Dataset Information

Better informing decision making with multiple outcomes cost-effectiveness analysis under uncertainty in cost-disutility space.

ABSTRACT:

Introduction

Comparing multiple, diverse outcomes with cost-effectiveness analysis (CEA) is important, yet challenging in areas like palliative care where domains are unamenable to integration with survival. Generic multi-attribute utility values exclude important domains and non-health outcomes, while partial analyses-where outcomes are considered separately, with their joint relationship under uncertainty ignored-lead to incorrect inference regarding preferred strategies.

Objective

The objective of this paper is to consider whether such decision making can be better informed with alternative presentation and summary measures, extending methods previously shown to have advantages in multiple strategy comparison.

Methods

Multiple outcomes CEA of a home-based palliative care model (PEACH) relative to usual care is undertaken in cost disutility (CDU) space and compared with analysis on the cost-effectiveness plane. Summary measures developed for comparing strategies across potential threshold values for multiple outcomes include: expected net loss (ENL) planes quantifying differences in expected net benefit; the ENL contour identifying preferred strategies minimising ENL and their expected value of perfect information; and cost-effectiveness acceptability planes showing probability of strategies minimising ENL.

Results

Conventional analysis suggests PEACH is cost-effective when the threshold value per additional day at home (?1) exceeds $1,068 or dominated by usual care when only the proportion of home deaths is considered. In contrast, neither alternative dominate in CDU space where cost and outcomes are jointly considered, with the optimal strategy depending on threshold values. For example, PEACH minimises ENL when ?1=$2,000 and ?2=$2,000 (threshold value for dying at home), with a 51.6% chance of PEACH being cost-effective.

Conclusion

Comparison in CDU space and associated summary measures have distinct advantages to multiple domain comparisons, aiding transparent and robust joint comparison of costs and multiple effects under uncertainty across potential threshold values for effect, better informing net benefit assessment and related reimbursement and research decisions.

SUBMITTER: McCaffrey N

PROVIDER: S-EPMC4353730 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Better informing decision making with multiple outcomes cost-effectiveness analysis under uncertainty in cost-disutility space.

McCaffrey Nikki N Agar Meera M Harlum Janeane J Karnon Jonathon J Currow David D Eckermann Simon S

PloS one 20150309 3

<h4>Introduction</h4>Comparing multiple, diverse outcomes with cost-effectiveness analysis (CEA) is important, yet challenging in areas like palliative care where domains are unamenable to integration with survival. Generic multi-attribute utility values exclude important domains and non-health outcomes, while partial analyses-where outcomes are considered separately, with their joint relationship under uncertainty ignored-lead to incorrect inference regarding preferred strategies.<h4>Objective< ...[more]

PMID: 25751629

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Project description:BackgroundStatistical inference based on small datasets, commonly found in precision oncology, is subject to low power and high uncertainty. In these settings, drawing strong conclusions about future research utility is difficult when using standard inferential measures. It is therefore important to better quantify the uncertainty associated with both significant and non-significant results based on small sample sizes.MethodsWe developed a new method, Bayesian Additional Evidence (BAE), that determines (1) how much additional supportive evidence is needed for a non-significant result to reach Bayesian posterior credibility, or (2) how much additional opposing evidence is needed to render a significant result non-credible. Although based in Bayesian analysis, a prior distribution is not needed; instead, the tipping point output is compared to reasonable effect ranges to draw conclusions. We demonstrate our approach in a comparative effectiveness analysis comparing two treatments in a real world biomarker-defined cohort, and provide guidelines for how to apply BAE in practice.ResultsOur initial comparative effectiveness analysis results in a hazard ratio of 0.31 with 95% confidence interval (0.09, 1.1). Applying BAE to this result yields a tipping point of 0.54; thus, an observed hazard ratio of 0.54 or smaller in a replication study would result in posterior credibility for the treatment association. Given that effect sizes in this range are not extreme, and that supportive evidence exists from a similar published study, we conclude that this problem is worthy of further research.ConclusionsOur proposed method provides a useful framework for interpreting analytic results from small datasets. This can assist researchers in deciding how to interpret and continue their investigations based on an initial analysis that has high uncertainty. Although we illustrated its use in estimating parameters based on time-to-event outcomes, BAE easily applies to any normally-distributed estimator, such as those used for analyzing binary or continuous outcomes.

Dataset Information

Better informing decision making with multiple outcomes cost-effectiveness analysis under uncertainty in cost-disutility space.

Introduction

Objective

Methods

Results

Conclusion

Publications

Better informing decision making with multiple outcomes cost-effectiveness analysis under uncertainty in cost-disutility space.

Similar Datasets

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