Evaluating early intervention in smoldering myeloma clinical trials: a systematic review.

BACKGROUND: Smoldering multiple myeloma (SMM), an asymptomatic precursor of multiple myeloma (MM), carries a variable risk of progression to MM. There is little consensus on the efficacy or optimal timing of treatment in SMM. We systematically reviewed the landscape of all clinical trials in SMM. We compared the efficacy of treatment regimens studied in SMM to results from these regimens when used in newly diagnosed multiple myeloma (NDMM), to determine whether the data suggest deeper responses in SMM versus NDMM. METHODS: All prospective interventional clinical trials for SMM, including published studies, meeting abstracts, and unpublished trials listed on ClinicalTrials.gov up to April 1, 2023, were identified. Trial-related variables were captured, including treatment strategy and efficacy results. Relevant clinical endpoints were defined as overall survival (OS) and quality of life. RESULTS: Among 45 SMM trials identified, 38 (84.4%) assessed active myeloma drugs, while 7 (15.6%) studied bone-modifying agents alone. Of 18 randomized trials in SMM, only one (5.6%) had a primary endpoint of OS; the most common primary endpoint was progression-free survival (n = 7, 38.9%). Among 32 SMM trials with available results, 9 (28.1%) met their prespecified primary endpoint, of which 5 were single-arm studies. Six treatment regimens were tested in both SMM and NDMM; 5 regimens yielded a lower rate of very good partial response rate or better (≥VGPR) in SMM compared to the corresponding NDMM trial (32% vs 63%, 43% vs 53%, 40% vs 63%, 86% vs 89%, 92% vs 95%, and 94% vs 87%, respectively). CONCLUSION: In this systematic review of all prospective interventional clinical trials in SMM, we found significant variability in trial design, including randomization status, primary endpoints, and types of intervention used. Despite the statistical limitations, comparison of treatment regimens revealed no compelling evidence that the treatment is more effective when introduced early in SMM compared to NDMM.

Unpublished trials of alprazolam XR and their influence on its apparent efficacy for panic disorder.

OBJECTIVE: To test for publication bias with alprazolam, the most widely prescribed benzodiazepine, by comparing its efficacy for panic disorder using trial results from (1) the published literature and (2) the US Food and Drug Administration (FDA). METHODS: From FDA reviews, we included data from all phase 2/3 efficacy trials of alprazolam extended-release (Xanax XR) for the treatment of panic disorder. A search for matching publications was performed using PubMed and Google Scholar. Publication bias was examined by comparing: (1) overall trial results (positive or not) according to the FDA v. corresponding publications; (2) effect size (Hedges's g) based on FDA data v. published data. RESULTS: The FDA review showed that five trials were conducted, only one of which (20%) was positive. Of the four not-positive trials, two were published conveying a positive outcome; the other two were not published. Thus, according to the published literature, three trials were conducted and all (100%) were positive. Alprazolam's effect size calculated using FDA data was 0.33 (CI95% 0.07-0.60) v. 0.47 (CI95% 0.30-0.65) using published data, an increase of 0.14, or 42%. CONCLUSIONS: Publication bias substantially inflates the apparent efficacy of alprazolam XR.

Sensitivity analysis for publication bias in meta-analysis of sparse data based on exact likelihood.

Meta-analysis is a powerful tool to synthesize findings from multiple studies. The normal-normal random-effects model is widely used to account for between-study heterogeneity. However, meta-analyses of sparse data, which may arise when the event rate is low for binary or count outcomes, pose a challenge to the normal-normal random-effects model in the accuracy and stability in inference since the normal approximation in the within-study model may not be good. To reduce bias arising from data sparsity, the generalized linear mixed model can be used by replacing the approximate normal within-study model with an exact model. Publication bias is one of the most serious threats in meta-analysis. Several quantitative sensitivity analysis methods for evaluating the potential impacts of selective publication are available for the normal-normal random-effects model. We propose a sensitivity analysis method by extending the likelihood-based sensitivity analysis with the $t$-statistic selection function of Copas to several generalized linear mixed-effects models. Through applications of our proposed method to several real-world meta-analyses and simulation studies, the proposed method was proven to outperform the likelihood-based sensitivity analysis based on the normal-normal model. The proposed method would give useful guidance to address publication bias in the meta-analysis of sparse data.

