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 examin- ing 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.

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

Venturini, Sergio
;
Bonetti, Marco;
In corso di stampa

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 examin- ing 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.
2022
Venturini, Sergio; Bonetti, Marco; Lazar, Ann A.; Cole, Bernard F.; Wang, Xin ; Victoria, ; Gelber, Richard D.; Yip, Wai-Ki
File in questo prodotto:
File Dimensione Formato  
vent-bone-laza-2022.pdf

accesso aperto

Descrizione: Article
Tipologia: Documento in Post-print (Post-print document)
Licenza: Creative commons
Dimensione 568.21 kB
Formato Adobe PDF
568.21 kB Adobe PDF Visualizza/Apri
stepp_suppmat.pdf

accesso aperto

Descrizione: Supp Mat
Tipologia: Documento in Post-print (Post-print document)
Licenza: Creative commons
Dimensione 891.96 kB
Formato Adobe PDF
891.96 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/4053171
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact