Parametric Survival Extrapolation of Early Survival Data in Economic Analyses: A Comparison of Projected Versus Observed Updated Survival

ObjectivesTo establish the value of cancer drugs by cost-effectiveness analysis, lifetime parametric survival extrapolations are often fitted to early data. Recent literature suggests that the benefit of cancer agents in primary publications is often different compared with updated data. This study aimed to examine the projected survival based on parametric extrapolations compared with observed survival based on updated data.MethodsUS Food and Drug Administration oncology approvals from January 2006 to December 2015 were reviewed to identify randomized controlled trials, with updated overall survival (OS) or progression-free survival (PFS) data within 5 years. Individual patient data were reconstructed using established methods on initial and updated publications. Projected survival was calculated as the best-fit parametric restricted mean survival time (RMST) based on extrapolated initial Kaplan-Meier curves whereas observed survival was calculated as observed RMST based on updated Kaplan-Meier curves. Mean deviations, mean absolute error (MAE), mean absolute percentage error, and linear regressions were conducted to examine the relationship between projected and observed survival.ResultsIn total, 32 randomized controlled trials were included. The MAE between the projected RMST and observed RMST was 3.18 months (OS) and 2.84 months (PFS) and absolute percentage error of 100% (OS) and 23% (PFS), suggesting substantial imprecision of the projected RMST in predicting the updated RMST. The linear regression indicated MAE increased as time extrapolated and as the percentage of censored patients increased.ConclusionsThis study demonstrated substantial difference in projected survival between initial and updated publications. Health technology assessment committees need to be aware of the potential uncertainty of incremental effectiveness and resultant value-for-money assessment when making reimbursement decisions based on initial publications with immature survival data.     

Survival Extrapolation in Cancer Immunotherapy: A Validation-Based Case Study

Background

Immune-checkpoint inhibitors may provide long-term survival benefits via a cured proportion, yet data are usually insufficient to prove this upon submission to health technology assessment bodies.

The Extrapolation Performance of Survival Models for Data With a Cure Fraction: A Simulation Study

ObjectivesCurative treatments can result in complex hazard functions. The use of standard survival models may result in poor extrapolations. Several models for data which may have a cure fraction are available, but comparisons of their extrapolation performance are lacking. A simulation study was performed to assess the performance of models with and without a cure fraction when fit to data with a cure fraction.MethodsData were simulated from a Weibull cure model, with 9 scenarios corresponding to different lengths of follow-up and sample sizes. Cure and noncure versions of standard parametric, Royston-Parmar, and dynamic survival models were considered along with noncure fractional polynomial and generalized additive models. The mean-squared error and bias in estimates of the hazard function were estimated.ResultsWith the shortest follow-up, none of the cure models provided good extrapolations. Performance improved with increasing follow-up, except for the misspecified standard parametric cure model (lognormal). The performance of the flexible cure models was similar to that of the correctly specified cure model. Accurate estimates of the cured fraction were not necessary for accurate hazard estimates. Models without a cure fraction provided markedly worse extrapolations.ConclusionsFor curative treatments, failure to model the cured fraction can lead to very poor extrapolations. Cure models provide improved extrapolations, but with immature data there may be insufficient evidence to choose between cure and noncure models, emphasizing the importance of clinical knowledge for model choice. Dynamic cure fraction models were robust to model misspecification, but standard parametric cure models were not.     

Approaches for Enhanced Extrapolation of Long-Term Survival Outcomes Using Electronic Health Records of Patients With Cancer