Nonparametric worst-case bounds for publication bias on the summary receiver operating characteristic curve.

The summary receiver operating characteristic (SROC) curve has been recommended as one important meta-analytical summary to represent the accuracy of a diagnostic test in the presence of heterogeneous cutoff values. However, selective publication of diagnostic studies for meta-analysis can induce publication bias (PB) on the estimate of the SROC curve. Several sensitivity analysis methods have been developed to quantify PB on the SROC curve, and all these methods utilize parametric selection functions to model the selective publication mechanism. The main contribution of this article is to propose a new sensitivity analysis approach that derives the worst-case bounds for the SROC curve by adopting nonparametric selection functions under minimal assumptions. The estimation procedures of the worst-case bounds use the Monte Carlo method to approximate the bias on the SROC curves along with the corresponding area under the curves, and then the maximum and minimum values of PB under a range of marginal selection probabilities are optimized by nonlinear programming. We apply the proposed method to real-world meta-analyses to show that the worst-case bounds of the SROC curves can provide useful insights for discussing the robustness of meta-analytical findings on diagnostic test accuracy.

Dissemination and outcome reporting bias in clinical malaria intervention trials: a cross-sectional analysis.

BACKGROUND: Dissemination and outcome reporting biases are a significant problem in clinical research, with far-reaching implications for both scientific understanding and clinical decision-making. This study investigates the prevalence of dissemination- and outcome reporting biases in registered interventional malaria research. METHODS: All malaria interventional trials registered on ClinicalTrials.gov from 2010 to 2020 were identified. Subsequently, publications that matched the registration were searched. The primary outcome measures were the percentage of registered studies that resulted in subsequent publication of study results, the concordance between registered outcomes, and reported outcomes. Secondary outcomes were compliance with WHO standards for timely publication (issued in 2017) of summary study results in the respective trial registry (within 12 months of study completion) or peer-reviewed publication (within 24 months of study completion) was evaluated. RESULTS: A total of 579 trials were identified on ClinicalTrials.gov, of which 544 met the inclusion criteria. Notably, almost 36.6% of these trials (199/544) were registered retrospectively, with 129 (23.7%) registered after the first patient enrolment and 70 (12.9%) following study completion. Publications were identified for 351 out of 544 registered trials (64.5%), involving 1,526,081 study participants. Conversely, publications were not found for 193 of the 544 registrations (35.5%), which aimed to enrol 417,922 study participants. Among these 544 registrations, 444 (81.6%) did not meet the WHO standard to post summary results within 12 months of primary study completion (the last visit of the last subject for collection of data on the primary outcome), while 386 out of 544 registrations (71.0%) failed to publish their results in a peer-reviewed journal within 24 months of primary study completion. Discrepancies were noted in the reported primary outcomes compared to the registered primary outcomes in 47.6% (222/466) of the published trials, and an even higher discordance rate of 73.2% (341/466 publications) for secondary outcomes. CONCLUSIONS: Non-dissemination remains a significant issue in interventional malaria research, with most trials failing to meet WHO standards for timely dissemination of summary results and peer-reviewed journal publications. Additionally, outcome reporting bias is highly prevalent across malaria publications. To address these challenges, it is crucial to implement strategies that enhance the timely reporting of research findings and reduce both non-dissemination and outcome reporting bias.

A Review of Meta-Analyses in Plastic Surgery: Need for Adequate Assessment of Publication Bias.

