Taxonomic and geographic bias in 50 years of research on the behaviour and ecology of galagids.

Identifying knowledge gaps and taxonomic and geographic bias in the literature is invaluable for guiding research towards a more representative understanding of animal groups. Galagids are nocturnal African primates and, for many species, detailed information on their behaviour and ecology is unavailable. To identify gaps and bias in the literature we reviewed published peer-reviewed research articles on galagid behaviour and ecology over a 50-year period from January 1971 to December 2020. Using the Web of Science and Google Scholar databases, we identified 758 articles, assessed 339 full texts for eligibility and included 211 in the review. Species of Otolemur have been extensively researched in comparison to other genera (78.2% of studies; Euoticus: 13.3% of studies; Galago: 66.4% of studies; Galagoides: 20.9% of studies; Paragalago: 22.3% of studies; Sciurocheirus: 15.2% of studies). The most common category of research was physiology (55.0% of studies), followed by behavioural ecology (47.4% of studies), and fewer studies were on genetics and taxonomy (16.1% of studies) and habitat and distribution (14.2% of studies). Text mining revealed that the word 'behaviour' was the most common word used in abstracts and keywords, and few words were related to ecology. Negative binomial regression revealed that mean body mass and geographic range size were significant positive predictors of the total number of scientific outputs on each species. Research on wild populations was carried out in only 24 (60%) of the 40 countries galagids are thought to inhabit. Studies were undertaken in locations with lower mean annual temperatures and higher human population densities over warmer and less populated areas. We encourage a more equal sampling effort both taxonomically and geographically that in particular addresses the paucity of research on smaller species and those with restricted ranges. Research on in situ populations, especially in warmer and remote areas, is urgently needed, particularly in West, Central and some Southern African countries.

Publication of Positive Studies in the Urologic Literature.

OBJECTIVE: To assessed rates of positive publications within the urologic literature, comparing the years 2012 and 2017. MATERIALS AND METHODS: All studies published in Journal of Urology, Neurourology and Urodynamics, Urologic Oncology, Journal of Endourology, and Urology in 2012 and 2017 were reviewed. The primary study outcome was proportion of positive studies. Additional article characteristics, including associated citations and subspecialty focus, were recorded and statistical analyses used to assess for differences in negative publication rates based on these variables. RESULTS: A total of 1,796 articles meeting inclusion criteria were analyzed (2012, 959; 2017, 837). The overall proportion of positive studies decreased in comparison of 2012 and 2017. (90%-86%, P =.01). A statistically significant decrease was seen in 2 of 5 journals: Neurourology and Urodynamics (97%-87%, P = .01) and Journal of Endourology (93%-83%, P <.01). There were no significant differences in associated citations for positive vs negative studies in either year. Logistic regression focused on year and journal revealed that studies published in 2017 and Urology were more likely to be negative. CONCLUSION: The vast majority of studies within the urologic literature are positive, with only a small increase in negative study publication comparing 2012 vs 2017. Continued efforts are needed to identify publication bias and promote dissemination of negative research findings.

Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions.

BACKGROUND: The purpose of the present meta-analysis was to provide evident data about use of Apparent Diffusion Coefficient (ADC) values for distinguishing malignant and benign breast lesions. METHODS: MEDLINE library and SCOPUS database were screened for associations between ADC and malignancy/benignancy of breast lesions up to December 2018. Overall, 123 items were identified. The following data were extracted from the literature: authors, year of publication, study design, number of patients/lesions, lesion type, mean value and standard deviation of ADC, measure method, b values, and Tesla strength. The methodological quality of the 123 studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for benign and malign lesions. RESULTS: The acquired 123 studies comprised 13,847 breast lesions. Malignant lesions were diagnosed in 10,622 cases (76.7%) and benign lesions in 3225 cases (23.3%). The mean ADC value of the malignant lesions was 1.03 × 10- 3 mm2/s and the mean value of the benign lesions was 1.5 × 10- 3 mm2/s. The calculated ADC values of benign lesions were over the value of 1.00 × 10- 3 mm2/s. This result was independent on Tesla strength, choice of b values, and measure methods (whole lesion measure vs estimation of ADC in a single area). CONCLUSION: An ADC threshold of 1.00 × 10- 3 mm2/s can be recommended for distinguishing breast cancers from benign lesions.

