Date of Award


Document Type

Open Access Dissertation


Health Services and Policy Management


Norman J. Arnold School of Public Health

First Advisor

Alexander C. McLain


In some longitudinal studies, the observed time points are often confounded with measurement error due to the sampling conditions, resulting into data with measurement error in the time variable. This type of data occurs mainly in observational studies when the onset of a longitudinal process is unknown or in clinical trials when individual visits do not take place as specified by the study protocol, but are often rounded to coincide with the study protocol. Methodological and inferential implications of error in time varying covariates for both linear and nonlinear models have been studied widely. In this dissertation, we shift attention to another source of measurement error in the time variable in longitudinal studies. Specifically, we develop statistical methods for analyzing longitudinal data when the onset of the process is unknown. This work has been motivated by a cervical dilation data from the Consortium on Safe Labor (CSL) study, a multi-center retrospective observational study conducted by the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The uncertainty in onset of labor poses methodological challenges since the observed time variable is related to when women get to the hospital, not the biologic process of interest. In Chapter II, we present a Longitudinal Threshold Regression model for estimating the distribution of the time a woman’s cervical dilation takes to progress from one threshold to another (in cm). In Chapter III, we present a Semi-parametric model with random shift parameters for modeling labor curves prospectively. In Chapter IV, we extend Chapter III to predict women’s time to full dilation given their past measurements. We demonstrate the proposed methods using simulation studies and a data from the CSL study.


© 2016, Caroline Munindi Mulatya