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优化患者数据的使用以改善患者结局:用于慢性非癌性疼痛的麻醉药品

Optimizing the use of patient data to improve outcomes for patients: narcotics for chronic noncancer pain.

作者信息

Busse Jason W, Guyatt Gordon H

机构信息

Department of Clinical Epidemiology and Biostatistics, Health Sciences Centre, 1200 Main Street West, Room 2C12, McMaster University, Hamilton, ON, L8L 8E7, Canada.

出版信息

Expert Rev Pharmacoecon Outcomes Res. 2009 Apr;9(2):171-9. doi: 10.1586/erp.09.7.

Abstract

Randomized trials can provide important direction to clinical decision-making; however, their strength of inferences may be weakened by methodological limitations, the extent that their reported outcomes fail to address patient-important end points and by failing to report results that provide interpretable estimates of magnitude of effect. Strategies that investigators can use to address interpretability include reporting mean differences between groups in relation to the minimal important difference and reporting the proportion of patients who benefit from treatment and the associated number needed to treat. These strategies also apply to reporting pooled estimates from meta-analyses, even when studies use different instruments to measure the same construct. We illustrate these techniques using, as an example, current evidence for the use of opioids in chronic noncancer pain.

摘要

随机试验可为临床决策提供重要指导;然而,其推论的力度可能会因方法学局限性、所报告的结果未能涉及对患者重要的终点以及未能报告提供可解释效应大小估计值的结果而被削弱。研究人员可用于解决可解释性问题的策略包括报告组间平均差异与最小重要差异的关系,以及报告从治疗中获益的患者比例和相关的需治疗人数。这些策略也适用于报告荟萃分析的汇总估计值,即使各研究使用不同的工具来测量同一结构。我们以目前关于阿片类药物用于慢性非癌性疼痛的证据为例来说明这些技术。

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