Reporting trends of p values in the neurosurgical literature.

OBJECTIVE: The objective of this study was to evaluate the trends in reporting of p values in the neurosurgical literature from 1990 through 2017. METHODS: All abstracts from the Journal of Neurology, Neurosurgery, and Psychiatry (JNNP), Journal of Neurosurgery (JNS) collection (including Journal of Neurosurgery: Spine and Journal of Neurosurgery: Pediatrics), Neurosurgery (NS), and Journal of Neurotrauma (JNT) available on PubMed from 1990 through 2017 were retrieved. Automated text mining was performed to extract p values from relevant abstracts. Extracted p values were analyzed for temporal trends and characteristics. RESULTS: The search yielded 47,889 relevant abstracts. A total of 34,324 p values were detected in 11,171 abstracts. Since 1990 there has been a steady, proportionate increase in the number of abstracts containing p values. There were average absolute year-on-year increases of 1.2% (95% CI 1.1%-1.3%; p < 0.001), 0.93% (95% CI 0.75%-1.1%; p < 0.001), 0.70% (95% CI 0.57%-0.83%; p < 0.001), and 0.35% (95% CI 0.095%-0.60%; p = 0.0091) of abstracts reporting p values in JNNP, JNS, NS, and JNT, respectively. There have also been average year-on-year increases of 0.045 (95% CI 0.031-0.059; p < 0.001), 0.052 (95% CI 0.037-0.066; p < 0.001), 0.042 (95% CI 0.030-0.054; p < 0.001), and 0.041 (95% CI 0.026-0.056; p < 0.001) p values reported per abstract for these respective journals. The distribution of p values showed a positive skew and strong clustering of values at rounded decimals (i.e., 0.01, 0.02, etc.). Between 83.2% and 89.8% of all reported p values were at or below the "significance" threshold of 0.05 (i.e., p ≤ 0.05). CONCLUSIONS: Trends in reporting of p values and the distribution of p values suggest publication bias remains in the neurosurgical literature.

Evidence that nonsignificant results are sometimes preferred: Reverse P-hacking or selective reporting?

There is increased concern about poor scientific practices arising from an excessive focus on P-values. Two particularly worrisome practices are selective reporting of significant results and 'P-hacking'. The latter is the manipulation of data collection, usage, or analyses to obtain statistically significant outcomes. Here, we introduce the novel, to our knowledge, concepts of selective reporting of nonsignificant results and 'reverse P-hacking' whereby researchers ensure that tests produce a nonsignificant result. We test whether these practices occur in experiments in which researchers randomly assign subjects to treatment and control groups to minimise differences in confounding variables that might affect the focal outcome. By chance alone, 5% of tests for a group difference in confounding variables should yield a significant result (P < 0.05). If researchers less often report significant findings and/or reverse P-hack to avoid significant outcomes that undermine the ethos that experimental and control groups only differ with respect to actively manipulated variables, we expect significant results from tests for group differences to be under-represented in the literature. We surveyed the behavioural ecology literature and found significantly more nonsignificant P-values reported for tests of group differences in potentially confounding variables than the expected 95% (P = 0.005; N = 250 studies). This novel, to our knowledge, publication bias could result from selective reporting of nonsignificant results and/or from reverse P-hacking. We encourage others to test for a bias toward publishing nonsignificant results in the equivalent context in their own research discipline.

Using risk of bias domains to identify opportunities for improvement in food- and nutrition-related research: An evaluation of research type and design, year of publication, and source of funding.

