| Summary: | Longitudinal studies often contain several statistical issues, suchas longitudinal process and time-to-event process, the associationamong which requires joint modeling strategy.
We firstly review the recent researches on the joint modeling topic. After that, four popular inference methods are introduced for jointly analyzing longitudinal data and time-to-event data based on a combination of typical parametric models. However, some of them may suffer from non-ignorable bias of the estimators. Others may be computationally intensive or even lead to convergence problems.
In this thesis, we propose an approximate likelihood-based simultaneous inference method for jointly modeling longitudinal
process and time-to-event process with covariate measurement errors problem. By linearizing the joint model, we design a strategy for updating the random effects that connect the two processes, and propose two algorithm frameworks for different scenarios of joint likelihood function. Both frameworks approximate the multidimensional integral in the observed-data joint likelihood by analytic expressions, which greatly reduce the computational intensity of the complex joint modeling problem.
We apply this new method to a real dataset along with some available methods. The inference result provided by our new method agrees with those from other popular methods, and makes sensible biological interpretation. We also conduct a simulation study for comparing these methods. Our new method looks promising in terms of estimation precision, as well as computation efficiency, especially when more subjects are given. Conclusions and discussions for future research are listed in the end.
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