Subpopulation Treatment Effect Pattern Plot (STEPP) Methods withRandStata

Journal of Data Science

article

Sergio Venturini¹  Marco Bonetti²  Ann A. Lazar⁴  Bernard F. Cole⁶  Xin Victoria Wang⁷  Richard D. Gelber⁷  Wai-Ki Yip⁹ 

[1] Department of Economic and Social Sciences, Università Cattolica del Sacro Cuore;Carlo F. Dondena Research Centre, Università Commerciale L. Bocconi;Bocconi Institute for Data Science and Analytics, Università Commerciale L. Bocconi;Division of Oral Epidemiology, Department of Preventive and Restorative Dental Sciences, University of California;Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California;Department of Mathematics and Statistics, University of Vermont;Department of Data Science, Dana-Farber Cancer Institute;Department of Biostatistics, Harvard T.H. Chan School of Public Health;Agenus, Inc.

关键词: clinical trial;    interaction;    subgroup analysis;    subpopulation;    treatment-covariate interaction;   

DOI  :  10.6339/22-JDS1060

学科分类：土木及结构工程学

来源: JDS

【 摘 要 】

We introduce thestepppackages forRandStatathat 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 inRare implemented inFortran , while theStataversion exploits the powerfulMatalanguage.

【 授权许可】

CC BY