervention is tested. We argue that to help decision-makers to prioritize resources for research and dissemination, it is important to model long-term costs and health outcomes. Further, this type of modeling gives important insights into the context in which interventions are studied and highlights where more research is required and where sufficient evidence is available. The viewpoint therefore invites the researcher not only to reflect on which interventions to study but also how to evaluate their long-term consequences.

Early Cost-Effectiveness Analysis of Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy for Gastric Cancer Patients with Limited Peritoneal Carcinomatosis

BACKGROUND

Gastric cancer patients with peritoneal carcinomatosis (PC) have a poor prognosis, with a median overall survival of 10 months when treated with systemic chemotherapy only. Cohort studies showed that cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) might improve the prognosis for gastric cancer patients with limited PC. Besides generating trial data on clinical effectiveness, it is crucial to timely collect information on economic aspects to guide the reimbursement decision-making process. No previous data have been published on the cost(-effectiveness) of CRS/HIPEC in this group of patients. Therefore, we performed an early model-based cost-effectiveness analysis of CRS/HIPEC for gastric cancer patients with limited PC in the Dutch setting.

METHODS

We constructed a two-state (alive-dead) Markov transition model to evaluate costs and clinical outcomes from a Dutch healthcare perspective. Clinical outcomes, transition probabilities and utilities were derived from literature and verified by clinical experts in the field. Costs were measured using two available representative cohorts (2010-2017): one 'systemic chemotherapy only' cohort and one 'CRS/HIPEC' cohort (n = 10 each). Incremental cost-utility ratios (ICURs) were expressed as Euros per quality-adjusted life-year (QALY). We performed probabilistic and deterministic sensitivity, scenario, and value-of-information analyses using a willingness-to-pay (WTP) threshold of €80,000/QALY, which reflects the Dutch norm for severe diseases.

RESULTS

In the base-case analysis, CRS/HIPEC yielded more QALYs (increment of 0.68) and more costs (increment of €34,706) compared with systemic chemotherapy only, resulting in an ICUR of €50,990/QALY. The probability that CRS/HIPEC was cost effective compared with systemic chemotherapy alone was 64%. To reduce uncertainty, the expected value of perfect information amounted to €4,021,468. The scenario analyses did not alter the results and showed that treatment costs, lifetime health-related quality of life and overall survival had the largest influence on the model.

CONCLUSIONS

The presented early cost-effectiveness analysis suggests that adding CRS/HIPEC to systemic chemotherapy for gastric cancer patients with limited PC has a good chance of being cost-effectiveness compared with systemic chemotherapy alone when using a WTP of €80,000/QALY. However, there is substantial uncertainty in view of the current available data on effectiveness. Results from the ongoing phase III PERISCOPE II trial are therefore crucial for further decisions on treatment policy and its cost-effectiveness.

survextrap: a package for flexible and transparent survival extrapolation

BACKGROUND

Health policy decisions are often informed by estimates of long-term survival based primarily on short-term data. A range of methods are available to include longer-term information, but there has previously been no comprehensive and accessible tool for implementing these.

RESULTS

This paper introduces a novel model and software package for parametric survival modelling of individual-level, right-censored data, optionally combined with summary survival data on one or more time periods. It could be used to estimate long-term survival based on short-term data from a clinical trial, combined with longer-term disease registry or population data, or elicited judgements. All data sources are represented jointly in a Bayesian model. The hazard is modelled as an M-spline function, which can represent potential changes in the hazard trajectory at any time. Through Bayesian estimation, the model automatically adapts to fit the available data, and acknowledges uncertainty where the data are weak. Therefore long-term estimates are only confident if there are strong long-term data, and inferences do not rely on extrapolating parametric functions learned from short-term data. The effects of treatment or other explanatory variables can be estimated through proportional hazards or with a flexible non-proportional hazards model. Some commonly-used mechanisms for survival can also be assumed: cure models, additive hazards models with known background mortality, and models where the effect of a treatment wanes over time. All of these features are provided for the first time in an R package, survextrap, in which models can be fitted using standard R survival modelling syntax. This paper explains the model, and demonstrates the use of the package to fit a range of models to common forms of survival data used in health technology assessments.

CONCLUSIONS

This paper has provided a tool that makes comprehensive and principled methods for survival extrapolation easily usable.

Assessing the Mortality Rate After Primary Total Knee Arthroplasty: An Observational Study to Inform Future Economic Analysis

BACKGROUND

Previous research has focused on the perioperative or short-term (<1 year) mortality rate of total knee arthroplasty (TKA), leaving the long-term (>1 year) mortality rate unresolved. In this study, we calculated the mortality rate up to 15 years after primary TKA.

