The Differences in Quality of Life in Prostate Cancer Project: Methods and Design of a Multidisciplinary Population-Based Follow-up Study

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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Modelling attrition and nonparticipation in a longitudinal study of prostate cancer

Spiers

Oral

Fontham

et al. 2018

BMC Med Res Methodol

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Unit Nonresponse in a Population-Based Study of Prostate Cancer

Oral

Simonsen²,

Brennan

et al. 2016

PLoS ONE

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Low unit response rates can increase bias and compromise study validity. Response rates have continued to fall over the past decade despite all efforts to increase participation. Many factors have been linked to reduced response, yet relatively few studies have employed multivariate approaches to identify characteristics that differentiate respondents from nonrespondents since it is hard to collect information on the latter. We aimed to assess factors contributing to enrollment of prostate cancer (PCa) patients. We combined data from the North Carolina-Louisiana (LA) PCa Project’s LA cohort, with additional sources such as US census tract and LA tumor registry data. We included specific analyses focusing on blacks, a group often identified as hard to enroll in health-related research. The ability to study the effect of Hurricane Katrina, which occurred amidst enrollment, as a potential determinant of nonresponse makes our study unique. Older age (≥ 70) for blacks (OR 0.65) and study phase with respect to Hurricane Katrina for both races (OR 0.59 for blacks, OR 0.48 for whites) were significant predictors of participation with lower odds. Neighborhood poverty for whites (OR 1.53) also was a significant predictor of participation, but with higher odds. Among blacks, residence in Orleans parish was associated with lower odds of participation (OR 0.33) before Katrina. The opposite occurred in whites, with lower odds (OR 0.43) after Katrina. Our results overall underscore the importance of tailoring enrollment approaches to specific target population characteristics to confront the challenges posed by nonresponse. Our results also show that recruitment-related factors may change when outside forces bring major alterations to a population's environment and demographics.

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The Differences in Quality of Life in Prostate Cancer Project: Methods and Design of a Multidisciplinary Population-Based Follow-up Study

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Cited by 2 publications

References 42 publications

Modelling attrition and nonparticipation in a longitudinal study of prostate cancer

Modelling attrition and nonparticipation in a longitudinal study of prostate cancer

Unit Nonresponse in a Population-Based Study of Prostate Cancer

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