In talking about the tools that are needed to ensure that published findings are based on sound data and analyses, Goodman referenced a paper titled “Reproducible Epidemiologic Research” that proposes a standard for reproducibility (Peng et al., 2006). The premise behind that paper is that independent replication of research findings is the fundamental mechanism by which scientific evidence accumulates to support a hypothesis. The authors, therefore, argue that datasets and software should be made available to allow other researchers to conduct their own analyses and verify the published results.

Peter Doshi, a postdoctoral fellow at the Johns Hopkins University School of Medicine, also discussed the application of shared data to credible assessment of clinical trial results. Doshi, however, argued for a broader view of what should be considered clinical trial data. He proposed that detailed records of measurements and analyses, as well as narratives—including descriptions of patient dispositions, study protocols, and even correspondence—are needed to evaluate the quality of published trial results.

Data Sharing for Discovery

Participant-level data from multiple trials also can be combined to learn more than can be derived from the results of a single trial. Elizabeth Loder, clinical epidemiology editor at BMJ, observed that although meta-analyses historically have been done using summary-level data, the number of meta-analyses of individual participant data has been growing substantially. Furthermore, meta-analyses done with individual patient data are typically more likely to be able to detect treatment effects that differ across subgroups than meta-analyses done with aggregate data (Riley et al., 2010). These subgroup effects are frequently of great interest to clinical investigators. As Loder said, drawing from the title of an essay by Stephen Jay Gould, “the median is not the message.”


The arguments in favor of sharing can be divided into two broad and overlapping categories, Loder explained. The first category consists of moral and ethical arguments. These arguments point to the necessity of fulfilling obligations to research participants, minimizing known risks

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