ObjectivesThis study aimed to demonstrate enhanced survival extrapolation methods using electronic health record-derived real-world data (RWD).MethodsThe study population included patients diagnosed of ER+/HER2? metastatic breast cancer who started first-line treatment with anastrozole or letrozole between November 18, 2014, and November 18, 2015. Two patient cohorts were constructed: a clinical trial cohort from digitized MONARCH-3 clinical trial results and a RWD cohort from a deidentified electronic health record-derived database. RWD patients were weighted to trial baseline covariate distributions. Standard parametric approaches were applied to trial data and a “best-fit” model was selected. We demonstrate traditional and enhanced hybrid (pooling with weighted RWD at start, 75%, or end of trial) extrapolation approaches.ResultsObserved and estimated 5-year progression-free survival (PFS) rates in extrapolating the trial control arm (n = 165) were comparable across all methods. Compared with the observed 5-year mean PFS in the RWD cohort (n = 118) of 20.4 months (95% confidence interval [CI] 16.9-23.8), there was some variation among studied methods. Best-fit standard parametric model (log-normal) had 5-year mean PFS of 21.3 months (95% CI 18.2-24.9), and for the hybrid methods in order of estimate conservativeness was start of trial (20.8 months; 95% CI 18.5-23.2), 75% of trial (21.3 months; 95% CI 18.1-24.5), and end of trial (21.8 months; 95% CI 18.8-25.2).ConclusionsOur study leverages RWD to enhance long-term survival extrapolation. Future use cases should include applying patient eligibility criteria, weighting on baseline characteristics, and choice of time window to add RWD to trial data.     

Predicting Survival for Chimeric Antigen Receptor T-Cell Therapy: A Validation of Survival Models Using Follow-Up Data From ZUMA-1

ObjectivesSurvival extrapolation for chimeric antigen receptor T-cell therapies is challenging, owing to their unique mechanistic properties that translate to complex hazard functions. Axicabtagene ciloleucel is indicated for the treatment of relapse or refractory diffuse large B-cell lymphoma after 2 or more lines of therapy based on the ZUMA-1 trial. Four data snapshots are available, with minimum follow-up of 12, 24, 36, and 48 months. This analysis explores how survival extrapolations for axicabtagene ciloleucel using ZUMA-1 data can be validated and compared.MethodsThree different parametric modeling approaches were applied: standard parametric, spline-based, and cure-based models. Models were compared using a range of metrics, across the 4 data snapshot, including visual fit, plausibility of long-term estimates, statistical goodness of fit, inspection of hazard plots, point-estimate accuracy, and conditional survival estimates.ResultsStandard and spline-based parametric extrapolations were generally incapable of fitting the ZUMA-1 data well. Cure-based models provided the best fit based on the earliest data snapshot, with extrapolations remaining consistent as data matured. At 48 months, the maximum survival overestimate was 8.3% (Gompertz mixture-cure model) versus the maximum underestimate of 33.5% (Weibull standard parametric model).ConclusionsWhere a plateau in the survival curve is clinically plausible, cure-based models may be helpful in making accurate predictions based on immature data. The ability to reliably extrapolate from maturing data may reduce delays in patient access to potentially lifesaving treatments. Additional research is required to understand how models compare in broader contexts, including different treatments and therapeutic areas.     

Prevalence of Immature Survival Data for Anti-Cancer Drugs Presented to the National Institute for Health and Care Excellence and Impact on Decision Making

ObjectivesThis research aims to explore how often the National Institute for Health and Care Excellence (NICE) uses immature overall survival data to inform reimbursement decisions on cancer treatments, and the implications of this for resource allocation decisions.MethodsNICE cancer technology appraisals published between 2015 and 2017 were reviewed to determine the prevalence of using immature survival data. A case study was used to demonstrate the potential impact of basing decisions on immature data. The economic model submitted by the company was reconstructed and was populated first using survival data available at the time of the appraisal, and then using data from an updated data cut published after the appraisal concluded. The incremental cost-effectiveness ratios (ICERs) obtained using the different data cuts were compared. Probabilistic sensitivity analysis was undertaken and expected value of perfect information estimated.ResultsForty-one percent of NICE cancer technology appraisals used immature data to inform reimbursement decisions. In the case study, NICE gave a positive recommendation for a limited patient subgroup, with ICERs too high in the complete patient population. ICERs were dramatically lower when the final data cut was used, irrespective of the parametric model used to model survival. Probabilistic sensitivity analysis and expected value of perfect information may not have fully characterized uncertainty, because as they did not account for structural uncertainty.ConclusionAnalyses of cancer treatments using immature survival data may result in incorrect estimates of survival benefit and cost-effectiveness, potentially leading to inappropriate funding decisions. This research highlights the importance of revisiting past decisions when updated data cuts become available.     