INTRODUCTION: Publication bias describes a phenomenon in which significant positive results have a higher likelihood of being published compared to negative or nonsignificant results. Publication bias can confound the estimated therapeutic effect in meta-analyses and needs to be adequately assessed in the surgical literature. METHODS: A review of meta-analyses published in five plastic surgery journals from 2002 to 2022 was conducted. The inclusion criteria for meta-analyses were factors that demonstrated an obligation to assess publication bias, such as interventions with comparable treatment groups and enough power for statistical analysis. Acknowledgment of publication bias risk, quality of bias assessment, methods used in assessment, and individual article factors were analyzed. RESULTS: 318 unique meta-analyses were identified in literature search, and after full-text reviews, 143 met the inclusion criteria for obligation to assess publication bias. 64% of eligible meta-analyses acknowledged the confounding potential of publication bias, and only 46% conducted a formal assessment. Of those who conducted an assessment, 49% used subjective inspection of funnel plots alone, while 47% used any statistical testing in analysis. Overall, only 9/143 (6.3%) assessed publication bias and attempted to correct for its effect. Journals with a higher average impact factor were associated with mention and assessment of publication bias, but more recent publication year and higher number of primary articles analyzed were not. CONCLUSIONS: This review identified low rates of proper publication bias assessment in meta-analyses published in five major plastic surgery journals. Assessment of publication bias using objective statistical testing is necessary to ensure quality literature within surgical disciplines.

Exploring trial publication and research waste in COVID-19 randomised trials of hydroxychloroquine, corticosteroids, and vitamin D: a meta-epidemiological cohort study.

BACKGROUND: The global research response to the COVID-19 pandemic was impressive, but also led to an infodemic and considerable research waste. Registered, but unpublished trials added to this noise. We aimed to determine the proportion of registered randomised trials of common COVID-19 treatments that were published and to describe the characteristics of these trials to examine the association between trial characteristics, publication status and research waste. METHODS: This meta-epidemiological cohort study used a sample of randomised trials of corticosteroids, hydroxychloroquine or vitamin D as treatments for COVID-19, registered between 1 November 2019 and 31 December 2021 and available via the WHO ICTRP portal. We searched for the trials' published results up to 20 October 2022. We extracted the trial characteristics, analysing with descriptive statistics. We performed univariate logistic regression to examine the association between trials' characteristics and publication status, followed by multiple logistic regression using significant characteristics to assess the association between trial characteristics and publication status. RESULTS: We identified 357 eligible trials on ICTRP. Of these, 107 (30%) had published or made their results available publicly by 20 October 2022, while 250 (70%) had not been published or shared their results publicly. Multiple logistic regression analysis showed that a larger target sample size was a significant positive predictor of publication with target sample sizes above 300 almost tripling the odds of publication (aOR: 2.75, 95% CI: 1.35 to 5.62). CONCLUSIONS: Less than one third of registered trials made their results public and our findings identified that many trialists had not updated their trial registry entry with the trial status, results or both. Failure to share trial results publicly is a disservice to patients, clinicians and policy makers and adds to research waste.

Publication Bias and Selective Outcome Reporting in Randomized Controlled Trials Related to Rehabilitation: A Literature Review.

OBJECTIVE: To investigate the rate of registered protocols published as research papers as a measure of publication bias, and the concordance rates of the primary outcomes between research protocol and published papers as a measure of selective outcome reporting bias in randomized controlled trials (RCTs) related to rehabilitation. DATA SOURCES: Protocols related to RCTs were extracted from electronic databases, the University Hospital Medical Information Network (UMIN), International Standard Research Clinical Trial Number (ISRCTN), ClinicalTrials.gov, and MEDLINE. Published papers were retrieved from MEDLINE. STUDY SELECTION: The inclusion criteria were as follows: (1) initial registration (UMIN, ISRCTN, ClinicalTrials.gov) within the designated period; (2) published as a paper from a research protocol in MEDLINE (PubMed); and (3) written in English or Japanese. The search period was from January 1, 2013, to December 31, 2020. DATA EXTRACTION: The outcome of this study was set as the rate of published papers that were consistent with the extracted research protocol and the concordance rate between the primary outcomes in published papers and in protocols. The concordance rate of the primary outcomes was evaluated by checking whether the description in the research protocol matched that in the paper's abstract and main text. DATA SYNTHESIS: Out of the 5597 research protocols registered, only 727 were published (13.0%). The concordance rates of the primary outcomes were 48.7% and 72.6% in the abstract and main text, respectively. CONCLUSIONS: This study revealed major discrepancies between the number of research protocols and published papers, and difference of description regarding the primary outcomes in published papers which were already defined in the research protocols.

Risk of bias and problematic trials: characterising the research integrity of trials submitted to Anaesthesia.

