Strategies to Fit Pattern-Mixture Models

  • Geert Molenberghs
  • Herbert Thijs
  • Geert Verbeke
  • Bart Michiels
  • Michael G. Kenward

Abstract

Whereas most models for incomplete longitudinal data are formulated within the selection model framework, pattern-mixture models have gained considerable interest in recent years. In this chapter, we outline several strategies to fit pattern-mixture models, including the so-called identifying-restrictions strategy. Multiple imputation is used to apply this strategy to realistic settings, such as quality-of-life data from a longitudinal study on metastatic breast cancer patients.

Keywords

Multiple Imputation Conditional Density Advanced Breast Cancer Patient Miss Data Mechanism Dropout Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • Geert Molenberghs
    • 1
  • Herbert Thijs
    • 1
  • Geert Verbeke
    • 1
  • Bart Michiels
    • 1
  • Michael G. Kenward
    • 1
  1. 1.Janssen Research Foundation, London School of Hygiene and Tropical MedicineUniversiteit Limburg, Katholieke Universiteit LeuvenGermany

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