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Dose–response modelling for bivariate covariates with and without a spike at zero: theory and application to binary outcomes

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  • E. Lorenz
  • C. Jenkner
  • W. Sauerbrei
  • H. Becher

Abstract

type="main"> In epidemiology and clinical research, there is often a proportion of unexposed individuals resulting in zero values of exposure, meaning that some individuals are not exposed and those exposed have some continuous distribution. Examples are smoking or alcohol consumption. We will call these variables with a spike at zero (SAZ). In this paper, we performed a systematic investigation on how to model covariates with a SAZ and derived theoretical odds ratio functions for selected bivariate distributions. We consider the bivariate normal and bivariate log normal distribution with a SAZ. Both confounding and effect modification can be elegantly described by formalizing the covariance matrix given the binary outcome variable Y. To model the effect of these variables, we use a procedure based on fractional polynomials first introduced by Royston and Altman (1994, Applied Statistics 43: 429–467) and modified for the SAZ situation (Royston and Sauerbrei, 2008, Multivariable model-building: a pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables, Wiley; Becher et al., 2012, Biometrical Journal 54: 686–700). We aim to contribute to theory, practical procedures and application in epidemiology and clinical research to derive multivariable models for variables with a SAZ. As an example, we use data from a case–control study on lung cancer.

Suggested Citation

  • E. Lorenz & C. Jenkner & W. Sauerbrei & H. Becher, 2015. "Dose–response modelling for bivariate covariates with and without a spike at zero: theory and application to binary outcomes," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(4), pages 374-398, November.
    • Handle: RePEc:bla:stanee:v:69:y:2015:i:4:p:374-398
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    References listed on IDEAS

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    1. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
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