METHODS

Data from the New Zealand Joint Registry from April 1998 to December 2021 were analyzed. Patients aged 45 years or older who underwent TKA for osteoarthritis were included. Mortality data were linked with national records from births, deaths, and marriages. To determine the expected mortality rates in the general population, age-sex-specific life tables from statistics New Zealand were used. Mortality rate was presented as standardized mortality ratios (SMRs) - a comparison of relative mortality rate between the TKA and general populations. In total, 98,156 patients with a median follow-up of 7.25 years (range, 0.00 to 23.74) were included.

RESULTS

Over the entire follow-up period, 22,938 patients (23.4%) had died. The overall SMR for the TKA cohort was 1.08 (95% confidence interval (CI): 1.06 to 1.09), suggesting that TKA patients have an 8% higher mortality rate compared to the general population. However, a reduction in short-term mortality rate was observed for TKA patients up to 5 years post TKA (SMR 5 years post TKA; 0.59 95% CI: 0.57 to 0.60]). On the contrary, a significantly increased long-term mortality rate was observed in TKA patients with greater than 11 years of follow-up, particularly in men over the age of 75 years (SMR 11 to 15 years post TKA for males ≥ 75 years; 3.13 [95% CI: 2.95 to 3.31]).

CONCLUSION

The results suggest a reduction in short-term mortality rate for patients who undergo primary TKA. However, there is an increased long-term mortality rate particularly in men over the age of 75 years. Importantly, the mortality rates observed in this study cannot be causally attributed to TKA alone.

Cost-Effectiveness Analysis of Monoclonal Antibodies Associated With Chemotherapy in First-Line Treatment of Metastatic Colorectal Cancer

OBJECTIVES

This study aimed to evaluate the cost-effectiveness of anti-epidermal growth factor receptor (cetuximab and panitumumab) or anti-vascular endothelial growth factor (bevacizumab) monoclonal antibodies associated with conventional chemotherapy (CT) (fluorouracil and leucovorin with irinotecan) as a first-line treatment for unresectable metastatic colorectal cancer.

METHODS

A partitioned survival analysis model was adopted to simulate direct health costs and benefits comparing therapeutic options in a 10 years' time horizon. Model data were extracted from the literature and costs were obtained from Brazilian official government databases. The analysis considered the perspective of the Brazilian Public Health System; costs were measured in local currency (BRL) and benefits in quality-adjusted life-years (QALY). A 5% discount rate was applied to costs and benefits. Alternative willingness-to-pay scenarios, varying from 3 to 5 times the cost-effectiveness threshold established in Brazil, were estimated. The results were presented incremental cost-effectiveness ratio (ICER), and both deterministic and probabilistic sensitivity analyses were performed.

RESULTS

The most cost-effective choice would be the association of CT with panitumumab, with an ICER of $58 330.15/QALY compared with isolated CT. The second-best option was CT with bevacizumab and panitumumab, with an ICER of $71 195.40/QALY compared with panitumumab alone. Although having higher costs, the second-best option was the most effective. Both strategies were cost-effective in part of the Monte Carlo iterations, considering the 3× threshold.

CONCLUSIONS

The therapeutic option CT + panitumumab + bevacizumab represents the most significant effectiveness gain in our study. It is the second-lowest cost-effectiveness, and this option includes monoclonal antibodies association for patients with and without KRAS mutation.

Survival Extrapolation Incorporating General Population Mortality Using Excess Hazard and Cure Models: A Tutorial

BACKGROUND

Different parametric survival models can lead to widely discordant extrapolations and decision uncertainty in cost-effectiveness analyses. The use of excess hazard (EH) methods, which incorporate general population mortality data, has the potential to reduce model uncertainty. This review highlights key practical considerations of EH methods for estimating long-term survival.

METHODS

Demonstration of methods used a case study of 686 patients from the German Breast Cancer Study Group, followed for a maximum of 7.3 y and divided into low (1/2) and high (3) grade cancers. Seven standard parametric survival models were fit to each group separately. The same 7 distributions were then used in an EH framework, which incorporated general population mortality rates, and fitted both with and without a cure parameter. Survival extrapolations, restricted mean survival time (RMST), and difference in RMST between high and low grades were compared up to 30 years along with Akaike information criterion goodness-of-fit and cure fraction estimates. The sensitivity of the EH models to lifetable misspecification was investigated.

RESULTS

In our case study, variability in survival extrapolations was extensive across the standard models, with 30-y RMST ranging from 7.5 to 14.3 y. Incorporation of general population mortality rates using EH cure methods substantially reduced model uncertainty, whereas EH models without cure had less of an effect. Long-term treatment effects approached the null for most models but at varying rates. Lifetable misspecification had minimal effect on RMST differences.

