Caveat emptor: the combined effects of multiplicity and selective reporting.

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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.





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Li, Tianjing, Evan Mayo-Wilson, Nicole Fusco, Hwanhee Hong and Kay Dickersin (2018). Caveat emptor: the combined effects of multiplicity and selective reporting. Trials, 19(1). p. 497. 10.1186/s13063-018-2888-9 Retrieved from

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Hwanhee Hong

Associate Professor of Biostatistics & Bioinformatics

I am interested in developing Bayesian statistical methods for comparative effectiveness research, network meta-analysis, causal inference, measurement error, and generalizability. A flexible Bayesian modeling framework enables us to easily integrate different data sources and borrow information adaptively across them. The ultimate goal of my research is to provide comprehensive evidence from multiple data sources for answering clinical and scientific questions in public health and medicine.

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