Abstract
This chapter presents a background on flowgraph models and reviews the basic series flowgraph structure. Flowgraph models provide greater generality for the analysis of time to event data arising from multistate models for disease progression. Flowgraphs allow non-exponential waiting times for each stage of the disease and provide a data-analytic method for modeling semi-Markov processes. They do not make the proportional hazards assumption but rather model the waiting-time densities directly and then use those to compute the corresponding hazard functions. Data analysis with series flowgraphs by using data for the progression of HIV and Bayesian methods is discussed. Saddlepoint approximations for flowgraph models and likelihood construction for flowgraphs in the presence of incomplete data, using an example of kidney failure, are explained along with parallel and loop flowgraph structures and a general procedure for handling flowgraphs that combine series, parallel, and loop structures such as the retinopathy flowgraph. A full analysis of the diabetic retinopathy flowgraph is also given.
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