Title: Estimation in the Cox survival regression model with covariate measurement error and a changepoint
Authors: Sarit Agami - The Hebrew University of Jerusalem (Israel) [presenting]
Abstract: The Cox regression model is a popular model for analyzing the relationship between a covariate vector and a survival endpoint. The standard Cox model assumes a constant covariate effect across the entire covariate domain. However, in many epidemiological and other applications, the covariate of main interest is subject to a threshold effect: a change in the slope at a certain point within the covariate domain. Often, the covariate of interest is subject to some degree of measurement error. The measurement error correction is discussed in the case where the threshold is known. Several bias correction methods are examined: two versions of regression calibration (RC1 and RC2, the latter of which is new), two methods based on the induced relative risk under a rare event assumption (RR1 and RR2, the latter of which is new), a maximum pseudo partial likelihood estimator (MPPLE), and simulation-extrapolation (SIMEX). The theoretical properties of these methods and a simulation comparing the methods are discussed. An illustrative example of the relationship between chronic air pollution exposure to particulate matter PM10 and fatal myocardial infarction (Nurses Health Study (NHS)) is presented.