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Joint modeling of a longitudinal biomarker, recurrent events and a terminal event in a matched study

Author:
Xu, Cong
Published:
[University Park, Pennsylvania] : Pennsylvania State University, 2017.
Physical Description:
1 electronic document
Additional Creators:
Chinchilli, Vernon M., 1952-
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Restrictions on Access:
Open Access.
Summary:
In longitudinal studies, matched designs are often employed to control the potential confounding effects in the field of biomedical research and public health. It is common to collect both repeated measures of risk factors (e.g., biomarkers) and time-to-event data for each subject, such as recurrent events and death. There are existing standard approaches to model the data separately. Mixed effects models are commonly used to model the association between repeated measures and covariates, which can incorporate the correlation among repeated measures. The Cox PH models or accelerated failure time (AFT) model are often used to estimate the covariate effects on the risk of the event. However, separate modeling may lead to biased results and are less efficient when the two processes are related through some unobserved variables. In many instances, the terminal event of death may prevent the observations and even the occurrence of any further recurrent events, but not vice versa. Thus, the common assumption of independent censoring for recurrent events is violated due to the competing risk of death because these two event processes are often correlated. For instance, if recurrent events (e.g., heart attacks) have a substantially negative effect on health condition, then the hazard for death could be increased. In addition, longitudinal biomarkers are often measured repeatedly over time for investigating their association with the event recurrence or death, thus identifying the candidate biomarker with enhance predictive accuracy is crucial for clinical practice. %Moreover, when the objective is to estimate the hazard of the events (e.g., death, cardiovascular disease) and the impact of prognostic biomarkers on the hazard of the event, a joint analysis taking their dependency into account is needed for valid inference. Motivated by the the Assessment, Serial Evaluation, and Subsequent Sequelae in Acute Kidney Injury (ASSESS-AKI) study, several challenges are recognized for joint modeling: 1) A certain large portion of subjects may not have any recurrent events during the study period due to non-susceptibility to events or censoring; 2) there exists left-censoring issue for some longitudinal biomarkers due to inherent limit of detection; 3) The correlation within matched cohorts need to be incorporated; 4) the informative censoring due to competing risk of death need to be adjusted. In this dissertation, first, we propose a joint frailty model with zero-inflated recurrent events and death in a matched study, where a matched logistic model is adopted to adjust for structural zero recurrent events. We incorporated two frailties to measure the dependency between subjects within a match pair and that among recurrent events within each individual. By sharing the random effects, two event processes of recurrent events and death are dependent with each other. Furthermore, because of left-censoring of the assay used to quantify the marker, longitudinal data could be complicated by left-censoring of some measures. Next, we propose a joint model of longitudinal biomarkers, recurrent events and death which can accommodate left-censoring biomarkers. The maximum likelihood based approach is applied for parameter estimation, where the Monte Carlo Expectation-Maximization (MCEM) algorithm is adopted and implemented in R. In addition, alternative estimation methods such as Gaussian quadrature (PROC NLMIXED) and a Bayesian approach (PROC MCMC) are also considered for comparison to show our method's superiority. Extensive simulations are conducted and a real data application on acute ischemic studies is provided.
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Dissertation Note:
Ph.D. Pennsylvania State University, 2017.
Reproduction Note:
Microfilm (positive). 1 reel ; 35 mm. (University Microfilms 28213145)
Technical Details:
The full text of the dissertation is available as an Adobe Acrobat .pdf file ; Adobe Acrobat Reader required to view the file.
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