PURPOSE: This retrospective cross-sectional study aimed to identify opportunities for improvement in food and nutrition research by examining risk of bias (ROB) domains. METHODS: Ratings were extracted from critical appraisal records for 5675 studies used in systematic reviews conducted by three organizations. Variables were as follows: ROB domains defined by the Cochrane Collaboration (Selection, Performance, Detection, Attrition, and Reporting), publication year, research type (intervention or observation) and specific design, funder, and overall quality rating (positive, neutral, or negative). Appraisal instrument questions were mapped to ROB domains. The kappa statistic was used to determine consistency when multiple ROB ratings were available. Binary logistic regression and multinomial logistic regression were used to predict overall quality and ROB domains. FINDINGS: Studies represented a wide variety of research topics (clinical nutrition, food safety, dietary patterns, and dietary supplements) among 15 different research designs with a balance of intervention (49%) and observation (51%) types, published between 1930 and 2015 (64% between 2000-2009). Duplicate ratings (10%) were consistent (κ = 0.86-0.94). Selection and Performance domain criteria were least likely to be met (57.9% to 60.1%). Selection, Detection, and Performance ROB ratings predicted neutral or negative quality compared to positive quality (p<0.001). Funder, year, and research design were significant predictors of ROB. Some sources of funding predicted increased ROB (p<0.001) for Selection (interventional: industry only and none/not reported; observational: other only and none/not reported) and Reporting (observational: university only and other only). Reduced ROB was predicted by combined and other-only funding for intervention research (p<0.005). Performance ROB domain ratings started significantly improving in 2000; others improved after 1990 (p<0.001). Research designs with higher ROB were nonrandomized intervention and time series designs compared to RCT and prospective cohort designs respectively (p<0.001). CONCLUSIONS: Opportunities for improvement in food and nutrition research are in the Selection, Performance, and Detection ROB domains.

Wireless Phone Use and Risk of Adult Glioma: Evidence from a Meta-Analysis.

BACKGROUND: Wireless phone use has been increasing rapidly and is associated with the risk of glioma. Many studies have been conducted on this association without reaching agreement. The aim of this meta-analysis was to determine the possible association between wireless phone use and risk of adult glioma. METHODS: Eligible studies were identified by searching PubMed and Embase up to July 2017. Random-effects or fixed-effects model was used to combine the results depending on the heterogeneity of the analysis. Publication bias was evaluated using Begg's funnel plot and Egger's regression asymmetry test. Subgroup analysis was performed to evaluate possible influence of these variables. RESULTS: Ten studies on the association of wireless phone use and risk of glioma were included. The combined odds ratio of adult gliomas associated with ever use of wireless phones was 1.03 (95% confidence interval [CI], 0.92-1.16) with high heterogeneity (I2 = 54.2%, P = 0.013). In subgroup analyses, no significant association was found between tumor location in the temporal lobe and adult glioma risk, with odds ratios of 1.26 (95% CI, 0.87-1.84), 0.93 (95% CI, 0.69-1.24), and 1.61 (95% CI, 0.78-3.33). A significant association with risk of glioma was found in long-term users (≥10 years) with odds ratio of 1.33 (95% CI, 1.05-1.67). CONCLUSIONS: Ever use of wireless phones was not significantly associated with risk of adult glioma, but there could be increased risk in long-term users.

Tobacco smoking and gastric cancer: meta-analyses of published data versus pooled analyses of individual participant data (StoP Project).

