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Modelling attrition and nonparticipation in a longitudinal study of prostate cancer
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 longi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6011525/ https://www.ncbi.nlm.nih.gov/pubmed/29925318 http://dx.doi.org/10.1186/s12874-018-0518-6 |
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author | Spiers, Samantha Oral, Evrim Fontham, Elizabeth T. H. Peters, Edward S. Mohler, James L. Bensen, Jeannette T. Brennan, Christine S. |
author_facet | Spiers, Samantha Oral, Evrim Fontham, Elizabeth T. H. Peters, Edward S. Mohler, James L. Bensen, Jeannette T. Brennan, Christine S. |
author_sort | Spiers, Samantha |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6011525 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-60115252018-07-05 Modelling attrition and nonparticipation in a longitudinal study of prostate cancer Spiers, Samantha Oral, Evrim Fontham, Elizabeth T. H. Peters, Edward S. Mohler, James L. Bensen, Jeannette T. Brennan, Christine S. BMC Med Res Methodol Research Article 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. BioMed Central 2018-06-20 /pmc/articles/PMC6011525/ /pubmed/29925318 http://dx.doi.org/10.1186/s12874-018-0518-6 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Spiers, Samantha Oral, Evrim Fontham, Elizabeth T. H. Peters, Edward S. Mohler, James L. Bensen, Jeannette T. Brennan, Christine S. Modelling attrition and nonparticipation in a longitudinal study of prostate cancer |
title | Modelling attrition and nonparticipation in a longitudinal study of prostate cancer |
title_full | Modelling attrition and nonparticipation in a longitudinal study of prostate cancer |
title_fullStr | Modelling attrition and nonparticipation in a longitudinal study of prostate cancer |
title_full_unstemmed | Modelling attrition and nonparticipation in a longitudinal study of prostate cancer |
title_short | Modelling attrition and nonparticipation in a longitudinal study of prostate cancer |
title_sort | modelling attrition and nonparticipation in a longitudinal study of prostate cancer |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6011525/ https://www.ncbi.nlm.nih.gov/pubmed/29925318 http://dx.doi.org/10.1186/s12874-018-0518-6 |
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