Developing Markov Models From Real-World Data: A Case Study of Heart Failure Modeling Using Administrative Data

ObjectivesMarkov models characterize disease progression as specific health states based on clinical or biological measures. However, these measures are not always collected outside clinical trials. In this article, an alternative approach is presented that uses real-world data to define the health states and to model transitions between them, specific to a local setting, to estimate the cost-effectiveness of telemonitoring (TM) versus no TM for heart failure.MethodsThe incidence of hospitalization for usual care was estimated from hospital episode statistics (HES) data in the United Kingdom and converted into a monthly transition matrix with 5 health states (4 states are defined based on the number of hospitalizations in the previous year and death) to estimate the cost-effectiveness of TM in a local UK primary care trust (PCT) using probabilistic sensitivity analysis from a healthcare perspective.ResultsGeographical variation in hospitalization rates were present, which led to different health state transition matrices in different localities. In the PCT that was evaluated, TM accrued mean additional costs of £3610 and 0.075 additional quality-adjusted life-years (QALYs) compared with usual care per patient, resulting in a mean incremental cost effectiveness ratio of £48 172/QALY.ConclusionsThe use of administrative data to define health states and transition matrices based on health service events is feasible, and TM was not cost-effective in our analysis. Given the increasing emphasis on using real-world evidence, it is likely that these approaches will be used more in the future.     

Estimating and Extrapolating Survival Using a State-Transition Modeling Approach: A Practical Application in Multiple Myeloma

ObjectivesState-transition models (STMs) applied in oncology have given limited considerations to modeling postprogression survival data. This study presents an application of an STM focusing on methods to evaluate the postprogression transition and its impact on survival predictions.MethodsData from the lenalidomide plus dexamethasone arm of the ASPIRE trial was used to estimate transition rates for an STM. The model accounted for the competing risk between the progression and preprogression death events and included an explicit structural link between the time to progression and subsequent death. The modeled transition rates were used to simulate individual disease trajectories in a discrete event simulation framework, based on which progression-free survival and overall survival over a 30-year time horizon were estimated. Survival predictions were compared with the observed trial data, matched external data, and estimates obtained from a more conventional partitioned survival analysis approach.ResultsThe rates of progression and preprogression death were modeled using piecewise exponential functions. The rate of postprogression mortality was modeled using an exponential function accounting for the nonlinear effect of the time to progression. The STM provided survival estimates that closely fitted the trial data and gave more plausible long-term survival predictions than the best-fitting Weibull model applied in a partitioned survival analysis.ConclusionsThe fit of the STM suggested that the modeled transition rates accurately captured the underlying disease process over the modeled time horizon. The considerations of this study may apply to other settings and facilitate a wider use of STMs in oncology.     

Survival extrapolation in the presence of cause specific hazards

Health economic evaluations require estimates of expected survival from patients receiving different interventions, often over a lifetime. However, data on the patients of interest are typically only available for a much shorter follow‐up time, from randomised trials or cohorts. Previous work showed how to use general population mortality to improve extrapolations of the short‐term data, assuming a constant additive or multiplicative effect on the hazards for all‐cause mortality for study patients relative to the general population. A more plausible assumption may be a constant effect on the hazard for the specific cause of death targeted by the treatments. To address this problem, we use independent parametric survival models for cause‐specific mortality among the general population. Because causes of death are unobserved for the patients of interest, a polyhazard model is used to express their all‐cause mortality as a sum of latent cause‐specific hazards. Assuming proportional cause‐specific hazards between the general and study populations then allows us to extrapolate mortality of the patients of interest to the long term. A Bayesian framework is used to jointly model all sources of data. By simulation, we show that ignoring cause‐specific hazards leads to biased estimates of mean survival when the proportion of deaths due to the cause of interest changes through time. The methods are applied to an evaluation of implantable cardioverter defibrillators for the prevention of sudden cardiac death among patients with cardiac arrhythmia. After accounting for cause‐specific mortality, substantial differences are seen in estimates of life years gained from implantable cardioverter defibrillators. © 2014 The Authors Statistics in Medicine Published by John Wiley & Sons Ltd.     

