Survival Analysis with Measurement Error in a Cumulative Exposure Variable: Radon Progeny in Relation to Lung Cancer Mortality

• Activity Number: 175
• Type: Contributed
• Date/Time: Monday, August 1, 2016 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Polyna Khudyakov* and Jonathan Samet and Charles Wiggins and Xiaomei Liao and Angela Meisner and Donna Spiegelman
• Companies: Harvard T.H. Chan School of Public Health and University of Southern California and University of New Mexico and Harvard T.H. Chan School of Public Health and New Mexico Tumor Registry and Harvard T.H. Chan School of Public Health
• Keywords: measurement error ; Cox proportional hazards model ; risk set regression calibration ; radon ; functions of a time-varying exposure history

Abstract

Exposure variables in occupational and environmental epidemiology are usually measured with error. This error tends to flatten the estimated exposure-response relationship. Here, we extended the risk set regression calibration (RRC) method for Cox models to be able to analyze cumulative exposure variables to obtain unbiased point and interval estimates of relative risks corrected for exposure measurement error. We show that the RRC methodology originally developed for use with an external validation study can also be applied to internal validation study designs as well. We then analyzed the 3,469 New Mexico uranium miners cohort with follow-up extended from 1957 to 2012. The exposure data were collected using several different methods of measurement, some of which had a substantial amount of error. After adjusting for bias due to exposure measurement error, the multivariate-adjusted hazard ratio for lung cancer mortality in relation to cumulative radon exposure was estimated to be 4.69 (95%CI 2.21-9.95), substantially higher than the estimate obtained from the standard analysis ignoring measurement error (HR=1.35, 95%CI 1.21-1.50).