Bayesian approaches to joint cure-rate and longitudinal models with applications to cancer vaccine trials.
Complex issues arise when investigating the association between longitudinal immunologic measures and time to an event, such as time to relapse, in cancer vaccine trials. Unlike many clinical trials, we may encounter patients who are cured and no longer susceptible to the time-to-event endpoint. If there are cured patients in the population, there is a plateau in the survival function, S(t), after sufficient follow-up. If we want to determine the association between the longitudinal measure and the time-to-event in the presence of cure, existing methods for jointly modeling longitudinal and survival data would be inappropriate, since they do not account for the plateau in the survival function. The nature of the longitudinal data in cancer vaccine trials is also unique, as many patients may not exhibit an immune response to vaccination at varying time points throughout the trial. We present a new joint model for longitudinal and survival data that accounts both for the possibility that a subject is cured and for the unique nature of the longitudinal data. An example is presented from a cancer vaccine clinical trial.
Department of Biostatistics, University of Washington, Seattle, Washington 98105, USA. elizab@u.washington.edu
Elizabeth R
ER
Brown
Joseph G
JG
Ibrahim
D001499:Bayes Theorem; D001699:Biometry; D019496:Cancer Vaccines / Q000494:pharmacology*; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D009369:Neoplasms / Q000276:immunology / Q000401:mortality / Q000628:therapy*; D015996:Survival Rate
D019496:Cancer Vaccines