Subpopulation Treatment Effect Pattern Plot (STEPP) Methods with R and Stata

Sergio Venturini*, M. Bonetti, A. A. Lazar, B. F. Cole, X. Wang, R. D. Gelber, W. Yip

*Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in rivistaArticolo in rivista

Abstract

We introduce the stepp packages for R and Stata that implement the subpopulation treatment effect pattern plot (STEPP) method. STEPP is a nonparametric graphical tool aimed at examining possible heterogeneous treatment effects in subpopulations defined on a continuous covariate or composite score. More pecifically, STEPP considers overlapping subpopulations defined with respect to a continuous covariate (or risk index) and it estimates a treatment effect for each subpopulation. It also produces confidence regions and tests for treatment effect heterogeneity among the subpopulations. The original method has been extended in different directions such as different survival contexts, outcome types, or more efficient procedures for identifying the overlapping subpopulations. In this paper, we also introduce a novel method to determine the number of subjects within the subpopulations by minimizing the variability of the sizes of the subpopulations generated by a specific parameter combination. We illustrate the packages using both synthetic data and publicly available data sets. The most intensive computations in R are implemented in Fortran, while the Stata version exploits the powerful Mata language.
Lingua originaleEnglish
pagine (da-a)1-21
Numero di pagine21
RivistaJOURNAL OF DATA SCIENCE
Volume2022
DOI
Stato di pubblicazionePubblicato - 2022

Keywords

  • clinical trial, interaction, subgroup analysis, subpopulation, treatment-covariate interaction

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