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

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.


Issue Date:
2009
Publication Type:
Journal Article
DOI and Other Identifiers:
st0164 (Other)
PURL Identifier:
http://purl.umn.edu/127338
Published in:
Stata Journal, Volume 09, Number 2
Page range:
230-251
Total Pages:
22

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 Record created 2017-04-01, last modified 2018-01-06

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