Semiparametric Time-Varying Coefficient Model for Matched Case-Crossover Studies

• Activity Number: 183
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Ana Maria Ortega Villa*+ and Inyoung Kim
• Companies: Virginia Tech and Virginia Tech
• Keywords: Conditional logistic regression ; Matched case-control study ; Regression splines ; Stratum ; Varying Coefficient Model

Abstract

In matched case-crossover studies, it is generally accepted that the covariates on which a case and associated controls are matched, cannot exert a confounding effect on independent predictors included in the conditional logistic regression model because any stratum effect is removed by the conditioning on the fixed number of sets of the case and controls in the stratum. However, some matching covariates such as time are not identified from stratum. They often play important role in parameter estimation. Hence in this paper, we propose a semiparametric approach to detect both parametric and nonparametric time varying coefficients. Our approach is developed using the regression splines varying coefficient model. We demonstrate the advantage of our approach using both simulation and an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis with drinking water turbidity.In matched case-crossover studies, it is generally accepted that the covariates on which a case and associated controls are matched, cannot exert a confounding effect on independent predictors included in the conditional logistic regression model because any stratum effect