RAND Statistics Seminar Series

Survival Analysis Using Flowgraph Models

Presented by Aparna V. Huzurbazar, Senior Advisor, Statistician
Department of Mathematics and Statistics
University of New Mexico
Thursday, June 19, 2003 4:00 pm
Main Conference Room


Flowgraph models are useful for modeling multistate time to event data. Such data commonly arise in survival analysis. Flowgraph models have been used to model progression of diseases such as cancer and AIDS, and for degenerative diseases such as diabetic retinopathy and kidney failure. They are especially useful for constructing Bayes predictive distributions, survivor functions, and hazard functions. Flowgraph models, when combined with saddlepoint approximations, allow a wide variety of parametric time-to-event modeling. They analyze semi-Markov processes using data on outcomes, probabilities of outcomes, and waiting times for outcomes to occur. They are useful for constructing likelihoods for incomplete data and useful in situations where data are unrecognizably incomplete. Recently, methodology has been developed that puts flowgraphs models into the counting processes framework. I will discuss inference based on flowgraph models using real data applications, the relationship of flowgraphs to counting processes, and time permitting, current work incorporating patient covariates into flowgraphs.