Controlling for selective dropout in longitudinal dementia data: Application to the SveDem registry

Ron Handels*, Linus Jönsson, Sara Garcia-Ptacek, Maria Eriksdotter, Anders Wimo

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


INTRODUCTION: Loss to follow-up in dementia studies is common and related to cognition, which worsens over time. We aimed to (1) describe dropout and missing cognitive data in the Swedish dementia registry, SveDem; (2) identify factors associated with dropout; and (3) estimate propensity scores and use them to adjust for dropout.

METHODS: Longitudinal cognitive data were obtained from 53,880 persons from the SveDem national quality dementia registry. Inverse probability of censoring weights (IPCWs) were estimated using a logistic regression model on dropout.

RESULTS: The mean annualized rate of change in Mini-Mental State Examination (MMSE) in those with a low MMSE (0 to 10) was likely underestimated in the complete case analysis (+1.5 points/year) versus the IPCW analysis (-0.3 points/year).

DISCUSSION: Handling dropout by IPCWs resulted in plausible estimates of cognitive decline. This method is likely of value to adjust for biased dropout in longitudinal cohorts of dementia.

Original languageEnglish
Pages (from-to)789-796
Number of pages8
JournalAlzheimer's & Dementia
Issue number5
Publication statusPublished - May 2020


  • attrition
  • cognition
  • dementia
  • dropout
  • inverse probability of censoring weighting
  • loss to follow-up
  • registry


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