BACKGROUND: There is some evidence for systematic biases and failures of research integrity in the anaesthesia literature. However, the features of problematic trials and effect of editorial selection on these issues have not been well quantified. METHODS: We analysed 209 randomised controlled trials submitted to Anaesthesia between 8 March 2019 and 31 March 2020. We evaluated the submitted manuscript, registry data and the results of investigations into the integrity of the trial undertaken at the time of submission. Trials were labelled 'concerning' if failures of research integrity were found, and 'problematic' if identified issues would have warranted retraction if they had been found after publication. We investigated how 'problematic' trials were detected, the distribution of p values and the risk of outcome reporting bias and p-hacking. We also investigated whether there were any factors that differed in problematic trials. RESULTS: We found that false data was the most common reason for a trial to be labelled as 'concerning', which occurred in 51/62 (82%) cases. We also found that while 195/209 (93%) trials were preregistered, we found adequate registration for only 166/209 (79%) primary outcomes, 100/209 (48%) secondary outcomes and 11/209 (5%) analysis plans. We also found evidence for a step decrease in the frequency of p values > 0.05 compared with p values < 0.05. 'Problematic' trials were all single-centre and appeared to have fewer authors (incident risk ratio (95%CI) 0.8 (0.7-0.9)), but could not otherwise be distinguished reliably from other trials. CONCLUSIONS: Identification of 'problematic' trials is frequently dependent on individual patient data, which is often unavailable after publication. Additionally, there is evidence of a risk of outcome reporting bias and p-hacking in submitted trials. Implementation of alternative research and editorial practices could reduce the risk of bias and make identification of problematic trials easier.

Reporting all results efficiently: A RARE proposal to open up the file drawer.

While the social sciences have made impressive progress in adopting transparent research practices that facilitate verification, replication, and reuse of materials, the problem of publication bias persists. Bias on the part of peer reviewers and journal editors, as well as the use of outdated research practices by authors, continues to skew literature toward statistically significant effects, many of which may be false positives. To mitigate this bias, we propose a framework to enable authors to report all results efficiently (RARE), with an initial focus on experimental and other prospective empirical social science research that utilizes public study registries. This framework depicts an integrated system that leverages the capacities of existing infrastructure in the form of public registries, institutional review boards, journals, and granting agencies, as well as investigators themselves, to efficiently incentivize full reporting and thereby, improve confidence in social science findings. In addition to increasing access to the results of scientific endeavors, a well-coordinated research ecosystem can prevent scholars from wasting time investigating the same questions in ways that have not worked in the past and reduce wasted funds on the part of granting agencies.

Postpartum pain and the risk of postpartum depression: A meta-analysis of observational studies.

OBJECTIVE: This meta-analysis of observational studies aimed to derive a more precise estimation of the relationship between postpartum pain and postpartum depression (PPD). METHODS: A systematic literature search was completed in the following databases from inception to September 26, 2022: PubMed, Embase, and Web of Science. Quality evaluation of each study was achieved through Newcastle-Ottawa scale (NOS) assessment. Heterogeneity across studies was evaluated by Cochran's Q test and I2 test. Pooled estimates of odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were analyzed using fixed-effects model or random-effects model, according to heterogeneity. Subgroup analysis, sensitivity analysis, and Egger's test were also performed. RESULTS: From the identified 1884 articles, a total of 8 studies involving 3973 participants were included in the final meta-analysis. Seven of the 8 studies were evaluated as high-quality, with NOS scores ≥7. A significant heterogeneity was observed (I2 = 66.5%, p = 0.004) among eight studies. Therefore, the performed random-effect model suggested a significant association between postpartum pain and PPD risk (OR 1.29, 95% CI 1.10-1.52, p = 0.002). However, the subgroup analyses did not define the source of heterogeneity. Moreover, the sensitivity analysis showed the stability of the pooled results, but the significant publication bias was identified (p = 0.009). The trim and fill method was performed and resulted in an OR of 1.14 (95% CI 0.95-1.37, p = 0.162). CONCLUSIONS: This meta-analysis found a potential association between postpartum pain and PPD. Further researches are needed to provide more robust evidences.

Funding bias in shoulder arthroplasty research.

