A Systematic Review of the Empirical Literature Evaluating IRBs: What We Know and What We Still Need to Learn

By Lura J. Abbott, C. Grady. 2011

283 citations recorded by the source corpus.

Institutional review boards (IRBs) are integral to the U.S. system of protection of human research participants. Evaluation of IRBs, although difficult, is essential. To date, no systematic review of IRB studies has been published. We conducted a systematic review of empirical studies of U.S. IRBs to determine what is known about the function of IRBs and to identify gaps in knowledge. A structured search in PubMed identified forty-three empirical studies evaluating U.S. IRBs. Studies were included if they reported an empirical investigation of the structure, process, outcomes, effectiveness, or variation of U.S. IRBs. The authors reviewed each study to extract information about study objectives, sample and methods, study results, and conclusions. Empirical evidence collected in forty-three published studies shows that for review of a wide range of types of research, U.S. IRBs differ in their application of the federal regulations, in the time they take to review studies, and in the decisions made. Existing studies show evidence of variation in multicenter review, inconsistent or ambiguous interpretation of the federal regulations, and inefficiencies in review. Despite recognition of a need to evaluate effectiveness of IRB review, no identified published study included an evaluation of IRB effectiveness. Multiple studies evaluating the structure, process, and outcome of IRB review in the United States have documented inconsistencies and inefficiencies. Efforts should be made to address these concerns. Additional research is needed to understand how IRBs accomplish their objectives, what issues they find important, what quality IRB review is, and how effective IRBs are at protecting human research participants.

Subfields: Economics and Econometrics, Public Health, Environmental and Occupational Health, Statistics and Probability, Evaluation & Benchmarks

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Citations in the corpus

Evaluating the Social Impact of Generative AI Systems in Systems and Society (2023, background): “Evaluating the effect AI has on people and societies, and evaluating people and groups themselves encounters similar challenges as those arising in sampling [20], surveying [126], determining preferences [270], and working with human subjects [131, 12], in addition to challenges that stem from the p”
Where are human subjects in Big Data research? The emerging ethics divide (2016, background): “…then applying those rules inconsistently across similar cases, which creates unfair burdens on researchers and expensive delays to research projects (Abbott and Grady, 2011; Committee on Revisions et al., 2014; Fost and Levine, 2007; Ledford, 2007; Rhodes et al., 2011; Silberman and Kahn, 2011).”