Joint Distribution Approaches to Quantifying Benefit and Risk in Clinical Trials
*Michele Lee Shaffer, Penn State College of Medicine
Kristi Watterberg, University of New Mexico
Keywords: Benefit-risk ratio, efficacy, adverse event, Bayesian, incremental cost-effectiveness ratio, incremental net health benefit
The benefit-risk ratio has been proposed to measure the tradeoff between benefits and risks of two therapies. Small sample sizes or expected differences in benefit or risk can lead to problematic solutions for confidence intervals. Alternatively, using the joint distribution of benefit and risk, uncertainty in benefits and risks can be represented by confidence ellipses. To quantify the probability of falling into regions of interest, the proportion of bootstrap estimates or posterior probabilities can be computed. Bayesian methods provide a flexible framework in which to summarize the joint distribution of benefit and risk. One can conduct benefit-risk analyses similar to the incremental net health benefit analyses used for cost-effectiveness research. This approach is based on linear combinations of benefit and risk and avoids many of the inferential problems associated with ratios.