Doctor of Public Health (Dr.PH.)
Degree Granting Department
Epidemiology and Biostatistics
Yangxin Huang, Ph.D.
Henian Chen, Ph.D.
Alfred Mbah, Ph.D.
Feng Cheng, Ph.D.
Bayesian inference, longitudinal-survival data, multivariate linear mixed-effects models, skewed distribution
In epidemiologic and clinical studies, a relatively large number of biomarkers are repeatedly measured in patients over time, often associated with data on epidemiologic and clinical interest events. So, much attention is focused on developing the specific patterns of the longitudinal measurements, and the associations between those patterns and the time to a certain event, such as heart attack, diagnose of disease, time to transplantation, or death. In the last two decades, the research into joint modeling of longitudinal and time-to-event data has received a tremendous amount of attention.
Numerous researchers have proposed joint modeling approaches for a single longitudinal exposure and time-to-event data, herein referred to as univariate joint modeling. Those model-based analyses may not provide robust inference when longitudinal measurements exhibit skewness and/or heavy tails. Additionally, the collected data are often featured by multivariate longitudinal exposures, which are significantly correlated, and ignore their correlation may lead to biased estimation. To the best of our knowledge, few studies focus on the multivariate joint modeling (MVJM) with a skewed distribution for longitudinal responses to cope with correlated multiple longitudinal exposures and adjust departures from normality and tailor linkage in specifying a survival process.
This dissertation research contributes to the Bayesian inference statistical methodology in the field of joint modeling analyses, which is applied to a type 1 diabetes (T1D) study and a primary biliary cirrhosis (PBC) study. In this dissertation, firstly, we develop multivariate mixed-effects joint modeling for skewed-longitudinal and time-to-event data with skew distribution; the associated Bayesian inferential approach is introduced; the proposed joint modeling is applied to analyze the diabetes study data. Simulation studies are also conducted to assess the performance of the proposed method under different scenarios. Secondly, a Bayesian trivariate linear mixed-effects models with skew-normal distribution is applied to analyze Mayo Clinical PBC study data. This biostatistical methodology can be widely used in epidemiological and clinical study. Our multivariate joint modeling can provide more precise information and evidence to physicians, benefiting their treatment evaluation and clinical decision-making. All the analyses introduced in this dissertation have been implemented under the Bayesian framework, in the R and WinBUGS public and freely available software environment. The software codes are available from the author upon request.
Scholar Commons Citation
Xu, Lan, "Bayesian Multivariate Joint Modeling for Skewed-longitudinal and Time-to-event Data" (2021). USF Tampa Graduate Theses and Dissertations.