通过双重抽样估计缺少结果数据的加权分位数治疗效果。

IF 1.4 4区 数学 Q3 BIOLOGY
Biometrics Pub Date : 2025-04-02 DOI:10.1093/biomtc/ujaf038
Shuo Sun, Sebastien Haneuse, Alexander W Levis, Catherine Lee, David E Arterburn, Heidi Fischer, Susan Shortreed, Rajarshi Mukherjee
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引用次数: 0

摘要

当关注点位于反事实分布的尾部时,因果加权量子治疗效应(WQTE)是对标准的以平均值为重点的因果对比的补充。然而,估计和推断因果加权量子治疗效应的现有方法假定所有相关因素的数据都是完整的,而实际情况往往并非如此,特别是当数据不是出于研究目的而收集时,如电子健康记录(EHR)和疾病登记。此外,这些数据可能特别容易造成结果数据的非随机遗漏(MNAR)。本文建议使用双重抽样,即从研究单位的子样本中确定原本缺失的数据,以此作为一种策略,在估算因果性 WQTE 时减少因 MNAR 数据造成的偏差。利用附加数据,我们提出了不需要原始数据中缺失假设的识别条件。然后,我们提出了一种新颖的反概率加权估计器,并推导出其渐近特性,包括在特定量化点上的渐近特性,以及在某个紧凑子集(0,1)上均匀跨量化点的渐近特性,从而可以估计倾向得分和双重抽样概率。为了进行实际推断,我们开发了一种自举法,既可用于点推断,也可用于均匀推断。我们进行了一项模拟研究,以检验所提出的估计器的有限样本性能。我们使用电子病历数据说明了所提出的方法,该数据检验了两种减肥手术对术后 3 年体重指数下降的相对影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating weighted quantile treatment effects with missing outcome data by double sampling.

Causal weighted quantile treatment effects (WQTEs) complement standard mean-focused causal contrasts when interest lies at the tails of the counterfactual distribution. However, existing methods for estimating and inferring causal WQTEs assume complete data on all relevant factors, which is often not the case in practice, particularly when the data are not collected for research purposes, such as electronic health records (EHRs) and disease registries. Furthermore, these data may be particularly susceptible to the outcome data being missing-not-at-random (MNAR). This paper proposes to use double sampling, through which the otherwise missing data are ascertained on a sub-sample of study units, as a strategy to mitigate bias due to MNAR data in estimating causal WQTEs. With the additional data, we present identifying conditions that do not require missingness assumptions in the original data. We then propose a novel inverse-probability weighted estimator and derive its asymptotic properties, both pointwise at specific quantiles and uniformly across quantiles over some compact subset of (0,1), allowing the propensity score and double-sampling probabilities to be estimated. For practical inference, we develop a bootstrap method that can be used for both pointwise and uniform inference. A simulation study is conducted to examine the finite sample performance of the proposed estimators. We illustrate the proposed method using EHR data examining the relative effects of 2 bariatric surgery procedures on BMI loss 3 years post-surgery.

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来源期刊
Biometrics
Biometrics 生物-生物学
CiteScore
2.70
自引率
5.30%
发文量
178
审稿时长
4-8 weeks
期刊介绍: The International Biometric Society is an international society promoting the development and application of statistical and mathematical theory and methods in the biosciences, including agriculture, biomedical science and public health, ecology, environmental sciences, forestry, and allied disciplines. The Society welcomes as members statisticians, mathematicians, biological scientists, and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences. The Society sponsors the biennial International Biometric Conference, held in sites throughout the world; through its National Groups and Regions, it also Society sponsors regional and local meetings.
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