Novel Methods for Analyzing Longitudinal Data with Measurement Error in the Time Variable

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

Implementation facilitation to introduce and support emergency department-initiated buprenorphine for opioid use disorder in high need, low resource settings: protocol for multi-site implementation-feasibility study

Background: For many reasons, the emergency department (ED) is a critical venue to initiate OUD interventions. The prevailing culture of the ED has been that substance use disorders are non-emergent conditions better addressed outside the ED where resources are less constrained. This study, its rapid funding mechanism, and accelerated timeline originated out of the urgent need to learn whether ED-initiated buprenorphine (BUP) with referral for treatment of OUD is generalizable, as well as to develop strategies to facilitate its adoption across a variety of ED settings and under real-world conditions. It both complements and uses methods adapted from Project ED Health (CTN-0069), a Hybrid Type 3 implementation-effectiveness study of using Implementation Facilitation (IF) to integrate ED-initiated BUP and referral programs. Methods: ED-CONNECT (CTN 0079) was a three-site implementation study exploring the feasibility, acceptability, and impact of introducing ED-initiated BUP in rural and urban settings with high-need, limited resources, and different staffing structures. We used a multi-faceted approach to develop, introduce and iteratively refine site-specific ED clinical protocols and implementation plans for opioid use disorder (OUD) screening, ED-initiated BUP, and referral for treatment. We employed a participatory action research approach and use mixed methods incorporating data derived from abstraction of medical records and administrative data, assessments of recruited ED patient-participants, and both qualitative and quantitative inquiry involving staff from the ED and community, patients, and other stakeholders. Discussion: This study was designed to provide the necessary, time-sensitive understanding of how to identify OUD and initiate treatment with BUP in the EDs previously not providing ED-initiated BUP, in communities in which this intervention is most needed: high need, low resource settings. Trial registration: The study was prospectively registered on ClinicalTrials.gov (NCT03544112) on June 01, 2018: https://clinicaltrials.gov/ct2/show/NCT03544112