BACKGROUND: Prior research has shown that industry funding can impact the outcomes reported in medical literature. Limited data exist on the degree of bias that industry funding may have on shoulder arthroplasty literature outside of the Journal of Shoulder and Elbow Surgery. The purpose of this study is to characterize the type and frequency of funding for recently published shoulder arthroplasty studies and the impact of industry funding on reported outcomes. We hypothesized that studies with industry funding are more likely to report positive outcomes than those without. MATERIALS AND METHODS: We performed a retrospective study searching all articles with the term "shoulder arthroplasty," "reverse shoulder arthroplasty," "anatomic total shoulder arthroplasty," or "total shoulder arthroplasty" on PubMed from the years January 2020 to December 2022. The primary outcome of studies was coded as either positive, negative, or neutral. A positive result was defined as one in which the null hypothesis was rejected. A negative result was defined as one in which the result did not favor the group in which the industry-funded implant was used. A neutral result was defined as one in which the null hypothesis was confirmed. Article funding type, subcategorized as National Institutes of Health funding or industry funding was recorded. Author disclosures were recorded to determine conflicts of interest. Statistical analysis was conducted using the χ2 test and Fisher exact test. RESULTS: A total of 750 articles reported on either conflict of interest or funding source and were included in the study. Of the total number of industry-funded studies, the majority were found to have a positive primary endpoint (58.1%, 104 of 179), as compared to a negative (7.8%, 14 of 179) or neutral endpoint (33.5%, 60 of 179) (P = .004). Overall, 363 articles reported an author conflict of interest, and the majority of these studies had positive primary endpoint (55.6%, 202 of 363) as compared to negative (9.1%, 33 of 363) or neutral endpoints (34.4%, 125 of 363) (P = .002). CONCLUSION: The results of this study suggest that there is a significant relationship between conflicts of interest and the primary outcome of shoulder arthroplasty studies, beyond the overall positive publication bias. Studies with industry funding and author conflicts of interest both report positive outcomes more frequently than negative outcomes. Shoulder surgeons should be aware of this potential bias when choosing to base clinical practice on published data.

Relative likelihood ratios for neutral comparisons of statistical tests in simulation studies.

When comparing the performance of two or more competing tests, simulation studies commonly focus on statistical power. However, if the size of the tests being compared are either different from one another or from the nominal size, comparing tests based on power alone may be misleading. By analogy with diagnostic accuracy studies, we introduce relative positive and negative likelihood ratios to factor in both power and size in the comparison of multiple tests. We derive sample size formulas for a comparative simulation study. As an example, we compared the performance of six statistical tests for small-study effects in meta-analyses of randomized controlled trials: Begg's rank correlation, Egger's regression, Schwarzer's method for sparse data, the trim-and-fill method, the arcsine-Thompson test, and Lin and Chu's combined test. We illustrate that comparing power alone, or power adjusted or penalized for size, can be misleading, and how the proposed likelihood ratio approach enables accurate comparison of the trade-off between power and size between competing tests.

Toward Open and Reproducible Epidemiology.

Starting in the 2010s, researchers in the experimental social sciences rapidly began to adopt increasingly open and reproducible scientific practices. These practices include publicly sharing deidentified data when possible, sharing analytical code, and preregistering study protocols. Empirical evidence from the social sciences suggests such practices are feasible, can improve analytical reproducibility, and can reduce selective reporting. In academic epidemiology, adoption of open-science practices has been slower than in the social sciences (with some notable exceptions, such as registering clinical trials). Epidemiologic studies are often large, complex, conceived after data have already been collected, and difficult to replicate directly by collecting new data. These characteristics make it especially important to ensure their integrity and analytical reproducibility. Open-science practices can also pay immediate dividends to researchers' own work by clarifying scientific reasoning and encouraging well-documented, organized workflows. We consider how established epidemiologists and early-career researchers alike can help midwife a culture of open science in epidemiology through their research practices, mentorship, and editorial activities.

The measurable impact of a diversity, equity, and inclusion editor on diversifying content, authorship, and peer review participation in the Journal of Vascular Surgery.

