Li Tianjing, Mayo-Wilson Evan, Fusco Nicole, Hong Hwanhee, Dickersin Kay
Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD, 21205, USA.
Department of Biostatistics and Bioinformatics, Duke University School of Medicine, 2424 Erwin Road, Suite 1105, 11041 Hock Plaza, Durham, NC, 27705, USA.
Trials. 2018 Sep 17;19(1):497. doi: 10.1186/s13063-018-2888-9.
Clinical trials and systematic reviews of clinical trials inform healthcare decisions. There is growing concern, however, about results from clinical trials that cannot be reproduced. Reasons for nonreproducibility include that outcomes are defined in multiple ways, results can be obtained using multiple methods of analysis, and trial findings are reported in multiple sources ("multiplicity"). Multiplicity combined with selective reporting can influence dissemination of trial findings and decision-making. In particular, users of evidence might be misled by exposure to selected sources and overly optimistic representations of intervention effects. In this commentary, drawing from our experience in the Multiple Data Sources in Systematic Reviews (MUDS) study and evidence from previous research, we offer practical recommendations to enhance the reproducibility of clinical trials and systematic reviews.
临床试验及临床试验的系统评价为医疗决策提供依据。然而,人们越来越担心临床试验的结果无法重现。无法重现的原因包括结果有多种定义方式、可使用多种分析方法获得结果,以及试验结果在多个来源中报告(“多重性”)。多重性与选择性报告相结合会影响试验结果的传播和决策。特别是,证据使用者可能会因接触到选定的来源以及对干预效果过于乐观的表述而受到误导。在本评论中,我们借鉴系统评价中的多数据源(MUDS)研究经验以及以往研究的证据,提出切实可行的建议,以提高临床试验和系统评价的可重复性。