From the MRC Centre for Causal Analyses in Translational Epidemiology, School of Social & Community Medicine, University of Bristol, Bristol, UK.
Epidemiology. 2013 Jan;24(1):1-9. doi: 10.1097/EDE.0b013e31827623b1.
Although cohort members tend to be healthy and affluent compared with the whole population, some studies indicate this does not bias certain exposure-outcome associations. It is less clear whether this holds when socioeconomic position (SEP) is the exposure of interest.
As an illustrative example, we use data from the Avon Longitudinal Study of Parents and Children. We calculate estimates of maternal education inequalities in outcomes for which data are available on almost the whole cohort (birth weight and length, breastfeeding, preterm birth, maternal obesity, smoking during pregnancy, educational attainment). These are calculated for the full cohort (n~12,000) and in restricted subsamples defined by continued participation at age 10 years (n∼7,000) and age 15 years (n∼5,000).
Loss to follow-up was related both to SEP and outcomes. For each outcome, loss to follow-up was associated with underestimation of inequality, which increased as participation rates decreased (eg, mean birth-weight difference between highest and lowest SEP was 116 g [95% confidence interval = 78 to 153] in the full sample and 93 g [45 to 141] and 62 g [5 to 119] in those attending at ages 10 and 15 years, respectively).
Considerable attrition from cohort studies may result in biased estimates of socioeconomic inequalities, and the degree of bias may worsen as participation rates decrease. However, even with considerable attrition (>50%), qualitative conclusions about the direction and approximate magnitude of inequalities did not change among most of our examples. The appropriate analysis approaches to alleviate bias depend on the missingness mechanism.
尽管队列成员与整个人群相比往往更健康、更富裕，但一些研究表明，这并不会影响某些暴露-结局关联。当社会经济地位（SEP）是感兴趣的暴露因素时，情况是否如此则不太清楚。
作为一个说明性的例子，我们使用了阿冯纵向研究父母和孩子的数据。我们计算了母亲教育不平等对几乎整个队列都有数据的结局的估计值（出生体重和身长、母乳喂养、早产、产妇肥胖、孕期吸烟、教育程度）。这些是根据整个队列（n~12000）和在 10 岁（n∼7000）和 15 岁（n∼5000）持续参与的受限子样本中计算得出的。
失访既与 SEP 又与结局有关。对于每个结局，失访与低估不平等有关，随着参与率的降低，这种低估程度增加（例如，在整个样本中，最高和最低 SEP 之间的平均出生体重差异为 116 克[95%置信区间=78 至 153]，而在 10 岁和 15 岁时参加的样本中，差异分别为 93 克[45 至 141]和 62 克[5 至 119]）。
队列研究的大量失访可能导致社会经济不平等的估计值存在偏差，并且随着参与率的降低，偏差程度可能会恶化。然而，即使存在相当大的失访率（>50%），我们大多数例子中的不平等的方向和大致幅度的定性结论也没有改变。减轻偏差的适当分析方法取决于缺失机制。
