For various reasons, cohort studies generally forgo probability sampling required to obtain population representative samples. However, such cohorts lack population representativeness, which invalidates estimates of population prevalences for novel health factors that are only available in cohorts. To improve external validity of estimates from cohorts, we propose a kernel weighting (KW) approach that uses survey data as a reference to create pseudoweights for cohorts. A jackknife variance is proposed for the KW estimates. In simulations, the KW method outperformed two existing propensity‐score‐based weighting methods in mean‐squared error while maintaining confidence interval coverage. We applied all methods to estimating US population mortality and prevalences of various diseases from the non‐representative US National Institutes of Health–American Association of Retired Persons cohort, using the sample from the US‐representative National Health Interview Survey as the reference. Assuming that the survey estimates are correct, the KW approach yielded generally less biased estimates compared with the existing propensity‐score‐based weighting methods.

中文翻译：

---

改善流行病学队列分析的外部有效性：核加权法

由于各种原因，队列研究通常放弃获得人群代表性样本所需的概率抽样。但是，此类人群缺乏人群代表性，这使得仅适用于人群的新型健康因素的人群患病率估算值无效。为了提高同类群组估算的外部有效性，我们提出了一种核仁权重（KW）方法，该方法使用调查数据作为创建同类群组的伪权重的参考。建议对KW估算采用折刀方差。在仿真中，在保持置信区间覆盖率的同时，KW方法在均方误差方面优于两种基于倾向得分的加权方法。我们使用来自美国代表性的美国国家卫生研究所-美国退休人员协会队列中的所有方法，来估计美国人口死亡率和各种疾病的患病率，并使用来自美国代表性的美国国民健康访问调查中的样本作为参考。假设调查估计是正确的，则与现有的基于倾向评分的加权方法相比，KW方法产生的偏差估计通常较小。