Kambara Manoj S, Sharma Shivam, Spouge John L, Jordan I King, Mariño-Ramírez Leonardo
National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland, USA.
IHRC-Georgia Tech Applied Bioinformatics Laboratory, Atlanta, Georgia, USA.
medRxiv. 2024 Oct 2:2024.10.02.24314774. doi: 10.1101/2024.10.02.24314774.
Large-scale population biobanks rely on volunteer participants, which may introduce biases that compromise the external validity of epidemiological studies. We characterized the volunteer participant bias for the Research Program cohort and developed a set of inverse probability (IP) weights that can be used to mitigate this bias. The cohort is older, more female, more educated, more likely to be covered by health insurance, less White, less likely to drink or smoke, and less healthy compared to the US population. IP weights developed via comparison of a nationally representative database eliminated the observed biases for all demographic and lifestyle characteristics and reduced the observed disease prevalence differences. IP weights also impact genetic associations with type 2 diabetes across diverse ancestry cohorts. We provide our IP weights as a community resource to increase the representativeness and external validity of the cohort.
大规模人群生物样本库依赖志愿者参与者，这可能会引入偏差，从而损害流行病学研究的外部有效性。我们对研究项目队列中的志愿者参与者偏差进行了特征描述，并开发了一组逆概率（IP）权重，可用于减轻这种偏差。与美国人群相比，该队列年龄更大、女性更多、受教育程度更高、更有可能享有医疗保险、白人更少、饮酒或吸烟的可能性更小，且健康状况更差。通过比较具有全国代表性的数据库开发的IP权重消除了所有人口统计学和生活方式特征中观察到的偏差，并减少了观察到的疾病患病率差异。IP权重还影响不同祖先队列中与2型糖尿病的遗传关联。我们将我们的IP权重作为社区资源提供，以提高该队列的代表性和外部有效性。