Tobacco smoking is one of the main risk factors for gastric cancer, but the magnitude of the association estimated by conventional systematic reviews and meta-analyses might be inaccurate, due to heterogeneous reporting of data and publication bias. We aimed to quantify the combined impact of publication-related biases, and heterogeneity in data analysis or presentation, in the summary estimates obtained from conventional meta-analyses. We compared results from individual participant data pooled-analyses, including the studies in the Stomach Cancer Pooling (StoP) Project, with conventional meta-analyses carried out using only data available in previously published reports from the same studies. From the 23 studies in the StoP Project, 20 had published reports with information on smoking and gastric cancer, but only six had specific data for gastric cardia cancer and seven had data on the daily number of cigarettes smoked. Compared to the results obtained with the StoP database, conventional meta-analyses overvalued the relation between ever smoking (summary odds ratios ranging from 7% higher for all studies to 22% higher for the risk of gastric cardia cancer) and yielded less precise summary estimates (SE ≤2.4 times higher). Additionally, funnel plot asymmetry and corresponding hypotheses tests were suggestive of publication bias. Conventional meta-analyses and individual participant data pooled-analyses reached similar conclusions on the direction of the association between smoking and gastric cancer. However, published data tended to overestimate the magnitude of the effects, possibly due to publication biases and limited the analyses by different levels of exposure or cancer subtypes.

Reviewer bias in single- versus double-blind peer review.

Peer review may be "single-blind," in which reviewers are aware of the names and affiliations of paper authors, or "double-blind," in which this information is hidden. Noting that computer science research often appears first or exclusively in peer-reviewed conferences rather than journals, we study these two reviewing models in the context of the 10th Association for Computing Machinery International Conference on Web Search and Data Mining, a highly selective venue (15.6% acceptance rate) in which expert committee members review full-length submissions for acceptance. We present a controlled experiment in which four committee members review each paper. Two of these four reviewers are drawn from a pool of committee members with access to author information; the other two are drawn from a disjoint pool without such access. This information asymmetry persists through the process of bidding for papers, reviewing papers, and entering scores. Reviewers in the single-blind condition typically bid for 22% fewer papers and preferentially bid for papers from top universities and companies. Once papers are allocated to reviewers, single-blind reviewers are significantly more likely than their double-blind counterparts to recommend for acceptance papers from famous authors, top universities, and top companies. The estimated odds multipliers are tangible, at 1.63, 1.58, and 2.10, respectively.

Publication Bias and Nonreporting Found in Majority of Systematic Reviews and Meta-analyses in Anesthesiology Journals.

BACKGROUND: Systematic reviews and meta-analyses are used by clinicians to derive treatment guidelines and make resource allocation decisions in anesthesiology. One cause for concern with such reviews is the possibility that results from unpublished trials are not represented in the review findings or data synthesis. This problem, known as publication bias, results when studies reporting statistically nonsignificant findings are left unpublished and, therefore, not included in meta-analyses when estimating a pooled treatment effect. In turn, publication bias may lead to skewed results with overestimated effect sizes. The primary objective of this study is to determine the extent to which evaluations for publication bias are conducted by systematic reviewers in highly ranked anesthesiology journals and which practices reviewers use to mitigate publication bias. The secondary objective of this study is to conduct publication bias analyses on the meta-analyses that did not perform these assessments and examine the adjusted pooled effect estimates after accounting for publication bias. METHODS: This study considered meta-analyses and systematic reviews from 5 peer-reviewed anesthesia journals from 2007 through 2015. A PubMed search was conducted, and full-text systematic reviews that fit inclusion criteria were downloaded and coded independently by 2 authors. Coding was then validated, and disagreements were settled by consensus. In total, 207 systematic reviews were included for analysis. In addition, publication bias evaluation was performed for 25 systematic reviews that did not do so originally. We used Egger regression, Duval and Tweedie trim and fill, and funnel plots for these analyses. RESULTS: Fifty-five percent (n = 114) of the reviews discussed publication bias, and 43% (n = 89) of the reviews evaluated publication bias. Funnel plots and Egger regression were the most common methods for evaluating publication bias. Publication bias was reported in 34 reviews (16%). Thirty-six of the 45 (80.0%) publication bias analyses indicated the presence of publication bias by trim and fill analysis, whereas Egger regression indicated publication bias in 23 of 45 (51.1%) analyses. The mean absolute percent difference between adjusted and observed point estimates was 15.5%, the median was 6.2%, and the range was 0% to 85.5%. CONCLUSIONS: Many of these reviews reported following published guidelines such as PRISMA or MOOSE, yet only half appropriately addressed publication bias in their reviews. Compared with previous research, our study found fewer reviews assessing publication bias and greater likelihood of publication bias among reviews not performing these evaluations.

