贝叶斯模型平均:改进匹配病例对照研究的变量选择。

Q3 Nursing
Yi Mu, I. See, J. Edwards
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引用次数: 6

摘要

在统计实践中,风险因素建模的变量选择问题一直是一个挑战。选择探索性风险因素子集的经典方法在医学研究领域占主导地位。然而,这种方法由于没有考虑到模型选择过程本身的不确定性而受到批评。这种限制可以通过贝叶斯模型平均方法来解决:而不是专注于单个模型和几个因素,贝叶斯模型平均考虑所有具有不可忽略概率的模型来进行推理。方法本文报道了一项模拟研究,旨在模拟匹配的病例对照研究,并比较经典和贝叶斯模型平均选择方法。我们使用马修斯相关系数来衡量二元分类的质量。对2011-2013年耐甲氧西林金黄色葡萄球菌出院后感染患者的匹配病例对照研究,采用经典模型和贝叶斯模型平均进行对比分析。结果贝叶斯模型平均优于经典方法,具有更低的假阳性率和更高的马修相关分数。贝叶斯模型平均也产生了更可靠和稳健的效应估计。结论贝叶斯模型平均是一种概念简单、统一的方法,可以产生稳健的结果。它可用于医学研究中病例对照研究中有争议的p值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian model averaging: improved variable selection for matched case-control studies.
Background The problem of variable selection for risk factor modeling is an ongoing challenge in statistical practice. Classical methods that select one subset of exploratory risk factors dominate the medical research field. However, this approach has been criticized for not taking into account the uncertainty of the model selection process itself. This limitation can be addressed by a Bayesian model averaging approach: instead of focusing on a single model and a few factors, Bayesian model averaging considers all the models with non-negligible probabilities to make inference. Methods This paper reports on a simulation study designed to emulate a matched case-control study and compares classical versus Bayesian model averaging selection methods. We used Matthews's correlation coefficient to measure the quality of binary classifications. Both classical and Bayesian model averaging were also applied and compared for the analysis of a matched case-control study of patients with methicillin-resistant Staphylococcus aureus infections after hospital discharge 2011-2013. Results Bayesian model averaging outperformed the classical approach with much lower false positive rates and higher Matthew's correlation scores. Bayesian model averaging also produced more reliable and robust effect estimates. Conclusion Bayesian model averaging is a conceptually simple, unified approach that produces robust results. It can be used to replace controversial P-values for case-control study in medical research.
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来源期刊
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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