Estimation of the Width of Uncertainty in Care Consumption and Costs When Using Common Data Collection Tools in Economic Evaluations: A Benchmark for Sensitivity Analyses

ObjectivesThis study aimed to evaluate the uncertainty related to the use of common collection tools to assess costs in economic evaluations compared with an exhaustive administrative database.MethodsA pragmatic study was performed using preexisting cost-effectiveness studies. Patients were probabilistically matched with themselves in the French National Health Data System (Système National des Données de Santé [SNDS]), and all their reimbursed hospital and ambulatory care data during the study were extracted. Outcomes included the ratio of the number of each type of resources consumed using trial data (case report forms for ambulatory care and local hospital data for hospital care) versus the SNDS and the ratio of corresponding costs. Mean ratios and 95% confidence intervals (CIs) were calculated using bootstrapping. The impact of the collection tool on the result of the economic evaluation was calculated with the difference in costs between the 2 treatment arms with both collection methods.ResultsFive cost-effectiveness studies were included in the analysis. A total of 397 patients had the SNDS hospital data, and 321 had ambulatory care data. Common collection tools underestimated hospital admissions by 13% (95% CI 8-20), corresponding costs by 5% (95% CI 2-14), and ambulatory acts by 41% (95% CI 33-51), with large variations in costs depending on the study. There was no change in the economic conclusion in any study.ConclusionsThe use of common collection tools underestimates healthcare resource consumption and its associated costs, particularly for ambulatory care. Our results could provide useful evidence-based estimates to inform sensitivity analyses’ parameters in future cost-effectiveness analyses.     

Assessing the Impact of Censoring of Costs and Effects on Health-Care Decision-Making: an Example Using the Atrial Fibrillation Follow-up Investigation of Rhythm Management (AFFIRM) Study

Objectives:  Losses to follow-up and administrative censoring can cloud the interpretation of trial-based economic evaluations. A number of investigators have examined the impact of different levels of adjustment for censoring, including nonadjustment, adjustment of effects only, and adjustment for both costs and effects. Nevertheless, there is a lack of research on the impact of censoring on decision-making. The objective of this study was to estimate the impact of adjustment for censoring on the interpretation of cost-effectiveness results and expected value of perfect information (EVPI), using a trial-based analysis that compared rate- and rhythm-control treatments for persons with atrial fibrillation.
Methods:  Three different levels of adjustment for censoring were examined: no censoring of cost and effects, censoring of effects only, and censoring of both costs and effects. In each case, bootstrapping was used to estimate the uncertainty incosts and effects, and the EVPI was calculated to determine the potential worth of further research.
Results:  Censoring did not impact the adoption decision. Nevertheless, this was not the case for the decision uncertainty or the EVPI. For a threshold of $50,000 per life-year, the EVPI varied between $626,000 (partial censoring) to $117 million (full censoring) for the eligible US population.
Conclusions:  The level of adjustment for censoring in trial-based cost-effectiveness analyses can impact on the decisions to fund a new technology and to devote resources for further research. Only when censoring is taken into account for both costs and effects are these decisions appropriately addressed.     

External Validation of Health Economic Decision Models for Chronic Obstructive Pulmonary Disease (COPD): Report of the Third COPD Modeling Meeting

Objectives

To validate outcomes of presently available chronic obstructive pulmonary disease (COPD) cost-effectiveness models against results of two large COPD trials—the 3-year TOwards a Revolution in COPD Health (TORCH) trial and the 4-year Understanding Potential Long-term Impacts on Function with Tiotropium (UPLIFT) trial.

体外自动除颤仪在急性心肌梗死心脏骤停急救中应用价值分析

目的:探索体外自动除颤仪与人工除颤在急性心肌梗死心脏骤停急救中应用价值分析.方法:选取2018年1月~2019年11月在本院进行急性心肌梗死心脏骤停急救的95例患者.急性心肌梗死心脏骤停患者被归类为体外自动除颤仪组(观察组,n=51):采用体外自动除颤仪方案治疗;人工除颤组(对照组,n=44):采取人工除颤方案治疗.比较两组患者的除颤成功率及存活率.结果:①经过治疗发现体外自动除颤仪方案患者除颤成功率对比人工除颤方案具有明显优势,P<0.05.②经过治疗发现体外自动除颤仪方案患者存活率对比人工除颤方案具有明显优势,P<0.05.结论:采用体外自动除颤仪方案治疗患者的效果确切,对比除颤成功率及存活率等方面有明显的优势,关于治疗后的长期效果该研究未进行深入探讨,存在一些不足,有待进一步研究.     

