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.
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