A counterfactual p-value approach for benefit-risk assessment in clinical trials.
Clinical trials generally allow various efficacy and safety outcomes to be collected for health interventions. Benefit-risk assessment is an important issue when evaluating a new drug. Currently, there is a lack of standardized and validated benefit-risk assessment approaches in drug development due to various challenges. To quantify benefits and risks, we propose a counterfactual p-value (CP) approach. Our approach considers a spectrum of weights for weighting benefit-risk values and computes the extreme probabilities of observing the weighted benefit-risk value in one treatment group as if patients were treated in the other treatment group. The proposed approach is applicable to single benefit and single risk outcome as well as multiple benefit and risk outcomes assessment. In addition, the prior information in the weight schemes relevant to the importance of outcomes can be incorporated in the approach. The proposed CPs plot is intuitive with a visualized weight pattern. The average area under CP and preferred probability over time are used for overall treatment comparison and a bootstrap approach is applied for statistical inference. We assess the proposed approach using simulated data with multiple efficacy and safety endpoints and compare its performance with a stochastic multi-criteria acceptability analysis approach.
a Department of Biostatistics , University of North Carolina , Chapel Hill , North Carolina , USA.
Donglin
D
Zeng
Ming-Hui
MH
Chen
Joseph G
JG
Ibrahim
Rachel
R
Wei
Beiying
B
Ding
Chunlei
C
Ke
Qi
Q
Jiang
D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D003661:Decision Support Techniques; D055808:Drug Discovery / Q000706:statistics & numerical data*; D023381:Endpoint Determination; D018570:Risk Assessment / Q000706:statistics & numerical data*; D016019:Survival Analysis
Area under the CP-region; Benefit-risk assessment; Counterfactual p-value; Preferred probability; Prior distribution