Most epidemiologic cohorts are composed of volunteers who do not represent the general population. To improve population inference from cohorts, propensity-score (PS)-based matching methods, such as PS-based kernel weighting (KW) method, utilise probability survey samples as external references to develop PSs for membership in the cohort versus survey. We identify a strong exchangeability assumption (SEA) that underlies existing PS-based matching methods whose failure invalidates inferences, even if the propensity model is correctly specified. Herein, we develop a framework of propensity estimation and relax the SEA to a weak exchangeability assumption (WEA) for matching methods. To recover efficiency, we propose a scaled KW (KW.S) matching method by scaling survey weights in propensity estimation. We prove consistency of KW.S estimators of means/prevalences under WEA and provide consistent finite population variance estimators. In simulations, the KW.S estimators had smallest mean squared error (MSE). Our data example showed the KW estimates requiring the SEA had large bias, whereas the proposed KW.S estimates had the smallest MSE.

中文翻译：

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使用流行病学队列进行人口推断的有效且稳健的基于倾向评分的方法

大多数流行病学队列由不代表一般人群的志愿者组成。为了改进来自群组的人口推断，基于倾向得分 (PS) 的匹配方法，例如基于 PS 的核加权 (KW) 方法，利用概率调查样本作为外部参考来开发群组与调查中的成员资格的 PS。我们确定了一个强大的可交换性假设 (SEA)，它是现有基于 PS 的匹配方法的基础，即使正确指定了倾向模型，该方法的失败也会使推理无效。在这里，我们开发了一个倾向估计框架，并将 SEA 放宽为用于匹配方法的弱交换假设 (WEA)。为了恢复效率，我们通过在倾向估计中缩放调查权重提出了一种缩放的 KW (KW.S) 匹配方法。我们证明了 KW 的一致性。WEA 下均值/流行率的 S 估计量，并提供一致的有限总体方差估计量。在模拟中，KW.S 估计量的均方误差 (MSE) 最小。我们的数据示例显示，需要 SEA 的 KW 估计具有较大的偏差，而提议的 KW.S 估计具有最小的 MSE。