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Related Experiment Videos

Model-based approaches to analysing incomplete longitudinal and failure time data

J W Hogan1, N M Laird

  • 1Center for Statistical Sciences, Brown University, Providence, RI 02192, USA.

Statistics in Medicine
|January 15, 1997
PubMed
Summary
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This review covers statistical models for handling missing data in longitudinal studies with informative dropout and survival analyses. It addresses challenges with repeated measurements and event times in complex clinical trial designs.

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Longitudinal Data Analysis

Background:

  • Models for longitudinal progression with informative dropout were introduced by Wu and Carroll.
  • Subsequent research has focused on joint models for longitudinal outcomes and time-to-event data.

Purpose of the Study:

  • To review statistical models designed for situations with both repeated measures and event time data.
  • To address challenges in analyzing incomplete response and covariate data in survival and longitudinal studies.

Main Methods:

  • The review examines various statistical modeling approaches.
  • These models accommodate situations where longitudinal data is subject to dropout or survival data includes time-varying covariates.

Main Results:

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  • Models exist for analyzing longitudinal data with informative dropout.
  • Models are also available for survival studies with time-varying covariates measured repeatedly.
  • These approaches handle incomplete, error-prone, or irregularly scheduled covariate data.

Conclusions:

  • A range of statistical models are available to address complex data structures in longitudinal and survival studies.
  • These models are crucial for robust inference when dealing with informative dropout and time-varying covariates.