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Any discussion of the validity of peer review and science must ultimately grapple with ontology, the branch of philosophy that asks what actually exists, and epistemology, the branch of philosophy that asks what, if anything, we can know (9). Most scientists would be satisfied if they could achieve the epistemologic status of empirical truth revealed by objective measurements in an experiment. In this context, the term “objective” refers to information obtained without human intervention. But the design of an instrument and the interpretation of findings always involve a component of human judgment. In scientific epistemology, the most rigorous standard of empirical objectivity we can hope for is “intersubjective agreement” on the findings (10–13). The objectivity of scientific knowledge resides in its being a social construct: its communal institutions, norms, and activities, including the peer-review system, are not just a background for the logic of the scientific method—they are constitutive of science as we know it (13). Peer review, of course, can never guarantee validity, and being a human enterprise, peer review cannot be free of error, any more than can polygraph tracings or digital readouts. Scientific journals are archives of work done, not of revealed truth (14). It is time that decides validity.

Rigor of Peer Review and the Standing of a Journal

Shortly after launching The Philosophical Transactions of the Royal Society in 1665, Henry Oldenberg, the first editor, wrote to Robert Boyle that a submitted manuscript needs to be carefully scrutinized “before we give a publick testimony of it to ye world, as is desired of us” (1). Right from the start, peer review was used as an instrument of quality control to distinguish scientific journals from book publishing. Through the emergence of reviewers, journals could insist on the highest standards for excellence before publishing a report of new research findings. This exacting scrutiny by fellow experts is without parallel in any other field, and the reviewer has been described as “the lynchpin about which the whole business of science is pivoted” (2).

For a component of pivotal importance to the progress of science, journals provide scant guidance to reviewers. The confidential and anonymous nature of editorial peer review makes it especially difficult for the novice to learn the skill. In this issue of AJRCCM (pp. 1019–1023), Hoppin (3) describes how he reviews a manuscript. He considers the task one of the utmost responsibility, and writes with wisdom garnered from more than 30 years of experience. His essay is unique: I am not aware of another published description of how to review a manuscript and the cognitive processes involved. All reviewers will benefit from reading Hoppin’s essay, with resulting enhancement of the quality of their critiques.

The publication of an article in a journal represents not only the work of the authors, but it also reflects the standards of that journal. The standing of a journal among other journals is determined by the expectations and demands of the scientists who serve as reviewers. Because the reviewers also submit their own research to the journal, they embody the journal’s standards for scientific excellence. When reviewers believe that a journal publishes work of a low standard, they will be less demanding in their critiques, the journal will publish manuscripts of lower quality, and its standing will fall. Authors regard reviewers as hurdles to vault before their work gets published and find it hard to see them as offering help. Readers, however, depend on reviewers to scrutinize a manuscript and guarantee them it will be worth the time invested in reading it. Readers also benefit from the improvements made to articles through the intellectual capital added by reviewers; in turn, the effort made by the reviewers depends on their assessment of the role of that journal in the advancement of science. And rounding this positive feedback loop, journals that employ the toughest reviewing procedures are the most attractive to authors who hold themselves to the highest standards.

Editorial peer review has always had its critics. At its dawn, Isaac Newton was complaining that addressing criticisms of his submission to The Philosophical Transactions “had sacrificed my peace (of mind), a matter of real substance” (4). Research into peer review has only gotten underway in the last 10 to 15 years and has been slow to uncover major benefits (5–7). These studies have been interpreted to mean that editorial peer review has no value (8). Absence of proof, however, is not proof of absence. It is not possible to do the definitive outcome study: to randomize two continents to the stricture, or lack thereof, that the results of all experiments be published in peer-reviewed journals and then to follow the progress of science in each continent for 300 years. Moreover, research into peer review has not included rigorous studies of its most critical and ineffable components: the making of subjective value judgments, and the effect on manuscript quality and relevance.

Any discussion of the validity of peer review and science must ultimately grapple with ontology, the branch of philosophy that asks what actually exists, and epistemology, the branch of philosophy that asks what, if anything, we can know (9). Most scientists would be satisfied if they could achieve the epistemologic status of empirical truth revealed by objective measurements in an experiment. In this context, the term “objective” refers to information obtained without human intervention. But the design of an instrument and the interpretation of findings always involve a component of human judgment. In scientific epistemology, the most rigorous standard of empirical objectivity we can hope for is “intersubjective agreement” on the findings (10–13). The objectivity of scientific knowledge resides in its being a social construct: its communal institutions, norms, and activities, including the peer-review system, are not just a background for the logic of the scientific method—they are constitutive of science as we know it (13). Peer review, of course, can never guarantee validity, and being a human enterprise, peer review cannot be free of error, any more than can polygraph tracings or digital readouts. Scientific journals are archives of work done, not of revealed truth (14). It is time that decides validity.

