Analytical results in longitudinal studies depended on target of inference and assumed mechanism of attrition

Jones, Mark, Mishra, Gita D. and Dobson, Annette (2015) Analytical results in longitudinal studies depended on target of inference and assumed mechanism of attrition. Journal of Clinical Epidemiology, 68 10: 1165-1175. doi:10.1016/j.jclinepi.2015.03.011


Author Jones, Mark
Mishra, Gita D.
Dobson, Annette
Title Analytical results in longitudinal studies depended on target of inference and assumed mechanism of attrition
Journal name Journal of Clinical Epidemiology   Check publisher's open access policy
ISSN 1878-5921
0895-4356
Publication date 2015
Sub-type Article (original research)
DOI 10.1016/j.jclinepi.2015.03.011
Volume 68
Issue 10
Start page 1165
End page 1175
Total pages 11
Place of publication Philadelphia, PA United States
Publisher Elsevier
Collection year 2016
Language eng
Formatted abstract
Objectives

To compare methods for analysis of longitudinal studies with missing data due to participant dropout and follow-up truncated by death.

Study Design and Setting

We analyzed physical functioning in an Australian longitudinal study of elderly women where the missing data mechanism could either be missing at random (MAR) or missing not at random (MNAR). We assumed either an immortal cohort where deceased participants are implicitly included after death or a mortal cohort where the target of inference is surviving participants at each survey wave. To illustrate the methods a covariate was included. Simulation was used to assess the effect of the assumptions.

Results

Ignoring attrition or restricting analysis to participants with complete follow up led to biased estimates. Linear mixed model was appropriate for an immortal cohort under MAR but not MNAR. Linear increment model and joint modeling of longitudinal outcome and time to death were the most robust to MNAR. For a mortal cohort, inverse probability weighting and multiple imputation could be used, but care is needed in specifying dropout and imputation models, respectively.

Conclusion

Appropriate analysis methodology to deal with attrition in longitudinal studies depends on the target of inference and the missing data mechanism.
Keyword Missing data
Longitudinal study
Mortal cohort
Dropout
Simulation study
Attrition
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
School of Public Health Publications
 
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