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Parametric models for incomplete continuous and categorical longitudinal data.

M G Kenward1, G Molenberghs

  • 1Institute of Mathematics and Statistics, University of Kent, Canterbury, UK. m.g.kenward@ukc.ac.uk

Statistical Methods in Medical Research
|May 29, 1999
PubMed
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This review examines models for incomplete longitudinal data, focusing on nonrandom missingness. It contrasts selection and pattern-mixture models, highlighting identifiability and sensitivity in statistical analysis.

Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Incomplete longitudinal data presents challenges in statistical modeling.
  • Nonrandom missingness requires specialized approaches beyond complete-data methods.
  • Rubin's classification provides a framework for understanding missing data mechanisms.

Purpose of the Study:

  • To review and compare statistical models for incomplete continuous and categorical longitudinal data.
  • To specifically address the complexities of nonrandom missingness.
  • To emphasize the critical concepts of identifiability and sensitivity in model selection.

Main Methods:

  • Review of existing statistical literature on missing data models.
  • Comparison and contrast of selection models and pattern-mixture models.

Related Experiment Videos

  • Illustrative examples to demonstrate model application and differences.

    • Main Results:

    • Selection and pattern-mixture models offer distinct strategies for handling nonrandom missingness.
    • Identifiability issues can arise in certain model specifications.
    • Sensitivity analyses are crucial for assessing the robustness of results to model assumptions.

    Conclusions:

    • Careful consideration of model class is necessary for valid inference with nonrandomly missing longitudinal data.
    • Understanding identifiability and performing sensitivity analyses are key steps in robust statistical practice.
    • Both selection and pattern-mixture models are valuable tools, each with specific strengths and limitations.