骨科观察性临床研究中解决问题的简单设计与分析策略。

Kelsey E Brown, Michael J Flores, Gerard Slobogean, David Shearer, Ida Leah Gitajn, Saam Morshed
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引用次数: 0

摘要

随机对照试验是临床研究中建立因果关系的黄金标准。然而,这些研究既昂贵又耗时。本文旨在为骨科医生和临床研究人员提供在观察性研究中优化推理和最小化偏差的方法,这些研究通常更可行。为了减轻非实验设计引起的偏倚风险,研究人员必须首先了解测量协变量影响治疗、结果和随访数据缺失的方式。有了这些关系的知识,研究人员就可以建立因果关系图,以最好地了解如何控制偏见的来源。控制偏差的一些常用技术包括匹配、回归、分层和倾向评分分析。选择偏差可能是由于缺少随访和数据缺失造成的。诸如多重输入和事件时间分析等策略对于处理缺失非常有用。对于纵向数据,重复测量使观察性研究能够最好地总结干预措施随时间的影响。熟悉因果推理的基本概念和本文所述技术的临床研究人员将有能力提高从骨科创伤外科临床研究中得出的推论的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Simple design and analysis strategies for solving problems in observational orthopaedic clinical research.

Simple design and analysis strategies for solving problems in observational orthopaedic clinical research.

Simple design and analysis strategies for solving problems in observational orthopaedic clinical research.

Simple design and analysis strategies for solving problems in observational orthopaedic clinical research.

Randomized controlled trials are the gold standard to establishing causal relationships in clinical research. However, these studies are expensive and time consuming to conduct. This article aims to provide orthopaedic surgeons and clinical researchers with methodology to optimize inference and minimize bias in observational studies that are often much more feasible to undertake. To mitigate the risk of bias arising from their nonexperimental design, researchers must first understand the ways in which measured covariates can influence treatment, outcomes, and missingness of follow-up data. With knowledge of these relationships, researchers can then build causal diagrams to best understand how to control sources of bias. Some common techniques for controlling for bias include matching, regression, stratification, and propensity score analysis. Selection bias may result from loss to follow-up and missing data. Strategies such as multiple imputation and time-to-event analysis can be useful for handling missingness. For longitudinal data, repeated measures allow observational studies to best summarize the impact of the intervention over time. Clinical researchers familiar with fundamental concepts of causal inference and techniques reviewed in this article will have the power to improve the quality of inferences made from clinical research in orthopaedic trauma surgery.

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