Dataset Information
Controlling for selective dropout in longitudinal dementia data: Application to the SveDem registry.
Ontology highlight
ABSTRACT: 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.
SUBMITTER: Handels R
PROVIDER: S-EPMC7984348 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
Publications
Controlling for selective dropout in longitudinal dementia data: Application to the SveDem registry.
Alzheimer's & dementia : the journal of the Alzheimer's Association 20200322 5
<h4>Introduction</h4>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.<h4>Methods</h4>Longitudinal cognitive data were obtained from 53,880 persons from the SveDem national quality dementia registry. Inverse probability of censoring ...[more]