Research Unit for Gynecology and Obstetrics, Odense University Hospital, Odense, Denmark.
Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
Acta Obstet Gynecol Scand. 2018 Apr;97(4):407-416. doi: 10.1111/aogs.13319. Epub 2018 Mar 5.
Longitudinal cohort studies can provide important evidence about preventable causes of disease, but the success relies heavily on the commitment of their participants, both at recruitment and during follow up. Initial participation rates have decreased in recent decades as have willingness to participate in subsequent follow ups. It is important to examine how such selection affects the validity of the results. In this article, we describe the conceptual framework for selection bias due to nonparticipation and loss to follow up in cohort studies, using both a traditional epidemiological approach and directed acyclic graphs. Methods to quantify selection bias are introduced together with analytical strategies to adjust for the bias including controlling for covariates associated with selection, inverse probability weighting and bias analysis. We use several studies conducted in the Danish National Birth Cohort as examples of how to quantify selection bias and also understand the underlying selection mechanisms. Although women who chose to participate in this cohort were typically of higher social status, healthier and with less disease than all those eligible for study, differential selection was modest and the influence of selection bias on several selected exposure-outcome associations was limited. These findings are reassuring and support enrolling a subset of motivated participants who would engage in long-term follow up rather than prioritize representativeness. Some of the presented methods are applicable even with limited data on nonparticipants and those lost to follow up, and can also be applied to other study designs such as case-control studies and surveys.
纵向队列研究可以提供有关疾病可预防原因的重要证据，但成功在很大程度上依赖于参与者的承诺，无论是在招募时还是在随访期间。最近几十年来，初始参与率下降，参与后续随访的意愿也下降。重要的是要研究这种选择如何影响结果的有效性。本文使用传统的流行病学方法和有向无环图，描述了由于非参与和随访丢失导致的队列研究选择偏差的概念框架。本文介绍了量化选择偏差的方法以及分析策略，包括控制与选择相关的协变量、逆概率加权和偏差分析，以调整偏差。我们使用在丹麦全国出生队列中进行的几项研究，举例说明了如何量化选择偏差，以及如何理解潜在的选择机制。尽管选择参加该队列的女性通常具有较高的社会地位、更健康、疾病更少，但差异选择程度较小，选择偏差对一些选定的暴露-结局关联的影响有限。这些发现令人放心，并支持招募一部分有积极性的参与者，他们愿意进行长期随访，而不是优先考虑代表性。即使对非参与者和随访丢失者的资料有限，本文提出的一些方法仍然适用，并且也可应用于其他研究设计，如病例对照研究和调查。
