Modelling attrition and nonparticipation in a longitudinal study of prostate cancer

Samantha Spiers, Evrim Oral, Elizabeth T.H. Fontham, Edward S. Peters, James L. Mohler, Jeannette T. Bensen, Christine S. Brennan

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Background: Attrition 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. Methods: Information 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. Results: High 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. Conclusions: Predictors 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.

Original languageEnglish (US)
Article number60
JournalBMC Medical Research Methodology
Volume18
Issue number1
DOIs
StatePublished - Jun 20 2018
Externally publishedYes

Keywords

  • Attrition
  • Longitudinal study
  • Nonresponse bias
  • Prostate cancer
  • Unit nonresponse

ASJC Scopus subject areas

  • Epidemiology
  • Health Informatics

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