In studying the evolution of a disease and effects of treatment on it, investigators often
collect repeated measures of disease severity (longitudinal data) and measure time to
occurrence of a clinical event (survival data). The development of joint models for such
longitudinal and survival data often uses individual-specific latent processes that evolve
over time and contribute to both the longitudinal and survival outcomes. Such models
allow substantial flexibility to incorporate association across repeated measurements,
among multiple longitudinal outcomes, and between longitudinal and survival outcomes.
The joint modeling framework has been extended to handle many complexities of
real data, but less attention has been paid to the properties of such models. We are
interested in the “payoff” of joint modeling, that is, whether using two sources of data
simultaneously offers better inference on individual- and population-level characteristics,
as compared to using them separately. We consider the problem of attributing
informational content to the data inputs of joint models by developing analytical and
numerical approaches and demonstrating their use.
As a motivating application, we consider a clinical trial for treatment of mesothelioma,
a rapidly fatal form of lung cancer. The trial protocol included patient-reported
outcome (PRO) collection throughout the treatment phase and followed patients until
progression or death to determine progression-free survival times. We develop models
that extend the joint modeling framework to accommodate several features of the longitudinal
data, including bounded support, excessive zeros, and multiple PROs measured
simultaneously. Our approaches produce clinically relevant treatment effect estimates
on several aspects of disease simultaneously and yield insights on individual-level variation
in disease processes.
University of Minnesota Ph.D. dissertation. August 2011. Major: Biostatistics. Advisor: Brad Carlin. 1 computer file (PDF); x, 111 pages, appendix A.
Hatfield, Laura A..
Bayesian hierarchical joint modeling for longitudinal and survival data..
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