Ilya Lipkovich

TL;DR: This work develops a novel recursive partitioning method for identifying subgroups of subjects with enhanced treatment effects based on a differential effect search algorithm and provides guidance on key topics of interest that include generating multiple promising subgroups using different splitting criteria, choosing optimal values of complexity parameters via cross‐validation, and addressing Type I error rate inflation inherent in data mining applications using a resampling‐based method.

TL;DR: This tutorial focuses on a general class of problems arising in data-driven subgroup analysis, namely, identification of biomarkers with strong predictive properties and patient subgroups with desirable characteristics such as improved benefit and/or safety.

TL;DR: In this paper, the authors describe a set of macros that may be used to draw a biplot display based on results from principal components analysis, correspondence analysis, canonical discriminant analysis, metric multidimensional scaling, redundancy analysis or canonical correspondence analysis.

TL;DR: A novel method called multiple imputation missingness pattern (MIMP) is proposed and compared with the naive estimator (ignoring propensity score) and three commonly used methods of handling missing covariates in propensity score-based estimation under different mechanisms of missing data and degree of correlation among covariates.

TL;DR: The main finding is that the adaptive SIDEScreen method is a more flexible biomarker discovery tool than SIDES and it better handles multiplicity in complex subgroup search problems.