Propensity score adjustment of a treatment effect with missing data in psychiatric health services research

B. Mayer, B. Puschner
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引用次数: 5

Abstract

Background: Missing values are a common problem for data analyses in observational studies, which are frequently applied in health services research. This paper examines the usefulness of different approaches to tackle the problem of incomplete observational data, focusing whether the Multiple Imputation (MI) strategy yields adequate estimates when applied to a complex analysis framework. Methods: Based on observational study data originally comparing three forms of psychotherapy, a simulation study with different missing data scenarios was conducted. The considered analysis model comprised a propensity score-adjusted treatment effect estimation. Missing values were handled by complete case analysis, different MI approaches, as well as mean and regression imputation. Results: All point estimators of the applied methods lay within the 95% confidence interval of the treatment effect derived from the complete simulation data set. Highest deviation was observed for complete case analysis. A distinct superiority of MI methods could not be demonstrated. Conclusion: Since there was no clear benefit of one method to deal with missing values over another, health services researchers faced with incomplete observational data are well-advised to apply different imputation methods and compare the results in order to get an impression of their sensitivity.
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精神卫生服务研究中数据缺失治疗效果的倾向评分调整
背景:在卫生服务研究中经常应用的观察性研究数据分析中,缺失值是一个常见的问题。本文探讨了解决观测数据不完整问题的不同方法的有用性,重点关注多重插值(MI)策略在应用于复杂分析框架时是否产生足够的估计。方法:基于最初比较三种心理治疗形式的观察性研究数据,进行了不同缺失数据情景的模拟研究。考虑的分析模型包括倾向得分调整后的治疗效果估计。通过完整的案例分析、不同的MI方法以及均值和回归插值来处理缺失值。结果:所采用方法的所有点估计量均位于由完整模拟数据集得出的治疗效果的95%置信区间内。在完整的病例分析中观察到最高的偏差。MI方法的明显优势不能被证明。结论:由于在处理缺失值时,没有一种方法明显优于另一种方法,因此,面对不完整的观测数据,卫生服务人员最好采用不同的归算方法,并对结果进行比较,以获得对其敏感性的印象。
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Epidemiology Biostatistics and Public Health
Epidemiology Biostatistics and Public Health PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
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期刊介绍: Epidemiology, Biostatistics, and Public Health (EBPH) is a multidisciplinary journal that has two broad aims: -To support the international public health community with publications on health service research, health care management, health policy, and health economics. -To strengthen the evidences on effective preventive interventions. -To advance public health methods, including biostatistics and epidemiology. EBPH welcomes submissions on all public health issues (including topics like eHealth, big data, personalized prevention, epidemiology and risk factors of chronic and infectious diseases); on basic and applied research in epidemiology; and in biostatistics methodology. Primary studies, systematic reviews, and meta-analyses are all welcome, as are research protocols for observational and experimental studies. EBPH aims to be a cross-discipline, international forum for scientific integration and evidence-based policymaking, combining the methodological aspects of epidemiology, biostatistics, and public health research with their practical applications.
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