LSUHSC, School of Public Health, Biostatistics Program, New Orleans, USA.
LSUHSC, School of Public Health, Epidemiology Program, New Orleans, USA.
BMC Med Res Methodol. 2018 Jun 20;18(1):60. doi: 10.1186/s12874-018-0518-6.
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.
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.
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.
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.
失访是指参与者未能对一个或多个研究波次做出回应。经过多个波次的失访积累，样本量和效能可能会降低，并导致最终样本在特征上与那些中途退出的人有所不同。进行纵向研究的主要原因是分析重复测量；中途退出的研究对象不容易被替换。我们小组最近调查了影响前列腺癌人群基础研究第一波次非参与（拒绝）的因素。在这项研究中，我们评估了同一研究第二波次失访的影响因素。我们将影响第二波次非参与的因素与影响第一波次非参与的因素进行了比较。
利用第一波次参与者的可利用信息来构建失访模型。分别调查了不同的失访原因。评估了整体和种族分层因素对失访的影响。计算 Kaplan-Meier 生存曲线估计值，以评估随访时间对参与的影响。
高癌症侵袭性是因死亡或体弱导致失访的主要预测因素。较高的 Charlson 合并症指数仅增加了非洲裔美国人（AA）因死亡或体弱导致失访的几率。AA 组诊断时年龄较小和欧洲裔美国人（EA）收入较低是因失访导致失访的预测因素。AA 组癌症侵袭性高、EA 组收入低、EA 组患者与提供者沟通评分低是因拒绝而失访的预测因素。这些非参与的预测因素与第一波次的不同。对于随访时间较短的情况，EA 组的参与概率高于 AA 组。
失访的预测因素可能因失访原因而异。应避免将所有失访原因归为一类进行整体失访（总体失访）评估，而不是对其不同来源进行区分。一个波次中影响失访的因素在后续波次中可能会有所不同，应分别进行研究。
