ArticleBMC medical research methodology2018
Modelling attrition and nonparticipation in a longitudinal study of prostate cancer.
Article in BMC medical research methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 30 citations in OpenAlex.
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- Characteristics and patient-reported outcomes associated with dropout in severely affected oncological patients: an exploratory study.BMC medical research methodology · 2021Trial
- Work Disability, Early Retirement, and Loss of a Loved One in the World Trade Center Health Registry Cohort.American journal of industrial medicine · 2026Article
- ATTITUDE - Addressing attrition in longitudinal cancer cohorts: an in-depth qualitative analysis of experiences and perspectives on participation in longitudinal studies among breast cancer survivors.Breast cancer research and treatment · 2026Observational
- Predictors of Retention in the 1Florida Alzheimer's Disease Research Center (ADRC) Over Two Waves.Journal of applied gerontology : the official journal of the Southern Gerontological Society · 2025Article
- Head and neck cancer patients with geriatric deficits are more often non-responders and lost from follow-up in quality of life studies.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Article
- Inverse probability weighting to handle attrition in cohort studies: some guidance and a call for caution.BMC medical research methodology · 2022Article
- Study retention and attrition in a longitudinal cohort study including patient-reported outcomes, fieldwork and biobank samples: results of the Netherlands quality of life and Biomedical cohort study (NET-QUBIC) among 739 head and neck cancer patients and 262 informal caregivers.BMC medical research methodology · 2022Article
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- Describing engagement practices for the Enhance Diversity Study using principles of Tailored Panel Management.New directions for evaluation · 2022Article
- Who Returns? Understanding Varieties of Longitudinal Participation in MIDUS.Journal of aging and health · 2021Article
- Preventing Cardiomyopathy in DMD: A Randomized Placebo-Controlled Drug Trial.Neurology. Clinical practice · 2021Article
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- Evaluation of the psychological burden during the early disease trajectory in patients with intracranial tumors by the ultra-brief Patient Health Questionnaire for Depression and Anxiety (PHQ-4).Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2019Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundAttrition occurs when a participant fails to respond to one or more study waves. The accumulation of attrition over several waves can lower the sample size and power and create a final sample that could differ in characteristics than those who drop out. The main reason to conduct a longitudinal study is to analyze repeated measures; research subjects who drop out cannot be replaced easily. Our group recently investigated factors affecting nonparticipation (refusal) in the first wave of a population-based study of prostate cancer. In this study we assess factors affecting attrition in the second wave of the same study. We compare factors affecting nonparticipation in the second wave to the ones affecting nonparticipation in the first wave.
methodsInformation available on participants in the first wave was used to model attrition. Different sources of attrition were investigated separately. The overall and race-stratified factors affecting attrition were assessed. Kaplan-Meier survival curve estimates were calculated to assess the impact of follow-up time on participation.
resultsHigh cancer aggressiveness was the main predictor of attrition due to death or frailty. Higher Charlson Comorbidity Index increased the odds of attrition due to death or frailty only in African Americans (AAs). Young age at diagnosis for AAs and low income for European Americans (EAs) were predictors for attrition due to lost to follow-up. High cancer aggressiveness for AAs, low income for EAs, and lower patient provider communication scores for EAs were predictors for attrition due to refusal. These predictors of nonparticipation were not the same as those in wave 1. For short follow-up time, the participation probability of EAs was higher than that of AAs.
conclusionsPredictors of attrition can vary depending on the attrition source. Examining overall attrition (combining all sources of attrition under one category) instead of distinguishing among its different sources should be avoided. The factors affecting attrition in one wave can be different in a later wave and should be studied separately.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.