Online peer review has reduced the time between submission and first decision to 33 days at AJRCCM. More important than speed, the online system has enhanced the rigor of peer review. Formerly, we assigned a manuscript to two reviewers, but now commonly assign four or more reviewers. The descriptor number on a manuscript allows us to instantly identify the reviewers with the greatest expertise in each of the 172 areas covered by AJRCCM. We have more than 5,600 reviewers in our database, and associate editors can assess level of expertise and prior performance by clicking a mouse when assigning manuscripts. New reviewers are constantly added to the database, and we also maintain a list of delinquent and superficial reviewers. The internet is revolutionizing the speed of processing manuscripts and the dissemination of new findings, but the bedrock of science has not changed since the 1660s: experiments are converted into science only after the results have been published in a peer-reviewed journal. This point is well illustrated by Leonardo da Vinci, perhaps the finest scientific mind ever. Leonardo’s notebooks are records of the most intense scientific efforts by a single individual. But his research efforts came to nothing, because the stricture for disciplined publication was not formulated until more than 150 years after Leonardo’s death.

He highlights an analysis of nine drugs that showed promise for treating amyotrophic lateral sclerosis (ALS) in animal studies. Eight of the drugs failed to slow the deadly neurodegenerative disease in people 2 .

Publish or perish:

Most scientists don’t intentionally conduct shoddy experiments, Harris notes. They want to produce findings that benefit society. But science has evolved in ways that make it difficult for them to follow that dream. Funding is scarce and the pressure to publish in high-impact journals is intense, he writes. As a result, scientists prioritize studies that are likely to make a splash over less sexy but perhaps more rigorous work.

Through interviews with researchers and journal editors, Harris learns that the pressure to publish and secure precious grant money drives scientists to engage in behaviors that undermine the reliability of their work. These include ignoring data that don’t fit with their theories, massaging statistics and overhyping results. Some scientists fabricate data outright.

Once a flawed study is published, there is no incentive for other scientists to attempt to replicate its results or point out its flaws, because they must spend their time and money developing and publishing their own research if they wish to advance their careers. And there is no way for scientists to admit their mistakes without damaging their reputations.

Watchdogs and U.S. lawmakers have become aware of the problem, spurring funding agencies to address the issue. The NIH, for instance, has spelled out expectations for how grantees should ensure rigor in their research. The rules include authenticating materials such as antibodies and cell lines, and accounting for gender. Dutch authorities announced last year that they would invest 3 million euros to replicate studies.

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In multi-arm study designs, if participants are randomly assigned to the intervention and control/comparison arm, this criterion is met. This is true for both individual and group randomized designs. If participants self-select into the intervention or if assignment to the intervention is not random, this criterion is not met. If the study only has an intervention arm, this criterion should be listed as not applicable.

Discussion

The Evidence Project tool assesses risk of bias in a range of different study designs with moderate to substantial reliability. This tool is one of many existing tools that systematic reviewers and others can select from. Viswanathan et al. [3] advocate that systematic reviewers should consider the following general principles when selecting a tool: (a) it should be specifically designed for use in systematic reviews, (b) be specific to the study designs being evaluated, (c) show transparency in how assessments are made, (d) address risk-of-bias categories through specifically related items, and (e) be based on theory or, ideally, empirical evidence. We believe our tool meets these criteria, though like any other tool, it has strengths and weaknesses and should be selected when it best meets the needs of a given review.

One strength of the Evidence Project risk of bias tool is its applicability to a range of study designs, from RCTs to case-control studies to cohorts to pre-post studies, and including both prospective and retrospective studies. Previous reviews have found that the majority (87%) of existing risk of bias tools are design-specific [4], although there may be clear benefits to including a range of study designs in a given systematic review [39]. This aspect also allows the tool to be used across a range of topics, thus facilitating comparison across topics; for example, we have found that some HIV prevention interventions (such as Condom Social Marketing [25]) rarely use RCTs, while other topics (such as school-based sex education [13]) are much more likely to do so. Our risk of bias tool highlights these differences when compared across reviews. Also facilitating comparability across reviews is the fact that the tool does not need to be adapted for each review, or for each included study. This distinguishes it from tools such as ROBINS-I [40], which asks reviewers to assess bias separately for each outcome included in each study (which may differ across studies and across review topics), or the Newcastle-Ottawa scale [41], which asks reviewers to select the most important factor for which studies should control (which may differ across review topics).

