Causal Responder Detection

Personalized medicine is expected to advance healthcare in the near future [Vicente et al., 2020]. In contrast to a one-size-fits-all approach, personalized medicine advocates for treatments tailored to individual patients based on their clinical characteristics. Responder analysis in clinical trials is a method used to evaluate the effectiveness of a treatment by identifying and analyzing the subset of participants who respond significantly to the treatment [Henschke et al., 2014]. This approach contrasts with the traditional method of evaluating average effects across all participants, which can sometimes obscure the benefits seen in a particular group of responders [Guyatt et al., 1998]. This approach is particularly important in trials with heterogeneous treatment effects, where understanding individual responses can lead to more effective therapies. Responder analysis is a common practice in clinical trials [Moore et al., 2010, Straube et al., 2010, Chuang et al., 2022].