Abstract
Personalized medicine aims at identifying best treatments for a patient with given characteristics. It has been shown in the literature that these methods can lead to great improvements in medicine compared to traditional methods prescribing the same treatment to all patients. Subgroup identification is a branch of personalized medicine, which aims at finding subgroups of the patients with similar characteristics for which some of the investigated treatments have a better effect than the other treatments. A number of approaches based on decision trees have been proposed to identify such subgroups, but most of them focus on two‐arm trials (control/treatment) while a few methods consider quantitative treatments (defined by the dose). However, no subgroup identification method exists that can predict the best treatments in a scenario with a categorical set of treatments. We propose a novel method for subgroup identification in categorical treatment scenarios. This method outputs a decision tree showing the probabilities of a given treatment being the best for a given group of patients as well as labels showing the possible best treatments. The method is implemented in an R package psica available on CRAN. In addition to a simulation study, we present an analysis of a community‐based nutrition intervention trial that justifies the validity of our method.
Highlights
It is very common that randomized trials are performed to investigate the efficiency of a new treatment
A new treatment is compared to a control treatment, and if the new treatment is shown to be more efficient than the control it is suggested to be used on a population-wide level
We introduce Probabilistic Subgroup Identification for CAtegorical (PSICA) trees
Summary
It is very common that randomized trials are performed to investigate the efficiency of a new treatment. This typically reduces to a high-dimensional regression problem with binary input variables indicating the absence or presence of corresponding genetic biomarkers Another important category of personalized medicine is subgroup identification, a comprehensive survey of methods from this category is available in the literature.[4] The methods from this category identify subgroups of patients, which benefit from the same treatments, and this identification can be based on the characteristics of various natures (binary, categorical, real valued). Our method is designed for randomized controlled trials and continuous outcome variables We believe that it is of great importance for a subgroup identification method to provide statistical guarantees in the form of the probabilities of a treatment being the best for a given subgroup and, when data are not sufficient for a reliable decision, to state that there is no statistical guarantee that one of the treatments is more appropriate than the others.
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