健康管理生活方式问卷设计最小数据集研究

目的确定健康管理生活方式问卷的最小数据集,利用最少数据元规范化表达健康体检生活方式问卷信息。方法采用生活方式问卷概念框架基础上自定义的生活方式问卷,对某三甲医院体检中心2012年某一周的体检人群进行调查,主要调查内容是生活方式信息。采用问卷的信度和效度分析,评价问卷的内容和结构。结果问卷信度评价结果显示,问卷各维度内部的相关系数远大于外部,其中整体Cronbach’α系数0.6,说明问卷的条目结构合理。验证性因子分析结果显示问卷结构与模型的拟合程度较好,GIF和CIF分别是0.83和0.74。问卷各维度条目的因子载荷均大于0.4,部分维度之间的相关系数也大于0.4。结论采用验证性因子分析确定的健康管理生活方式问卷最小数据集为健康管理生活方式问卷的制定提供了参考和标准依据,有助于数据的收集、管理和分析,但最小数据集还有待听取专家提议和进一步扩大样本量来验证,数据元标准化工作还有待进一步完善。     

Systematic Screening for Chlamydia trachomatis: Estimating Cost-Effectiveness Using Dynamic Modeling and Dutch Data

OBJECTIVE: To estimate the cost-effectiveness of a systematic one-off Chlamydia trachomatis (CT) screening program including partner treatment for Dutch young adults. METHODS: Data on infection prevalence, participation rates, and sexual behavior were obtained from a large pilot study conducted in The Netherlands. Opposite to almost all previous economic evaluations of CT screening, we developed a dynamic Susceptible-Infected-Susceptible (SIS) model to estimate the impact of the screening program on the incidence and prevalence of CT in the population. SIS models are widely used in epidemiology of infectious diseases, for modeling the transmission dynamics over time. Subsequently, a predictive decision model was used to calculate the complications averted by the screening program. Cost-effectiveness was expressed as the net costs per major outcome averted (MOA) and was estimated in the baseline analysis and in sensitivity analysis. RESULTS: The overall prevalence decreased from 1.79% to 1.05% as a result of the screening program directed at both men and women. The program costs were mainly offset by the averted costs, although not fully. Resulting net costs per MOA were 373 euro sin the baseline analysis. Sensitivity analysis showed that partner treatment and sending a reminder are important aspects improving cost-effectiveness. Additionally, restricting the screening to women only was estimated to save costs. CONCLUSIONS: Our cost-effectiveness analysis shows that the Dutch society has net to pay for the prevention of CT-complications through screening young men and women. One could argue although that 373 euros per MOA presents a reasonable cost. A screening program consisting of screening women only should always be adopted from a pharmacoeconomic point of view. Our dynamic approach appreciates better the specific characteristics of an infectious disease, such as CT.     

The ICER Value Framework: Integrating Cost Effectiveness and Affordability in the Assessment of Health Care Value

What should be the relationship between the concepts of cost effectiveness and affordability in value assessments for health care interventions? This question has received greater attention in recent years given increasing financial pressures on health systems, leading to different views on how assessment reports and decision-making processes can provide the best structure for considering both elements. In the United States, the advent of explicit value frameworks to guide drug assessments has also focused attention on this issue, driven in part by the prominent inclusion of affordability within the value framework used to guide reports from the Institute for Clinical and Economic Review. After providing a formal definition of affordability for health care systems, this article argues that, even after using empirical estimates of true health system opportunity cost, cost-effectiveness thresholds cannot by themselves be set in a way that subsumes questions about short-term affordability. The article then presents an analysis of different approaches to integrating cost effectiveness and budget impact assessments within information to guide decision making. The evolution and experience with the Institute for Clinical and Economic Review value framework are highlighted, providing lessons learned and guiding principles for future efforts to bring measures of affordability within the scope of value assessment.     

