Attrition and generalizability in longitudinal studies: findings from a 15-year population-based study and a Monte Carlo simulation study.
Attrition and generalizability in longitudinal studies: findings from a 15-year population-based study and a Monte Carlo simulation study.
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DOI：
10.1186/1471-2458-12-918
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发表时间：
2012-10-29
影响因子：
4.5
通讯作者：
Røysamb E
中科院分区：
文献类型：
--
作者：
Gustavson K;von Soest T;Karevold E;Røysamb E
损耗是纵向研究中主要的方法论问题之一。如果留在研究中的参与者与退出研究的参与者不同，可能会降低研究结果的普遍性。本研究的目的是检验损耗在多大程度上导致对变量的均值和它们之间的关联的有偏估计。1993年，18个月大婴儿的母亲被纳入了一项基于人群的研究（N=913），该研究旨在调查一般人群中儿童及其家庭的发育情况。15年后，56%的样本退出了。目前的研究考察了人员流失的预测因素，以及留下来的人和退出研究的人之间变量之间的基线关联。还进行了蒙特卡罗模拟研究。那些退出研究超过15年的人在基线时的教育水平低于那些继续研究的人，但他们在基线心理和关系变量方面没有差异。留下来的人和后来离开的人之间的基线相关性是一样的。模拟研究表明，即使在低流失率和流失率与后续变量之间只有弱依赖性的情况下，对平均值的估计也会出现偏差。只有当损耗同时依赖于基线和随访变量时，变量之间的关联估计才会出现偏差。流失率不影响变量间关联的估计。长期纵向研究对于研究风险/保护因素与健康结果之间的关系是有价值的，即使考虑到大量的损失率。
Attrition is one of the major methodological problems in longitudinal studies. It can deteriorate generalizability of findings if participants who stay in a study differ from those who drop out. The aim of this study was to examine the degree to which attrition leads to biased estimates of means of variables and associations between them. Mothers of 18-month-old children were enrolled in a population-based study in 1993 (N=913) that aimed to examine development in children and their families in the general population. Fifteen years later, 56% of the sample had dropped out. The present study examined predictors of attrition as well as baseline associations between variables among those who stayed and those who dropped out of that study. A Monte Carlo simulation study was also performed. Those who had dropped out of the study over 15 years had lower educational level at baseline than those who stayed, but they did not differ regarding baseline psychological and relationship variables. Baseline correlations were the same among those who stayed and those who later dropped out. The simulation study showed that estimates of means became biased even at low attrition rates and only weak dependency between attrition and follow-up variables. Estimates of associations between variables became biased only when attrition was dependent on both baseline and follow-up variables. Attrition rate did not affect estimates of associations between variables. Long-term longitudinal studies are valuable for studying associations between risk/protective factors and health outcomes even considering substantial attrition rates.
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