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The Stata Journal
Volume 9 Number 2: pp. 230-251



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Two techniques for investigating interactions between treatment and continuous covariates in clinical trials

Patrick Royston
Cancer and Statistical Methodology Groups
MRC Clinical Trials Unit
London, UK
pr@ctu.mrc.ac.uk
Willi Sauerbrei
Institute for Medical Biometry and Medical Informatics
Freiburg University Medical Center
Freiburg, Germany
wfs@imbi.uni-freiburg.de
Abstract.  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-bybinary 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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View all articles with these keywords: mfpi, mfpi_plot, stepp_tail, stepp_window, stepp_plot, continuous covariates, treatment–covariate interaction, clinical trials, fractional polynomials, subpopulation treatment-effect pattern plot

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