Sequential Advantage Selection for Optimal Treatment Regimes
This work addresses the problem of improving treatment decisions in clinical trials or observational studies by better identifying variables that interact with treatments, though it is incremental as it builds on prior S-score methods.
The authors tackled the problem of variable selection for optimal treatment regimes, where existing methods often miss variables critical for decision-making due to poor predictive power, by developing a sequential advantage selection method based on a modified S-score that selects qualitatively interacted variables sequentially, resulting in more comprehensive and reliable treatment regimes, with simulation results showing good performance in practical settings and application to a clinical trial for depression.
Variable selection for optimal treatment regime in a clinical trial or an observational study is getting more attention. Most existing variable selection techniques focused on selecting variables that are important for prediction, therefore some variables that are poor in prediction but are critical for decision-making may be ignored. A qualitative interaction of a variable with treatment arises when treatment effect changes direction as the value of this variable varies. The qualitative interaction indicates the importance of this variable for decision-making. Gunter et al. (2011) proposed S-score which characterizes the magnitude of qualitative interaction of each variable with treatment individually. In this article, we developed a sequential advantage selection method based on the modified S-score. Our method selects qualitatively interacted variables sequentially, and hence excludes marginally important but jointly unimportant variables {or vice versa}. The optimal treatment regime based on variables selected via joint model is more comprehensive and reliable. With the proposed stopping criteria, our method can handle a large amount of covariates even if sample size is small. Simulation results show our method performs well in practical settings. We further applied our method to data from a clinical trial for depression.