Staudenmayer John, Spiegelman Donna
Department of Mathematics and Statistics, University of Massachusetts, Lederle Graduate Research Tower, Amherst, Massachusetts 01003, USA.
Biometrics. 2002 Dec;58(4):871-7. doi: 10.1111/j.0006-341x.2002.00871.x.
This article considers the problem of segmented regression in the presence of covariate measurement error in main study/validation study designs. First, we derive a closed and interpretable form for the full likelihood. After that, we use the likelihood results to compute the bias of the estimated changepoint in the case when the measurement error is ignored. We find the direction of the bias in the estimated changepoint to be determined by the design distribution of the observed covariates, and the bias can be in either direction. We apply the methodology to data from a nutritional study that investigates the relation between dietary folate and blood serum homocysteine levels and find that the analysis that ignores covariate measurement error would have indicated a much higher minimum daily dietary folate intake requirement than is obtained in the analysis that takes covariate measurement error into account.
