A graphical method to assess treatment-covariate interactions using the Cox model on subsets of the data.
We introduce the subpopulation treatment effect pattern plot (STEPP) method, designed to facilitate the interpretation of estimates of treatment effect derived from different but potentially overlapping subsets of clinical trial data. In particular, we consider sequences of subpopulations defined with respect to a covariate, and obtain confidence bands for the collection of treatment effects (here obtained from the Cox proportional hazards model) associated with the sequences. The method is aimed at determining whether the magnitude of the treatment effect changes as a function of the values of the covariate. We apply STEPP to a breast cancer clinical trial data set to evaluate the treatment effect as a function of the oestrogen receptor content of the primary tumour.
Bonetti
M
M
Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute, Boston, MA 02115, USA. bonetti@jimmy.harvard.edu
Gelber
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RD
D018931:Antineoplastic Agents, Hormonal / Q000627:therapeutic use*; D001943:Breast Neoplasms / Q000188:drug therapy* / Q000401:mortality; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003196:Computer Graphics; D018572:Disease-Free Survival; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008875:Middle Aged; D015337:Multicenter Studies as Topic / Q000706:statistics & numerical data*; D016016:Proportional Hazards Models; D013629:Tamoxifen / Q000627:therapeutic use*; D013997:Time Factors
D018931:Antineoplastic Agents, Hormonal; D013629:Tamoxifen