Estimating Time-to-Event Characteristics via Longitudinal Threshold Regression Models - an Application to Cervical Dilation Progression

• Activity Number: 466 - First-Hitting-Time Based Threshold Regression and Applications
• Type: Topic Contributed
• Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
• Sponsor: Section on Risk Analysis
• Author(s): Caroline Mulatya* and Alexander C McLain and Bo Cai and James Hardin and Paul S Albert
• Companies: Emmes and University of South Carolina and University of South Carolina and University of South Carolina and National Cancer Institute
• Keywords: First hitting time; Threshold regression; Wiener process

Abstract

In longitudinal studies, it is sometimes of interest to estimate the distribution of the time a longitudinal process takes to traverse from one threshold to another. For example, the distribution of the time it takes a woman's cervical dilation to progress from 3 to 4 cm can aid the decision making of obstetricians as to whether a stalled labor should be allowed to proceed or stopped in favor of other options. Often researchers treat this type of data structure as interval censored and employ traditional survival analysis methods. However, the traditional interval censoring approaches are inefficient in that they do not use all of the available data. In this talk, we propose utilizing a longitudinal threshold model to estimate the distribution of the elapsed time between two thresholds of the longitudinal process from repeated measurements. A Wiener process under the first hitting time (FHT) framework is used to represent survival distribution. We demonstrate our model through an analysis of data from the Consortium on Safe Labor (CSL) study.