比较估算行政健康数据因果治疗效果的方法:质谱模拟研究

IF 2 3区 医学 Q2 ECONOMICS
Health economics Pub Date : 2024-09-10 DOI:10.1002/hec.4891
Vanessa Ress, Eva-Maria Wild
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引用次数: 0

摘要

估算医疗政策干预措施的因果效应对政策制定至关重要,但由于缺乏方法指导,在使用真实世界的行政医疗数据时却面临挑战。为了填补这一空白,我们利用最近在德国一个贫困城市地区推出的一项政策措施中的此类数据进行了质谱模拟。我们的目的是评估和比较以下几种估算因果效应的方法:倾向得分匹配法、逆概率治疗加权法和熵平衡法,所有这些方法都与差异分析、增强逆概率加权法和目标最大似然估算相结合。此外,我们还使用回归模型和称为超级学习器的集合学习器估算了干扰参数。我们重点研究了与就诊次数、总医疗费用和住院治疗相关的治疗效果。虽然每种方法都有其优缺点,但我们的结果表明,当结合双重稳健估计方法来估计感兴趣的因果对比时,超级学习器通常能很好地处理大型协变量集中的滋扰项。相比之下,基于回归的滋扰参数估计与单稳健方法相结合时,在小型协变量集中效果最佳。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Comparing methods for estimating causal treatment effects of administrative health data: A plasmode simulation study

Comparing methods for estimating causal treatment effects of administrative health data: A plasmode simulation study

Estimating the causal effects of health policy interventions is crucial for policymaking but is challenging when using real-world administrative health care data due to a lack of methodological guidance. To help fill this gap, we conducted a plasmode simulation using such data from a recent policy initiative launched in a deprived urban area in Germany. Our aim was to evaluate and compare the following methods for estimating causal effects: propensity score matching, inverse probability of treatment weighting, and entropy balancing, all combined with difference-in-differences analysis, augmented inverse probability weighting, and targeted maximum likelihood estimation. Additionally, we estimated nuisance parameters using regression models and an ensemble learner called superlearner. We focused on treatment effects related to the number of physician visits, total health care cost, and hospitalization. While each approach has its strengths and weaknesses, our results demonstrate that the superlearner generally worked well for handling nuisance terms in large covariate sets when combined with doubly robust estimation methods to estimate the causal contrast of interest. In contrast, regression-based nuisance parameter estimation worked best in small covariate sets when combined with singly robust methods.

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来源期刊
Health economics
Health economics 医学-卫生保健
CiteScore
3.60
自引率
4.80%
发文量
177
审稿时长
4-8 weeks
期刊介绍: This Journal publishes articles on all aspects of health economics: theoretical contributions, empirical studies and analyses of health policy from the economic perspective. Its scope includes the determinants of health and its definition and valuation, as well as the demand for and supply of health care; planning and market mechanisms; micro-economic evaluation of individual procedures and treatments; and evaluation of the performance of health care systems. Contributions should typically be original and innovative. As a rule, the Journal does not include routine applications of cost-effectiveness analysis, discrete choice experiments and costing analyses. Editorials are regular features, these should be concise and topical. Occasionally commissioned reviews are published and special issues bring together contributions on a single topic. Health Economics Letters facilitate rapid exchange of views on topical issues. Contributions related to problems in both developed and developing countries are welcome.
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