观察研究
混淆
选择偏差
因果关系(物理学)
因果关系
医学
队列研究
研究设计
回顾性队列研究
数据收集
结果(博弈论)
临床研究设计
计量经济学
统计
临床试验
外科
数学
病理
物理
数理经济学
量子力学
政治学
法学
作者
Daniel I. Sessler,Peter B. Imrey
标识
DOI:10.1213/ane.0000000000000861
摘要
Case-control and cohort studies are invaluable research tools and provide the strongest feasible research designs for addressing some questions. Case-control studies usually involve retrospective data collection. Cohort studies can involve retrospective, ambidirectional, or prospective data collection. Observational studies are subject to errors attributable to selection bias, confounding, measurement bias, and reverse causation—in addition to errors of chance. Confounding can be statistically controlled to the extent that potential factors are known and accurately measured, but, in practice, bias and unknown confounders usually remain additional potential sources of error, often of unknown magnitude and clinical impact. Causality—the most clinically useful relation between exposure and outcome—can rarely be definitively determined from observational studies because intentional, controlled manipulations of exposures are not involved. In this article, we review several types of observational clinical research: case series, comparative case-control and cohort studies, and hybrid designs in which case-control analyses are performed on selected members of cohorts. We also discuss the analytic issues that arise when groups to be compared in an observational study, such as patients receiving different therapies, are not comparable in other respects.
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