OBJECTIVE: Women and minorities remain under-represented in academic vascular surgery. This under-representation persists in the editorial peer review process which may contribute to publication bias. In 2020, the Journal of Vascular Surgery (JVS) addressed this by diversifying the editorial board and creating a new Editor of Diversity, Equity, and Inclusion (DEI). The impact of a DEI editor on modifying the output of JVS has not yet been examined. We sought to determine the measurable impact of a DEI editor on diversifying perspectives represented in the journal, and on contributing to changes in the presence of DEI subject matter across published journal content. METHODS: The authorship and content of published primary research articles, editorials, and special articles in JVS were examined from November 2019 through July 2022. Publications were examined for the year prior to initiation of the DEI Editor (pre), the year following (post), and from September 2021 to July 2022, accounting for the average 47-week time period from submission to publication in JVS (lag). Presence of DEI topics and women authorship were compared using χ2 tests. RESULTS: During the period examined, the number of editorials, guidelines, and other special articles dedicated to DEI topics in the vascular surgery workforce or patient population increased from 0 in the year prior to 4 (16.7%) in the 11-month lag period. The number of editorials, guidelines, and other special articles with women as first or senior authors nearly doubled (24% pre, 44.4% lag; P = .31). Invited commentaries and discussions were increasingly written by women as the study period progressed (18.7% pre, 25.9% post, 42.6% lag; P = .007). The number of primary research articles dedicated to DEI topics increased (5.6% pre, 3.3% post, 8.1% lag; P = .007). Primary research articles written on DEI topics were more likely to have women first or senior authors than non-DEI specific primary research articles (68.0% of all DEI vs 37.5% of a random sampling of non-DEI primary research articles; P < .001). The proportion of distinguished peer reviewers increased (from 2.8% in 2020 to 21.9% in 2021; P < .001). CONCLUSIONS: The addition of a DEI editor to JVS significantly impacted the diversification of topics, authorship of editorials, special articles, and invited commentaries, as well as peer review participation. Ongoing efforts are needed to diversify subject matter and perspective in the vascular surgery literature and decrease publication bias.

Statistical significance and publication reporting bias in abstracts of reproductive medicine studies.

