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Handling interactions in Stata, especially with continuous predictors

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  • Patrick Royston

    (MRC Clinical Trials Unit and University College London)

  • Willi Sauerbrei

    (University of Freiburg)

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    Abstract

    In an era in which doctors and patients aspire to personalized medicine and more sophisticated risk estimation, detecting and modeling interactions between covariates or between covariates and treatment is increasingly important. In observational studies (for example, in epidemiology), interactions are known as effect modifiers; their presence can substantially change the understanding of how a risk factor impacts the outcome. However, modeling interactions in an appropriate and interpretable way is not straightforward. In our talk, we consider two related topics. The first topic is modeling interactions in observational studies that involve at least one continuous covariate, an area that practitioners apparently find difficult. We introduce a new Stata program, mfpigen, for detecting and modeling such interactions using fractional polynomials, adjusting for confounders if necessary. The second topic is modeling interactions between treatment and continuous covariates in randomized controlled trials. We outline a Stata program, mfpi, designed for this purpose. Key themes of our talk are the vital role played by graphical displays of interactions and the importance of applying simple plausibility checks.

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    File URL: http://repec.org/usug2012/UK12_royston.pdf
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    Bibliographic Info

    Paper provided by Stata Users Group in its series United Kingdom Stata Users' Group Meetings 2012 with number 16.

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    Date of creation: 22 Sep 2012
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    Handle: RePEc:boc:usug12:16

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    Web page: http://www.stata.com/meeting/uk12
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