A Bivariate Regression-Based Cost-Effectiveness Analysis

Arsham, Aryana; Bebu, Ionut; Mathew, Thomas

doi:10.1007/s42519-022-00255-x

Cited by 1 publication

(1 citation statement)

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“…Additionally, investigation into sample size determination for the methods presented would be useful for many applications. Adaptation of the CEP metrics for inclusion of patient‐level characteristics may be useful to policy makers in resource allocation for particular groups of patients, such analysis is developed in Arsham et al 18 for the CEP in the presence of a single effectiveness outcome. Lastly, it may be possible to generalize the net benefit separation criterion introduced in Spieker et al 13 for multiple effectiveness measures.…”

Section: Discussionmentioning

confidence: 99%

Cost‐effectiveness analysis under multiple effectiveness outcomes: A probabilistic approach

Arsham

Bebu

Mathew

2023

Statistics in Medicine

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Probability based criteria are proposed for the assessment of cost‐effectiveness of a new treatment compared to a standard treatment when there are multiple effectiveness measures. Depending on the preferences of a policy maker, there are several options to define such criteria. Two such metrics are investigated in detail. One metric is defined as the conditional probability that a new treatment is more effective with respect to the multiple effectiveness measures for patients having lower costs under the new treatment. A second metric is defined as the conditional probability that a new treatment is less costly for patients having greater health benefits under the new treatment. The metrics offer considerable flexibility to a policy maker as thresholds for cost and effectiveness can be incorporated into the metrics. Parametric confidence limits are developed using a percentile bootstrap approach assuming multivariate normality for the joint distribution of the log(cost) and effectiveness measures. A non‐parametric estimation procedure is also developed using the theory of U‐statistics. Numerical results indicate that the proposed confidence limits accurately maintain coverage probabilities. The methodologies are illustrated using a study on the treatment of type two diabetes. Code implementing the proposed methods are provided in the supporting information.

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Section: Discussionmentioning

confidence: 99%