STUDY QUESTION: What were the frequency and temporal trends of reporting P-values and effect measures in the abstracts of reproductive medicine studies in 1990-2022, how were reported P-values distributed, and what proportion of articles that present with statistical inference reported statistically significant results, i.e. 'positive' results? SUMMARY ANSWER: Around one in six abstracts reported P-values alone without effect measures, while the prevalence of effect measures, whether reported alone or accompanied by P-values, has been increasing, especially in meta-analyses and randomized controlled trials (RCTs); the reported P-values were frequently observed around certain cut-off values, notably at 0.001, 0.01, or 0.05, and among abstracts present with statistical inference (i.e. P-value, CIs, or significant terms), a large majority (77%) reported at least one statistically significant finding. WHAT IS KNOWN ALREADY: Publishing or reporting only results that show a 'positive' finding causes bias in evaluating interventions and risk factors and may incur adverse health outcomes for patients.Despite efforts to minimize publication reporting bias in medical research, it remains unclear whether the magnitude and patterns of the bias have changed over time. STUDY DESIGN, SIZE, DURATION: We studied abstracts of reproductive medicine studies from 1990 to 2022. The reproductive medicine studies were published in 23 first-quartile journals under the category of Obstetrics and Gynaecology and Reproductive Biology in Journal Citation Reports and 5 high-impact general medical journals (The Journal of the American Medical Association, The Lancet, The BMJ, The New England Journal of Medicine, and PLoS Medicine). Articles without abstracts, animal studies, and non-research articles, such as case reports or guidelines, were excluded. PARTICIPANTS/MATERIALS, SETTING, METHODS: Automated text-mining was used to extract three types of statistical significance reporting, including P-values, CIs, and text description. Meanwhile, abstracts were text-mined for the presence of effect size metrics and Bayes factors. Five hundred abstracts were randomly selected and manually checked for the accuracy of automatic text extraction. The extracted statistical significance information was then analysed for temporal trends and distribution in general as well as in subgroups of study designs and journals. MAIN RESULTS AND THE ROLE OF CHANCE: A total of 24 907 eligible reproductive medicine articles were identified from 170 739 screened articles published in 28 journals. The proportion of abstracts not reporting any statistical significance inference halved from 81% (95% CI, 76-84%) in 1990 to 40% (95% CI, 38-44%) in 2021, while reporting P-values alone remained relatively stable, at 15% (95% CI, 12-18%) in 1990 and 19% (95% CI, 16-22%) in 2021. By contrast, the proportion of abstracts reporting effect measures alone increased considerably from 4.1% (95% CI, 2.6-6.3%) in 1990 to 26% (95% CI, 23-29%) in 2021. Similarly, the proportion of abstracts reporting effect measures together with P-values showed substantial growth from 0.8% (95% CI, 0.3-2.2%) to 14% (95% CI, 12-17%) during the same timeframe. Of 30 182 statistical significance inferences, 56% (n = 17 077) conveyed statistical inferences via P-values alone, 30% (n = 8945) via text description alone such as significant or non-significant, 9.3% (n = 2820) via CIs alone, and 4.7% (n = 1340) via both CI and P-values. The reported P-values (n = 18 417), including both a continuum of P-values and dichotomized P-values, were frequently observed around common cut-off values such as 0.001 (20%), 0.05 (16%), and 0.01 (10%). Of the 13 200 reproductive medicine abstracts containing at least one statistical inference, 77% of abstracts made at least one statistically significant statement. Among articles that reported statistical inference, a decline in the proportion of making at least one statistically significant inference was only seen in RCTs, dropping from 71% (95% CI, 48-88%) in 1990 to 59% (95% CI, 42-73%) in 2021, whereas the proportion in the rest of study types remained almost constant over the years. Of abstracts that reported P-value, 87% (95% CI, 86-88%) reported at least one statistically significant P-value; it was 92% (95% CI, 82-97%) in 1990 and reached its peak at 97% (95% CI, 93-99%) in 2001 before declining to 81% (95% CI, 76-85%) in 2021. LIMITATIONS, REASONS FOR CAUTION: First, our analysis focused solely on reporting patterns in abstracts but not full-text papers; however, in principle, abstracts should include condensed impartial information and avoid selective reporting. Second, while we attempted to identify all types of statistical significance reporting, our text mining was not flawless. However, the manual assessment showed that inaccuracies were not frequent. WIDER IMPLICATIONS OF THE FINDINGS: There is a welcome trend that effect measures are increasingly reported in the abstracts of reproductive medicine studies, specifically in RCTs and meta-analyses. Publication reporting bias remains a major concern. Inflated estimates of interventions and risk factors could harm decisions built upon biased evidence, including clinical recommendations and planning of future research. STUDY FUNDING/COMPETING INTEREST(S): No funding was received for this study. B.W.M. is supported by an NHMRC Investigator grant (GNT1176437); B.W.M. reports research grants and travel support from Merck and consultancy from Merch and ObsEva. W.L. is supported by an NHMRC Investigator Grant (GNT2016729). Q.F. reports receiving a PhD scholarship from Merck. The other author has no conflict of interest to declare. TRIAL REGISTRATION NUMBER: N/A.

Open Science in Health Psychology and Behavioral Medicine: A Statement From the Behavioral Medicine Research Council.

Open Science practices include some combination of registering and publishing study protocols (including hypotheses, primary and secondary outcome variables, and analysis plans) and making available preprints of manuscripts, study materials, de-identified data sets, and analytic codes. This statement from the Behavioral Medicine Research Council (BMRC) provides an overview of these methods, including preregistration; registered reports; preprints; and open research. We focus on rationales for engaging in Open Science and how to address shortcomings and possible objections. Additional resources for researchers are provided. Research on Open Science largely supports positive consequences for the reproducibility and reliability of empirical science. There is no solution that will encompass all Open Science needs in health psychology and behavioral medicine's diverse research products and outlets, but the BMRC supports increased use of Open Science practices where possible.

Factors relating to nonpublication and publication bias in clinical trials in Canada: A qualitative interview study.

