Predictors of Retention in the 1Florida Alzheimer’s Disease Research Center (ADRC) Over Two Waves

<b>Background:</b> Attrition is a significant methodological concern in longitudinal studies. Sample loss can limit generalizability and compromise internal validity. <b>Methods:</b> Wave one (<i>n</i> = 346) and wave two follow-ups (<i>n</i> = 196) of the 1Florida ADRC clinical core were examined using a 24-month visit window. <b>Results:</b> The sample (59% Hispanic) demonstrated retention rates of 77.2% and 86.2% in waves one and two, respectively. Predictors of lower retention in wave one included older age, amnestic MCI or dementia, and lower cognition and function scores. Completing a baseline MRI and lack of hippocampal atrophy were associated with higher retention in both waves. In wave two, a greater neighborhood disadvantage score was associated with attrition. <b>Discussion:</b> Predictors of retention changed over time, possibly due to the early withdrawal of the most vulnerable in the initial wave. Understanding predictors of retention can facilitate retention strategies, reduce attrition, and increase the validity of findings.

Authors

Institutions

Publication Details

Journal
Journal of Applied Gerontology
Published
2024-12-10
DOI
https://doi.org/10.1177/07334648241302159
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predictors of Retention in the 1Florida Alzheimer’s Disease Research Center (ADRC) Over Two Waves

Warren Barker, Shanna L. Burke, Miriam J. Rodríguez, Raquel Behar et al.
Journal of Applied Gerontology
Advanced Causal Inference Techniques
article

Predictors of Retention in the 1Florida Alzheimer’s Disease Research Center (ADRC) Over Two Waves

Warren Barker, Shanna L. Burke, Miriam J. Rodríguez, Raquel Behar, Cesar Chirinos, Adrienne Grudzien, Idaly Vélez Uribe, Mónica Rosselli, Michael Marsiske, Mercedes Herrera, Carlos Quiñonez, Joanna Gonzalez, Malek Adjouadi, Maria T. Greig‐Custo, David Loewenstein, Ranjan Duara, Yaimara Gonzalez Pineiro, Rosemarie Rodriguez
article en

Abstract

<b>Background:</b> Attrition is a significant methodological concern in longitudinal studies. Sample loss can limit generalizability and compromise internal validity. <b>Methods:</b> Wave one (<i>n</i> = 346) and wave two follow-ups (<i>n</i> = 196) of the 1Florida ADRC clinical core were examined using a 24-month visit window. <b>Results:</b> The sample (59% Hispanic) demonstrated retention rates of 77.2% and 86.2% in waves one and two, respectively. Predictors of lower retention in wave one included older age, amnestic MCI or dementia, and lower cognition and function scores. Completing a baseline MRI and lack of hippocampal atrophy were associated with higher retention in both waves. In wave two, a greater neighborhood disadvantage score was associated with attrition. <b>Discussion:</b> Predictors of retention changed over time, possibly due to the early withdrawal of the most vulnerable in the initial wave. Understanding predictors of retention can facilitate retention strategies, reduce attrition, and increase the validity of findings.

Journal of Applied GerontologyVol. 44(8)
University of Miami (US), Florida International University (US), University of Florida (US), Mount Sinai Medical Center (US), Indiana University Bloomington (US), Florida Atlantic University (US)
National Science Foundation, National Institutes of Health, National Institute on Aging
Openalex Percentile: Top 77%
Advanced Causal Inference Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.