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dc.contributor.authorNgwa, Julius S.en_US
dc.date.accessioned2016-03-16T19:04:10Z
dc.date.available2016-03-16T19:04:10Z
dc.date.issued2013
dc.identifier.urihttps://hdl.handle.net/2144/15210
dc.description.abstractJoint modeling of longitudinal and survival data has received much attention and is becoming increasingly useful. In clinical studies, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for longitudinal data and a survival model is applied to event outcomes. The argument in favor of a joint model has been the efficient use of the data as the survival information goes into modeling the longitudinal process and vice versa. In this thesis, we present joint maximum likelihood methods, a two stage approach and time dependent covariate methods that link longitudinal data to survival data. First, we use simulation studies to explore and assess the performance of these methods with bias, accuracy and coverage probabilities. Then, we focus on four time dependent methods considering models that are unadjusted and adjusted for time. Finally, we consider mediation analysis for longitudinal and survival data. Mediation analysis is introduced and applied in a research framework based on genetic variants, longitudinal measures and disease risk. We implement accelerated failure time regression using the joint maximum likelihood approach (AFT-joint) and an accelerated failure time regression model using the observed longitudinal measures as time dependent covariates (AFT-observed) to assess the mediated effect. We found that the two stage approach (TSA) performed best at estimating the link parameter. The joint maximum likelihood methods that used the predicted values of the longitudinal measures, similar to the TSA, provided larger estimates. The time dependent covariate methods that used the observed longitudinal measures in the survival analysis underestimated the true estimates. The mediation results showed that the AFT-joint and the AFT-observed underestimated the mediated effect. Comparison of the methods in Framingham Heart Study data revealed similar patterns. We recommend adjusting for time when estimating the association parameter in time dependent Cox and logistic models. Additional work is needed for estimating the mediated effect with longitudinal and survival data.en_US
dc.language.isoen_US
dc.subjectBiostatisticsen_US
dc.subjectMediationen_US
dc.subjectAccelerated failure timeen_US
dc.subjectJoint likelihooden_US
dc.subjectLongitudinal and survival dataen_US
dc.subjectTime dependent covariate methodsen_US
dc.subjectTwo stage approachen_US
dc.titleComparing methods for modeling longitudinal and survival data, with consideration of mediation analysisen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2016-03-14T19:22:22Z
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplineBiostatisticsen_US
etd.degree.grantorBoston Universityen_US


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