ABSTRACT
Clinical trials evaluate the safety and efficacy of treatments for specific diseases. Ensuring these studies are well-powered is crucial for identifying superior treatments. With the rise of personalized medicine, treatment efficacy may vary based on biomarker profiles. However, researchers often lack prior knowledge about which biomarkers are linked to varied treatment effects. Fixed or response-adaptive designs may not sufficiently account for heterogeneous patient characteristics, such as genetic diversity, potentially reducing the chance of selecting the optimal treatment for individuals. Recent advances in Bayesian nonparametric modeling pave the way for innovative trial designs that not only maintain robust power but also offer the flexibility to identify subgroups deriving greater benefits from specific treatments. Building on this inspiration, we introduce a Bayesian adaptive design for multi-arm trials focusing on time-to-event endpoints. We introduce a covariate-adjusted response adaptive randomization, updating treatment allocation probabilities grounded on causal effect estimates using a random intercept accelerated failure time BART model. After the trial concludes, we suggest employing a multi-response decision tree to pinpoint subgroups with varying treatment impacts. The performance of our design is then assessed via comprehensive simulations.
Subject(s)
Bayes Theorem , Machine Learning , Randomized Controlled Trials as Topic , Research Design , Humans , Randomized Controlled Trials as Topic/methods , Models, Statistical , Decision Trees , BiomarkersABSTRACT
Oral tranexamic acid (TXA) is a relatively new treatment option for melasma. It is thought to reduce hyperpigmentation through inhibition of the plasminogen/plasmin pathway with resulting decreases in epidermal melanocyte tyrosinase activity, inflammatory mediators, dermal neovascularization, and mast cell numbers.