Abstract
A plethora of disparate statistical methods have been proposed for subgroup identification to help tailor treatment decisions for patients. However a majority of them do not have corresponding R packages and the few that do pertain to particular statistical methods or provide little means of evaluating whether meaningful subgroups have been found. Recently, the work of Chen, Tian, Cai, and Yu (2017) unified many of these subgroup identification methods into one general, consistent framework. The goal of the personalized package is to provide a corresponding unified software framework for subgroup identification analyses that provides not only estimation of subgroups, but evaluation of treatment effects within estimated subgroups. The personalized package allows for a variety of subgroup identification methods for many types of outcomes commonly encountered in medical settings. The package is built to incorporate the entire subgroup identification analysis pipeline including propensity score diagnostics, subgroup estimation, analysis of the treatment effects within subgroups, and evaluation of identified subgroups. In this framework, different methods can be accessed with little change in the analysis code. Similarly, new methods can easily be incorporated into the package. Besides familiar statistical models, the package also allows flexible machine learning tools to be leveraged in subgroup identification. Further estimation improvements can be obtained via efficiency augmentation.
Highlights
Many studies of medical interventions, especially clinical trials, often focus on population average treatment effects
Optimal treatment allocation can be thought of as a subgroup identification task, where subgroups are personalized: A Package for Subgroup Identification determined based on the heterogeneity of treatment effect
Heterogeneity of treatment effect can be characterized by the interaction of the treatment with patient characteristics
Summary
Many studies of medical interventions, especially clinical trials, often focus on population average treatment effects. The goal in subgroup identification is to characterize and estimate these interactions in order to construct an optimal mapping from patient characteristics to a treatment assignment. This mapping is called an individualized treatment rule (ITR). The overall patient outcome is impacted by both the main effects of patient characteristics and the treatment-covariate interactions and many approaches, such as Qian and Murphy (2011), focus on modeling this full relationship to estimate ITRs. In their work, Qian and Murphy (2011) show robustness properties to model misspecification under certain conditions. There has been much focus on estimation of subgroups based on patient characteristics, yet not enough emphasis on evaluation of the treatment effects within the resulting estimated subgroups, which is an important but challenging aspect of any subgroup analysis
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