Survival Analysis with Error-Prone Time-Varying Covariates: A Risk Set Calibration Approach

• Activity Number: 184
• Type: Contributed
• Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Xiaomei Liao*+ and David M. Zucker and Yi Li and Donna Spiegelman
• Companies: Harvard University and Hebrew University and Harvard University/Dana-Farber Cancer Institute and Harvard University
• Keywords: Cox proportional hazards model ; Measurement error ; Risk set regression calibration ; Time-varying covariates

Abstract

Occupational and environmental epidemiologists are often interested in estimating the prospective effect of time-varying exposure variables such as the cumulative exposure or cumulative updated average exposure,in relation to chronic disease endpoints such as cancer incidence and mortality. By recalibrating within each risk set, the risk set regression method is proposed for this setting. An algorithm for a bias-corrected point estimate of the relative risk using an RRC approach is presented,followed by the derivation of an estimate of its variance, resulting in a sandwich estimator.Emphasis is on methods which apply to the main study/external validation study design. Simulation studies with different error models are carried out to show the validity and efficiency of the method, compared to the `naive' cox model, and the method is applied to a study of diet and cancer from HPFS data.