AIMS: This study aims to understand factors contributing to nonpublication and publication bias in clinical trials in Canada. METHODS: Qualitative interviews were conducted between March 2019 and April 2021 with 34 participants from the Canadian provinces of Alberta, British Columbia and Ontario, including 17 clinical trial investigators, 1 clinical research coordinator, 3 research administrators, 3 research ethics board members and 10 clinical trial participants. We conducted a thematic analysis involving coding of interview transcripts and memo-writing to identify key themes. RESULTS: Several factors contribute to nonpublication and publication bias in clinical trial research. A core theme was that reporting practices are shaped by incentives within the research system taht favour publication of positive over negative trials. Investigators are discouraged from reporting by experiences or perceptions of difficulty in publishing negative findings but rewarded for publishing positive findings in various ways. Trial investigators more strongly associated positive clinical trials than negative trials with opportunities for industry and nonindustry funding and with academic promotion, bonuses and recognition. Research institutions and ethics boards tended to lack well-resourced, proactive policies and practices to ensure trial findings are reported in registries or journals. CONCLUSION: Clinical trial reporting practices in Canada are shaped by incentives favouring reporting of positive over negative trials, such as funding opportunities and academic promotion, bonuses and recognition. Research institutions could help change incentives by adopting performance metrics that emphasize full reporting of results in journals or registries.

Trial Registry Searches in Plastic Surgery Systematic Reviews: A Meta-epidemiological Study.

INTRODUCTION: Clinical trial registry searches for unpublished clinical trial data are a means of mitigating publication bias within systematic reviews (SRs). The purpose of our study is to look at the rate of clinical trial registry searches conducted by SRs in the top five Plastic and Reconstructive Surgery journals. METHODS: We identified the top five plastic and reconstructive surgery journals using the Google h-5 index. We then searched Pubmed for SRs published in these journals and compared them to plastic surgery SRs published in the Cochrane Collaboration for SRs over the last 5 y. We included all SRs that were published within these top five journals and Cochrane between December 6, 2016 and December 6, 2021. We then conducted a secondary analysis on clinicaltrials.gov looking for unpublished clinical trials for 100 randomized SRs that did not conduct a clinical trial registry search. RESULTS: In SRs, 3.3% (17/512) from plastic surgery journals conducted trial registry searches. In comparison, 95.0% (38/40) of Cochrane Collaboration SRs conducted trial registry searches. Our secondary analysis found that 50% (50/100) of SRs could have included at least one unpublished clinical trial data set. CONCLUSIONS: We found that plastic surgery SRs rarely include searches for unpublished clinical trial data in clinical trial registries. To improve the data completeness of SRs in plastic surgery journals, we recommend journals alter their author guidelines to require a clinical trial registry search for unpublished literature.

We don't know what we're missing: Evidence of a vastly undersampled invasive plant pool.

Invasive plants are a prominent threat to ecosystems and economies worldwide. Knowing the identity of invasive plants is critical for preventing their introduction and spread. Yet several lines of evidence, including spatial and taxonomic biases in reporting and the ongoing emergence of new invasives, suggest that we are missing basic information about the identity of invasive plants. Using a database of invasive plants reported in the peer-reviewed literature between 1959 and 2020, we examined trends in the accumulation of new invasive plants over time and estimated the size of the current pool of invasive plants both continentally and globally. The number of new invasive plants continues to increase exponentially over time, showing no sign of saturation, even in the best studied regions. Moreover, a sample-size based rarefaction-extrapolation curve of reported taxa suggests that what is documented in the current literature (3008 taxa) only captures 64% of the likely number of invasive plants globally (4721 taxa ± 132 SE). These estimates varied continentally; less than half of invasive plant taxa have likely been identified in Oceania and Central and South Americas. Studies that included multiple invasive plants (e.g., floristic studies) were much more efficient at adding new taxa to our global understanding of what is invasive (identifying 4.2 times more new taxa than single-taxon studies). With more potential invaders arriving every day, this analysis highlights a critical gap in our knowledge of the current invasive plant pool. Expanding invasion science to better encompass understudied geographic areas and increasing the numbers of floristic surveys would greatly improve our ability to accurately and efficiently identify what taxa are invasive. Preventing invasive plant introductions is incumbent upon knowing the identity of invasive plants. Thus, large knowledge gaps remain in invasion ecology that hinder efforts to proactively prevent and manage invasive plants.