CONCLUSIONS

EH methods may be useful for survival extrapolation, and in cancer, EHs may decrease over time and be easier to extrapolate than all-cause hazards. EH cure models may be helpful when cure is plausible and likely to result in less extrapolation variability.

HIGHLIGHTS

In health economic modeling, to help anchor long-term survival extrapolation, it has been recommended that survival models incorporate background mortality rates using excess hazard (EH) methods.We present a thorough description of EH methods with and without the assumption of cure and demonstrate user-friendly software to aid researchers wishing to use these methods.EH models are applied to a case study, and we demonstrate that EHs are easier to extrapolate and that the use of the EH cure model, when cure is plausible, can reduce extrapolation variability.EH methods are relatively robust to lifetable misspecification.

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.

Blended Survival Curves: A New Approach to Extrapolation for Time-to-Event Outcomes from Clinical Trials in Health Technology Assessment

BACKGROUND

Survival extrapolation is essential in cost-effectiveness analysis to quantify the lifetime survival benefit associated with a new intervention, due to the restricted duration of randomized controlled trials (RCTs). Current approaches of extrapolation often assume that the treatment effect observed in the trial can continue indefinitely, which is unrealistic and may have a huge impact on decisions for resource allocation.

OBJECTIVE

We introduce a novel methodology as a possible solution to alleviate the problem of survival extrapolation with heavily censored data from clinical trials.

METHOD

The main idea is to mix a flexible model (e.g., Cox semiparametric) to fit as well as possible the observed data and a parametric model encoding assumptions on the expected behavior of underlying long-term survival. The two are "blended" into a single survival curve that is identical with the Cox model over the range of observed times and gradually approaching the parametric model over the extrapolation period based on a weight function. The weight function regulates the way two survival curves are blended, determining how the internal and external sources contribute to the estimated survival over time.

RESULTS

A 4-y follow-up RCT of rituximab in combination with fludarabine and cyclophosphamide versus fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia is used to illustrate the method.

CONCLUSION

Long-term extrapolation from immature trial data may lead to significantly different estimates with various modelling assumptions. The blending approach provides sufficient flexibility, allowing a wide range of plausible scenarios to be considered as well as the inclusion of external information, based, for example, on hard data or expert opinion. Both internal and external validity can be carefully examined.

HIGHLIGHTS

Interim analyses of trials with limited follow-up are often subject to high degrees of administrative censoring, which may result in implausible long-term extrapolations using standard approaches.In this article, we present an innovative methodology based on "blending" survival curves to relax the traditional proportional hazard assumption and simultaneously incorporate external information to guide the extrapolation.The blended method provides a simple and powerful framework to allow a careful consideration of a wide range of plausible scenarios, accounting for model fit to the short-term data as well as the plausibility of long-term extrapolations.

Exploring uncertainty and use of real-world data in the National Institute for Health and Care Excellence single technology appraisals of targeted cancer therapy

OBJECTIVES

Dealing with uncertainty is one of the critical topics in health technology assessment. The greater decision uncertainty in appraisals, the less clear the clinical- and cost-effectiveness of the health technology. Although the development of targeted cancer therapies (TCTs) has improved patient health care, additional complexity has been introduced in drug appraisals due to targeting more specific populations. Real-world data (RWD) are expected to provide helpful information to fill the evidence gaps in appraisals. This study compared appraisals of TCTs with those of non-targeted cancer therapies (non-TCTs) regarding sources of uncertainty and reviewed how RWD have been used to supplement the information in these appraisals.

METHODS

This study reviews single technology appraisals (STAs) of oncology medicines performed by the National Institute for Health and Care Excellence (NICE) over 11 years up to December 2021. Three key sources of uncertainty were identified for comparison (generalisability of clinical trials, availability of direct treatment comparison, maturity of survival data in clinical trials). To measure the intensity of use of RWD in appraisals, three components were identified (overall survival, volume of treatment, and choice of comparators).

RESULTS

TCTs received more recommendations for provision through the Cancer Drugs Fund (27.7, 23.6% for non-TCT), whereas similar proportions were recommended for routine commissioning. With respect to sources of uncertainty, the external validity of clinical trials was greater in TCT appraisals (p = 0.026), whereas mature survival data were available in fewer TCT appraisals (p = 0.027). Both groups showed similar patterns of use of RWD. There was no clear evidence that RWD have been used more intensively in appraisals of TCT.

CONCLUSIONS

Some differences in uncertainty were found between TCT and non-TCT appraisals. The appraisal of TCT is generally challenging, but these challenges are neither new nor distinctive. The same sources of uncertainty were often found in the non-TCT appraisals. The uncertainty when appraising TCT stems from insufficient data rather than the characteristics of the drugs. Although RWD might be expected to play a more active role in appraisals of TCT, the use of RWD has generally been limited.