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
[email protected]
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Willi Sauerbrei
Institute for Medical Biometry and Medical Informatics
Freiburg University Medical Center
Freiburg, Germany
[email protected]
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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.
View all articles by these authors:
Patrick Royston, Willi Sauerbrei
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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