Browsing by Author "Li, Tianjing"

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    Caveat emptor: the combined effects of multiplicity and selective reporting.
    (Trials, 2018-09-17) Li, Tianjing; Mayo-Wilson, Evan; Fusco, Nicole; Hong, Hwanhee; Dickersin, Kay
    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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    Correction to: Integrating multiple data sources (MUDS) for meta-analysis to improve patient-centered outcomes research: a protocol for a systematic review.
    (Alzheimer's research & therapy, 2018-02-16) Mayo-Wilson, Evan; Hutfless, Susan; Li, Tianjing; Gresham, Gillian; Fusco, Nicole; Ehmsen, Jeffrey; Heyward, James; Vedula, Swaroop; Lock, Diana; Haythornthwaite, Jennifer; Payne, Jennifer L; Cowley, Theresa; Tolbert, Elizabeth; Rosman, Lori; Twose, Claire; Stuart, Elizabeth A; Hong, Hwanhee; Doshi, Peter; Suarez-Cuervo, Catalina; Singh, Sonal; Dickersin, Kay
    CORRECTION:The correct title of the article [1] should be "Integrating multiple data sources (MUDS) for meta-analysis to improve patient-centered outcomes research: a protocol". The article is a protocol for a methodological study, not a systematic review.
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    Hydroxychloroquine/chloroquine for the treatment of hospitalized patients with COVID-19: An individual participant data meta-analysis.
    (PloS one, 2022-01) Di Stefano, Leon; Ogburn, Elizabeth L; Ram, Malathi; Scharfstein, Daniel O; Li, Tianjing; Khanal, Preeti; Baksh, Sheriza N; McBee, Nichol; Gruber, Joshua; Gildea, Marianne R; Clark, Megan R; Goldenberg, Neil A; Bennani, Yussef; Brown, Samuel M; Buckel, Whitney R; Clement, Meredith E; Mulligan, Mark J; O'Halloran, Jane A; Rauseo, Adriana M; Self, Wesley H; Semler, Matthew W; Seto, Todd; Stout, Jason E; Ulrich, Robert J; Victory, Jennifer; Bierer, Barbara E; Hanley, Daniel F; Freilich, Daniel; Pandemic Response COVID-19 Research Collaboration Platform for HCQ/CQ Pooled Analyses

    Background

    Results from observational studies and randomized clinical trials (RCTs) have led to the consensus that hydroxychloroquine (HCQ) and chloroquine (CQ) are not effective for COVID-19 prevention or treatment. Pooling individual participant data, including unanalyzed data from trials terminated early, enables more detailed investigation of the efficacy and safety of HCQ/CQ among subgroups of hospitalized patients.

    Methods

    We searched ClinicalTrials.gov in May and June 2020 for US-based RCTs evaluating HCQ/CQ in hospitalized COVID-19 patients in which the outcomes defined in this study were recorded or could be extrapolated. The primary outcome was a 7-point ordinal scale measured between day 28 and 35 post enrollment; comparisons used proportional odds ratios. Harmonized de-identified data were collected via a common template spreadsheet sent to each principal investigator. The data were analyzed by fitting a prespecified Bayesian ordinal regression model and standardizing the resulting predictions.

    Results

    Eight of 19 trials met eligibility criteria and agreed to participate. Patient-level data were available from 770 participants (412 HCQ/CQ vs 358 control). Baseline characteristics were similar between groups. We did not find evidence of a difference in COVID-19 ordinal scores between days 28 and 35 post-enrollment in the pooled patient population (odds ratio, 0.97; 95% credible interval, 0.76-1.24; higher favors HCQ/CQ), and found no convincing evidence of meaningful treatment effect heterogeneity among prespecified subgroups. Adverse event and serious adverse event rates were numerically higher with HCQ/CQ vs control (0.39 vs 0.29 and 0.13 vs 0.09 per patient, respectively).

    Conclusions

    The findings of this individual participant data meta-analysis reinforce those of individual RCTs that HCQ/CQ is not efficacious for treatment of COVID-19 in hospitalized patients.