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A comparison of procedures to correct for base‐line differences in the analysis of continuous longitudinal data: a case‐study

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  • G. Verbeke
  • S. Fieuws
  • E. Lesaffre
  • B. S. Kato
  • M. D. Foreman
  • P. L. O. Broos
  • K. Milisen

Abstract

Summary. The main advantage of longitudinal studies is that they can distinguish changes over time within individuals (longitudinal effects) from differences between subjects at the start of the study (base‐line characteristics; cross‐sectional effects). Often, especially in observational studies, subjects are very heterogeneous at base‐line, and one may want to correct for this, when doing inferences for the longitudinal trends. Three procedures for base‐line correction are compared in the context of linear mixed models for continuous longitudinal data. All procedures are illustrated extensively by using data from an experiment which aimed at studying the relationship between the post‐operative evolution of the functional status of elderly hip fracture patients and their preoperative neurocognitive status.

Suggested Citation

  • G. Verbeke & S. Fieuws & E. Lesaffre & B. S. Kato & M. D. Foreman & P. L. O. Broos & K. Milisen, 2006. "A comparison of procedures to correct for base‐line differences in the analysis of continuous longitudinal data: a case‐study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(1), pages 93-101, January.
    • Handle: RePEc:bla:jorssc:v:55:y:2006:i:1:p:93-101
    DOI: 10.1111/j.1467-9876.2005.00531.x
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    Cited by:

    1. Orelien, Jean G. & Edwards, Lloyd J., 2008. "Fixed-effect variable selection in linear mixed models using R2 statistics," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1896-1907, January.
    2. Chung-Wei Shen & Yi-Hau Chen, 2012. "Model Selection for Generalized Estimating Equations Accommodating Dropout Missingness," Biometrics, The International Biometric Society, vol. 68(4), pages 1046-1054, December.

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