Background Empirical evaluation of inverse probability weighting (IPW) for self-selection bias correction is inaccessible without the full source population. We aimed to: (i) investigate how self-selection biases frequency and association measures and (ii) assess self-selection bias correction using IPW in a cohort with register linkage. Methods The source population included 17 936 individuals invited to the Copenhagen Aging and Midlife Biobank during 2009–11 (ages 49–63 years). Participants counted 7185 (40.1%). Register data were obtained for every invited person from 7 years before invitation to the end of 2020. The association between education and mortality was estimated using Cox regression models among participants, IPW participants and the source population. Results Participants had higher socioeconomic position and fewer hospital contacts before baseline than the source population. Frequency measures of participants approached those of the source population after IPW. Compared with primary/lower secondary education, upper secondary, short tertiary, bachelor and master/doctoral were associated with reduced risk of death among participants (adjusted hazard ratio [95% CI]: 0.60 [0.46; 0.77], 0.68 [0.42; 1.11], 0.37 [0.25; 0.54], 0.28 [0.18; 0.46], respectively). IPW changed the estimates marginally (0.59 [0.45; 0.77], 0.57 [0.34; 0.93], 0.34 [0.23; 0.50], 0.24 [0.15; 0.39]) but not only towards those of the source population (0.57 [0.51; 0.64], 0.43 [0.32; 0.60], 0.38 [0.32; 0.47], 0.22 [0.16; 0.29]). Conclusions Frequency measures of study participants may not reflect the source population in the presence of self-selection, but the impact on association measures can be limited. IPW may be useful for (self-)selection bias correction, but the returned results can still reflect residual or other biases and random errors.

中文翻译：

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死亡率社会不平等调查中自我选择偏差校正的逆概率加权


背景 如果没有完整的源群体，就无法对用于自选择偏差校正的逆概率加权 (IPW) 进行实证评估。我们的目的是：(i) 研究自我选择如何影响频率和关联性测量，以及 (ii) 在具有登记关联的队列中使用 IPW 评估自我选择偏差校正。方法 来源人群包括 2009-11 年间受邀加入哥本哈根老龄化和中年生物库的 17 936 名个人（年龄 49-63 岁）。参与者人数为 7185 人（40.1%）。获得了每位受邀者从受邀前 7 年到 2020 年底的注册数据。使用参与者、IPW 参与者和源人群之间的 Cox 回归模型估计了教育与死亡率之间的关联。结果 与源人群相比，参与者在基线前具有更高的社会经济地位和更少的医院接触。 IPW 后参与者的频率测量值接近源人群的频率测量值。与小学/初中教育相比，高中、短期高等教育、学士和硕士/博士与参与者的死亡风险降低相关（调整后的风险比[95% CI]：0.60 [0.46; 0.77]、0.68 [0.42; 1.11] ]、0.37 [0.25；0.54]、0.28 [0.18；0.46]）。 IPW 略微改变了估计值（0.59 [0.45; 0.77]、0.57 [0.34; 0.93]、0.34 [0.23; 0.50]、0.24 [0.15; 0.39]），但不仅针对源人口的估计值（0.57 [0.51; 0.64]） 、0.43[0.32；0.60]、0.38[0.32；0.47]、0.22[0.16；0.29])。结论 在存在自我选择的情况下，研究参与者的频率测量可能无法反映源人群，但对关联测量的影响可能是有限的。 IPW 对于（自）选择偏差校正可能有用，但返回的结果仍然可以反映残差或其他偏差和随机误差。