Other strengths of the Evidence Project risk of bias tool include its relative ease of use and clarity. The eight items are fairly straightforward and easy to assess, which should make data extraction less prone to error and easier for reviewers with less experience. The tool is also relatively easy for readers to interpret and read, as all information can be condensed into a single table with one row per study.

However, our tool also has some limitations. Some items, as noted above, may capture elements based on study features other than bias differentially across studies. For example, length of follow-up, which differs across studies, affects the 80% retention cutoff. Similarly, sample size and the choice of sociodemographic or outcome variables may both affect whether comparison groups are equivalent on these measures. While these items could be adapted for individual reviews, that would reduce the consistency across topics noted above.

Second, while our decision to change the tool to a simple checklist, rather than a checklist with a summary (numerical) judgment, avoids criticisms of summary scores, Viswanathan et al. have recently noted that this approach “devolves the burden of interpretation of a study’s risk of bias from the systematic reviewer to the reader.” [3] When we did present a summary score, readers found it easy to see differences in overall quality across included articles; without the summary score, we feel it has become more difficult to succinctly communicate overall risk of bias in presentation of the review results. An alternative may be to use individual items in the scale to create general categories, where studies could be ranked as “low,” “medium,” and “high” risk of bias. We have not done this to date, as the different items and domains do not assess an equal risk of bias; however, it could be considered by others using the tool.

Third, the Evidence Project risk of bias tool does not capture some elements of quality that other tools assess. For example, ROBINS-I [40] assesses bias in the classification of interventions, deviations from intended interventions, measurement of outcomes, and selection of the reported results. The Newcastle-Ottawa scale [41] considers items such as the case definition (for case-control studies) and ascertainment of exposure. The Cochrane Risk of Bias tool [42] includes items such as random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, and selective reporting. For the Evidence Project, we focus on behavioral interventions that are often impossible to blind, and with few RCTs included in our reviews, items such as random sequence generation and allocation concealment are rare. In line with recommendations to “select the most important categories of bias for the outcome(s) and topic at hand” [3], we have found the categories in our risk of bias tool to be useful for an overall assessment of the diverse types of studies we see in the field of HIV behavioral interventions in low- and middle-income countries.

Inter-rater reliability was moderate to substantial for all items in our tool individually, and the median inter-rater reliability across items was substantial. This compares favorably to other risk of bias tools. Assessing the Cochrane Risk of Bias tool, Harding et al. found inter-rater agreement ranged from slight (κ = 0.13) to substantial (κ = 0.74) across items [33], while Armijo-Olivo et al. found inter-rater reliability was poor for both the overall score (κ = 0.02) and individual items (median κ = 0.19, range − 0.04 to 0.62). The Newcastle-Ottawa score has similarly been found to have fair inter-rater reliability overall (κ = 0.29), with individual items ranging from substantial (κ = 0.68) to poor (κ = − 0.06) [9]. The relative ease of use and clarity of items on our tool likely increased its reliability. However, as both reviewers were from the same study team, our inter-rater reliability results may have been more consistent than would be expected if the tool were applied by members of different groups. Several studies have found consistency may be even lower across different groups, such as Cochrane reviewers and blinded external reviewers [7] or across consensus assessments of reviewer pairs [8].

The Evidence Project risk of bias tool has been used in over 30 systematic reviews to date, including both Evidence Project publications [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27] and other systematic reviews not connected with the Evidence Project [43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58]. Some of these reviews have changed the tools’ criteria slightly—for example, by using a 75% instead of 80% cutoff [44, 48, 49, 52, 54] or by adding an extra item for whether the study adjusted for confounding variables [44, 46, 48, 49, 52,53,54]. The Evidence Project risk of bias tool has been used in reviews of a range of topics, including in Cochrane reviews [14, 52] and reviews to inform World Health Organization guidelines [43,44,45,46,47,48, 50, 53]. We believe this widespread use in reputable settings, including by researchers outside our study team, provides at least some indication that others feel the tool is useful and has face validity.


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