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Analytical Methods for Observational Data to Generate Hypotheses and Inform Clinical Decisions

机译:用于产生假设的观测数据的分析方法,并提供临床决策

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Randomized controlled trials have been considered the gold standard in informing clinical decision-making while observational studies have generally been utilized to generate hypotheses for future studies. The rising cost of randomized studies along with increased difficulty in accrual has led the clinical community to consider utilizing observational studies to inform clinical decisions. Various statistical methods exist to analyze observational data. Researchers must consider each method carefully, paying specific attention to its ability to answer the hypotheses, while ensuring the underlying assumptions are met. While each has its own strengths and weaknesses, research has shown that each method may yield similar estimates of treatment effect when conducted appropriately. We describe several commonly used analytical methods including their: strengths, weaknesses, and common missteps in order to inform and serve as a reference to the broader oncology community. (C) 2019 Elsevier Inc. All rights reserved.
机译:随机对照试验已被认为是通知临床决策的黄金标准,而普遍用来为未来的研究产生假设的观察性研究。随机研究的成本上升以及应计的难度增加导致临床群落能够考虑利用观察研究来提供临床决策。存在各种统计方法来分析观察数据。研究人员必须仔细考虑各种方法,特别关注其回答假设的能力,同时确保符合潜在的假设。虽然每个都有自己的优点和缺点,但研究表明,当适当进行时,每种方法都可以产生类似的治疗效果估计。我们描述了几种常用的分析方法,包括:优势,劣势和常见的误解,以便为更广泛的肿瘤社区提供参考。 (c)2019 Elsevier Inc.保留所有权利。

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