Parity of publication for psychiatry.

BACKGROUND: There is an established disparity between physical and mental healthcare. Parity of research outputs has not been assessed internationally across influential medical journals. AIMS: To assess parity of publication between physical and mental health, and within psychiatry. METHOD: Four major medical disciplines were identified and their relative burden estimated. All publications from the highest-impact general medical journals in 2001, 2006 and 2011 were categorised accordingly. The frequency of psychiatry, cardiology, oncology and respiratory medicine articles were compared with the expected proportion (given illness burdens). Six subspecialties within psychiatry were also compared. RESULTS: Psychiatry was consistently and substantially underrepresented; other specialties were overrepresented. Dementia and psychosis demonstrated overrepresentation, with addiction and anxiety disorders represented proportionately and other disorders underrepresented. The underrepresentation of mood disorders increased more recently. CONCLUSIONS: There appears to be an important element of disparity of esteem; further action is required to achieve equivalence between mental and physical health research publications.

Some salt with your statin, professor?

We know that clinical trials sponsored by the pharmaceutical industry are likely to exaggerate benefit and minimise harms. But do these biases extend to their sponsorship of non-human animal research? Using systematic review and meta-analysis Bero and colleagues show that, in the case of statins, things are a little more complicated. While the conclusions of industry-sponsored studies were indeed more enthusiastic than warranted by their data, the data themselves painted a picture more conservative than was seen in non-industry-sponsored studies. This behaviour is consistent with maximising the return on investment, seeking robust data before embarking on a clinical trial, and, once that investment has been made, making every effort to "prove" that the drug is safe and effective if this is at all credible. The findings suggest that there is something different about industry-sponsored non-human animal research, perhaps reflecting higher standards than is the case elsewhere. Perhaps the academic community can learn something from our colleagues in the commercial sector.

Nonindustry-sponsored preclinical studies on statins yield greater efficacy estimates than industry-sponsored studies: a meta-analysis.

Industry-sponsored clinical drug studies are associated with publication of outcomes that favor the sponsor, even when controlling for potential bias in the methods used. However, the influence of sponsorship bias has not been examined in preclinical animal studies. We performed a meta-analysis of preclinical statin studies to determine whether industry sponsorship is associated with either increased effect sizes of efficacy outcomes and/or risks of bias in a cohort of published preclinical statin studies. We searched Medline (January 1966-April 2012) and identified 63 studies evaluating the effects of statins on atherosclerosis outcomes in animals. Two coders independently extracted study design criteria aimed at reducing bias, results for all relevant outcomes, sponsorship source, and investigator financial ties. The I(2) statistic was used to examine heterogeneity. We calculated the standardized mean difference (SMD) for each outcome and pooled data across studies to estimate the pooled average SMD using random effects models. In a priori subgroup analyses, we assessed statin efficacy by outcome measured, sponsorship source, presence or absence of financial conflict information, use of an optimal time window for outcome assessment, accounting for all animals, inclusion criteria, blinding, and randomization. The effect of statins was significantly larger for studies sponsored by nonindustry sources (-1.99; 95% CI -2.68, -1.31) versus studies sponsored by industry (-0.73; 95% CI -1.00, -0.47) (p value<0.001). Statin efficacy did not differ by disclosure of financial conflict information, use of an optimal time window for outcome assessment, accounting for all animals, inclusion criteria, blinding, and randomization. Possible reasons for the differences between nonindustry- and industry-sponsored studies, such as selective reporting of outcomes, require further study.