Techniques for incorporating longitudinal measurements into analyses of survival data from clinical trials.
This article reviews existing approaches for joint analysis of longitudinal measurements, possibly measured with error or incompletely observed, and event-time data, possibly censored. The models take the form of selection or pattern-mixture models; estimation proceeds via the EM algorithm or Bayesian sampling techniques. The models are compared, their estimation and inferential procedures described, and advantages and disadvantages noted. Examples are discussed from several disease areas, including cancer and AIDS.
Troxel
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AB
Department of Biostatistics, Mailman School of Public Health, Columbia University, 622 W 168th Street, PH18W, New York, NY 10032, USA. atroxel@biostat.columbia.edu
D000465:Algorithms; D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D015658:HIV Infections / Q000276:immunology / Q000628:therapy; D006801:Humans; D008137:Longitudinal Studies; D008175:Lung Neoplasms / Q000601:surgery; D012559:Schizophrenia / Q000188:drug therapy; D013223:Statistics as Topic / Q000379:methods*; D016019:Survival Analysis; D014481:United States