Statistical analysis of data from opioid use disorder study

Author: 
Date created: 
2020-04-24
Identifier: 
etd20852
Keywords: 
Cox regression model
Frailty variable
Generalized linear mixed-effects model
Logistic regression model
Poisson rate
Robust variance estimation
Abstract: 

This project presents statistical analyses of data from a population based opioid use disorder research program. The primary interest is in estimating the association of a range of demographic, clinical and provider-related characteristics on retention in treatment for opioid use disorders. This focus was motivated by the province’s efforts to respond to the opioid overdose crisis, and the methodological challenges inherent in analyzing the recurrent nature of opioid use disorder and the treatment episodes. We start with executing a network analysis to clarify the influence of provider-related characteristics, including individual-, case-mix and prescriber network-related characteristics on treatment retention. We observe that the network characteristics have a statistically significant impact on OAT retention. Then we use a Cox proportional hazards model with a gamma frailty, while also considering how the ending of the previous episode will impact the future ones to start our investigation into the importance of the episode endings. Moreover, we consider three different analyses under multiple scenarios to reach our final goal of analyzing the multi-type events. The OAT episode counts of the study subjects throughout the follow-ups are analyzed using Poisson regression models. Logistic regression analyses of the records of the OAT episode types are conducted with mixed effects. Lastly, we analyze the OAT episode duration times marginally via an estimating function approach. The robust variance estimator is identified for the estimator of the model parameters. In addition, we conduct a simulation study to verify the findings of the data analysis. The outcomes of the analyses indicate that the OAT episode counts and duration times are significantly associated with a few covariates, such as gender and birth era, and the relationships are varying according to the OAT episode types.

Document type: 
Graduating extended essay / Research project
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Supervisor(s): 
X. Joan Hu
Department: 
Science: Department of Statistics and Actuarial Science
Thesis type: 
(Project) M.Sc.
Statistics: