Dataset Information

A Bayesian Framework for Patient-Level Partitioned Survival Cost-Utility Analysis.

ABSTRACT: Patient-level health economic data collected alongside clinical trials are an important component of the process of technology appraisal. For end-of-life treatments, the modeling of cost-effectiveness data may involve some form of partitioned survival analysis, in which measures of quality of life and survival for pre- and postprogression periods are combined to generate aggregate measures of clinical benefits (e.g., quality-adjusted survival). In addition, resource use data are often collected and costs are calculated for each type of health service (e.g., treatment, hospital, or adverse events costs). A critical problem in these analyses is that effectiveness and cost data present some complexities, such as nonnormality, spikes, and missingness, which should be addressed using appropriate methods to avoid biased results. This article proposes a general Bayesian framework that takes into account the complexities of trial-based partitioned survival cost-utility data to provide more adequate evidence for policy makers. Our approach is motivated by, and applied to, a working example based on data from a trial assessing the cost-effectiveness of a new treatment for patients with advanced non-small-cell lung cancer.[Box: see text].

SUBMITTER: Gabrio A

PROVIDER: S-EPMC8488644 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Publications

A Bayesian Framework for Patient-Level Partitioned Survival Cost-Utility Analysis.

Gabrio Andrea A

Medical decision making : an international journal of the Society for Medical Decision Making 20210519 8

Patient-level health economic data collected alongside clinical trials are an important component of the process of technology appraisal. For end-of-life treatments, the modeling of cost-effectiveness data may involve some form of partitioned survival analysis, in which measures of quality of life and survival for pre- and postprogression periods are combined to generate aggregate measures of clinical benefits (e.g., quality-adjusted survival). In addition, resource use data are often collected ...[more]

PMID: 34009065

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Project description:A preliminary cost-utility analysis (CUA) of prosthetic care innovations can provide timely information during the early stage of product development and clinical usage. Concepts of preliminary CUAs are emerging. However, several obstacles must be overcome before these analyses are performed routinely. Disparities of methods and high uncertainty make the outcomes of usual preliminary CUAs challenging to interpret, appraise and share. These shortcomings create opportunities for a basic framework of preliminary CUAs. First, I introduced a basic framework of a preliminary CUA built around a series of constructs and hands-on recommendations. Then, I appraised this framework considering the strengths and weaknesses, barriers and facilitators, and return on investment. The design of the basic framework was determined through the review of health economic and prosthetic-specific literature. A preliminary CUA comparing the costs and utilities between usual intervention and an innovation could be achieved through a 15-step iterative process focusing on feasibility, constructs, analysis, and interpretation of outcomes. This CUA provides sufficient evidence to identify knowledge gaps and improvement areas, educate about the design of subsequent full CUAs, and obtain fast-track approval from governing bodies. Like previous CUAs, the main limitations were inherent to the constructs (e.g., narrow perspective, plausible scenarios, mid-term time horizon, substantial assumptions, data mismatch, high uncertainty). Key facilitators potentially transferable across preliminary CUAs of prosthetic care innovations included choosing abided constructs, capitalizing on prior schedules of expenses, and benchmarking baseline or incremental utilities. This new approach with preliminary CUA can simplify the selection of methods, standardize outcomes, ease comparisons between innovations, and streamline pathways for adoption. Further collegial efforts toward validating standard preliminary CUAs will facilitate access to economic prosthetic care innovations, improving the lives of individuals suffering from limb loss worldwide.

Project description:Event-time or continuous-time statistical approaches have been applied throughout the biostatistical literature and have led to numerous scientific advances. However, these techniques have traditionally relied on knowing failure times. This has limited application of these analyses, particularly, within the ecological field where fates of marked animals may be unknown. To address these limitations, we developed an integrated approach within a Bayesian framework to estimate hazard rates in the face of unknown fates. We combine failure/survival times from individuals whose fates are known and times of which are interval-censored with information from those whose fates are unknown, and model the process of detecting animals with unknown fates. This provides the foundation for our integrated model and permits necessary parameter estimation. We provide the Bayesian model, its derivation, and use simulation techniques to investigate the properties and performance of our approach under several scenarios. Lastly, we apply our estimation technique using a piece-wise constant hazard function to investigate the effects of year, age, chick size and sex, sex of the tending adult, and nesting habitat on mortality hazard rates of the endangered mountain plover (Charadrius montanus) chicks. Traditional models were inappropriate for this analysis because fates of some individual chicks were unknown due to failed radio transmitters. Simulations revealed biases of posterior mean estimates were minimal (≤ 4.95%), and posterior distributions behaved as expected with RMSE of the estimates decreasing as sample sizes, detection probability, and survival increased. We determined mortality hazard rates for plover chicks were highest at <5 days old and were lower for chicks with larger birth weights and/or whose nest was within agricultural habitats. Based on its performance, our approach greatly expands the range of problems for which event-time analyses can be used by eliminating the need for having completely known fate data.

Project description:BackgroundStudies have shown that centralising surgical treatment for some cancers can improve patient outcomes, but there is limited evidence of the impact on costs or health-related quality of life.ObjectivesWe report the results of a cost-utility analysis of the RESPECT-21 study using difference-in-differences, which investigated the reconfiguration of specialist surgery services for four cancers in an area of London, compared to the Rest of England (ROE).MethodsElectronic health records data were obtained from the National Cancer Registration and Analysis Service for patients diagnosed with one of the four cancers of interest between 2012 and 2017. The analysis for each tumour type used a short-term decision tree followed by a 10-year Markov model with 6-monthly cycles. Costs were calculated by applying National Health Service (NHS) Reference Costs to patient-level hospital resource use and supplemented with published data. Cancer-specific preference-based health-related quality-of-life values were obtained from the literature to calculate quality-adjusted life-years (QALYs). Total costs and QALYs were calculated before and after the reconfiguration, in the London Cancer (LC) area and in ROE, and probabilistic sensitivity analysis was performed to illustrate the uncertainty in the results.ResultsAt a threshold of £30,000/QALY gained, LC reconfiguration of prostate cancer surgery services had a 79% probability of having been cost-effective compared to non-reconfigured services using difference-in-differences. The oesophago-gastric, bladder and renal reconfigurations had probabilities of 62%, 49% and 12%, respectively, of being cost-effective at the same threshold. Costs and QALYs per surgical patient increased over time for all cancers across both regions to varying degrees. Bladder cancer surgery had the smallest patient numbers and changes in costs, and QALYs were not significant. The largest improvement in outcomes was in renal cancer surgery in ROE, making the relative renal improvements in LC appear modest, and the probability of the LC reconfiguration having been cost-effective low.ConclusionsProstate cancer reconfigurations had the highest probability of being cost-effective. It is not clear, however, whether the prostate results can be considered in isolation, given the reconfigurations occurred simultaneously with other system changes, and healthcare delivery in the NHS is highly networked and collaborative. Routine collection of quality-of-life measures such as the EQ-5D-5L would have improved the analysis.

Dataset Information

A Bayesian Framework for Patient-Level Partitioned Survival Cost-Utility Analysis.

Publications

A Bayesian Framework for Patient-Level Partitioned Survival Cost-Utility Analysis.

Similar Datasets

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