Estimating the Cost-Effectiveness of Fluticasone Propionate for Treating Chronic Obstructive Pulmonary Disease in the Presence of Missing Data

OBJECTIVES: To explore the cost-effectiveness of fluticasone propionate (FP) for the treatment of chronic obstructive pulmonary disease (COPD), we estimated costs and quality-adjusted life-years (QALYs) over 3 years, based on an economic appraisal of a previously reported clinical trial (Inhaled Steroids in Obstructive Lung Disease in Europe [ISOLDE]). METHODS: Seven hundred forty-two patients enrolled in the ISOLDE trial who received either FP or placebo had data available on health-care costs and quality of life over the period of the study. The SF-36-based utility scores for quality of life were used to calculate QALYs. A combined imputation and bootstrapping procedure was employed to handle missing data and to estimate statistical uncertainty in the estimated cumulative costs and QALYs over the study period. The imputation approach was based on propensity scoring and nesting this approach within the bootstrap ensured that multiple imputations were performed such that statistical estimates included imputation uncertainty. RESULTS: Complete data were available on mortality within the follow-up period of the study and a nonsignificant trend toward improved survival of 0.06 (95% confidence interval [CI]-0.01 to 0.15) life-years was observed. In an analysis based on a propensity scoring approach to missing data we estimated the incremental costs of FP versus placebo to be 1021 sterling pound(95% CI 619-1338 sterling pound) with an additional effect of 0.11 QALYs (CI 0.04-0.20). Cost-effectiveness estimates for the within-trial period of 17,700 sterling pound per life-year gained (6900 sterling pound to infinity) and 9500 sterling pound per QALY gained (CI 4300-26,500 sterling pound) were generated that include uncertainty due to the imputation process. An alternative imputation approach did not materially affect these estimates. CONCLUSIONS: Previous analyses of the ISOLDE study showed significant improvement on disease-specific health status measures and a trend toward a survival advantage for treatment with FP. This analysis shows that joint considerations of quality of life and survival result in a substantial increase in QALYs favoring treatment with FP. Based on these data, the inhaled corticosteroid FP appears cost-effective for the treatment of COPD. Confirmation or refutation of this result may be achieved once the Towards a Revolution in COPD Health (TORCH) study reports, a large randomized controlled trial powered to detect mortality changes associated with the use of FP alone, or in combination with salmeterol, which is also collecting resource use and utility data suitable for estimating cost-effectiveness.     

Informing Global Cost-Effectiveness Thresholds Using Country Investment Decisions: Human Papillomavirus Vaccine Introductions in 2006-2018

ObjectivesCost-effectiveness analysis can guide decision making about health interventions, but the appropriate cost-effectiveness threshold to use is unclear in most countries. The World Health Organization (WHO) recommends vaccinating girls 9 to 14 years against human papillomavirus (HPV), but over half the world’s countries have not introduced it. This study aimed to investigate whether country-level decisions about HPV vaccine introduction are consistent with a particular cost-effectiveness threshold, and to estimate what that threshold may be.MethodsThe cost-effectiveness of vaccinating 12-year-old girls was estimated in 179 countries using the Papillomavirus Rapid Interface for Modelling and Economics (PRIME) model, together with vaccine price data from World Health Organization’s Market Information for Access to Vaccines database. In each year from 2006 to 2018, countries were categorized based on (1) whether they had introduced HPV vaccination, and (2) whether the incremental cost-effectiveness ratio for HPV vaccine introduction fell below a certain cost-effectiveness threshold.ResultsA cost-effectiveness threshold of 60% to 65% of GDP per capita has the best ability to discriminate countries that introduced vaccination, with a diagnostic odds ratio of about 7. For low-income countries the optimal threshold was lower, at 30% to 40% of GDP per capita.ConclusionsA cost-effectiveness threshold has some ability to discriminate between HPV vaccine introducer and non-introducer countries, although the average threshold is below the widely used threshold of 1 GDP per capita. These results help explain the current pattern of HPV vaccine use globally. They also inform the extent to which cost-effectiveness thresholds proposed in the literature reflect countries’ actual investment decisions.