Journal
STATA JOURNAL
Volume 9, Issue 2, Pages 230-251Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/1536867X0900900204
Keywords
st0164; mfpi; mfpi_plot; stepp_tail; stepp_window; stepp_plot; continuous covariates; treatment-covariate interaction; clinical trials; fractional polynomials; subpopulation treatment-effect pattern plot
Funding
- Medical Research Council [MC_EX_G0800814] Funding Source: researchfish
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There is increasing interest in the medical world in the possibility of tailoring treatment to the individual patient. Statistically, the relevant task is to identify interactions between covariates and treatments, such that the patient's value of a given covariate influences how strongly (or even whether) they are likely to respond to a treatment. The most valuable data are obtained in randomized controlled clinical trials of novel treatments in comparison with a control treatment. We describe two techniques to detect and model such interactions. The first technique, multivariable fractional polynomials interaction, is based on fractional polynomials methodology, and provides a method of testing for continuous-by-binary interactions and by modeling the treatment effect as a function of a continuous covariate. The second technique, subpopulation treatment-effect pattern plot, aims to do something similar but is focused on producing a nonparametric estimate of the treatment effect, expressed graphically. Stata programs for both of these techniques are described. Real data for brain and breast cancer are used as examples.
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