Caveat emptor: the combined effects of multiplicity and selective reporting.
Abstract
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.
Type
Journal articleSubject
HumansReproducibility of Results
Evidence-Based Medicine
Research Design
Randomized Controlled Trials as Topic
Data Accuracy
Clinical Decision-Making
Systematic Reviews as Topic
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https://hdl.handle.net/10161/18574Published Version (Please cite this version)
10.1186/s13063-018-2888-9Publication Info
Li, Tianjing; Mayo-Wilson, Evan; Fusco, Nicole; Hong, Hwanhee; & Dickersin, Kay (2018). Caveat emptor: the combined effects of multiplicity and selective reporting. Trials, 19(1). pp. 497. 10.1186/s13063-018-2888-9. Retrieved from https://hdl.handle.net/10161/18574.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
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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