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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 METHODS: All prospective interventional clinical trials for SMM, including published studies, meeting abstracts, and unpublished trials listed on ClinicalTrials.gov up to April 1, 2023, were identified.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.

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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.

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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.

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Background: It has been recognized that HIV-related stigma hinders efforts in testing, treatment, and prevention. In this systematic review, we aimed to summarize available findings on the association between HIV-related stigma and age, social support, educational status, depression, employment status, wealth index, gender, residence, knowledge about HIV, marital status, duration since diagnosis, and disclosure status using a large number of studies. Methods: Electronic databases including Scopus, Medline/PubMed, Web of Sciences (WOS), Cochrane Library, Google Scholar, and Open Research Dataset Challenge were systematically searched until 15 April 2023. We included all kinds of HIV-stigma studies, regardless of language, publishing date, or geographic location. The inclusion criteria were met by 40 studies, with a total of 171,627 patients. A mixed-effect model was used to pool estimates and evaluate publication bias, as well as to conduct sensitivity analysis. Results: Factors such as older age, social support, greater education, higher socioeconomic status, good knowledge of HIV, and longer years of living with HIV significantly lowered the likelihood of HIV-related stigma. Contrarily, factors such as depression, residing in rural areas, female respondents, and non-disclosure of HIV status were significantly associated with a high risk of HIV-related stigma. Conclusion: To combat systemic HIV-associated stigma, it is crucial to develop wholesome and comprehensive social methods by raising community-level HIV awareness. In addition to activism, local economic development is also crucial for creating thriving communities with a strong social fabric.

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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.

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Social biases may concentrate the attention of researchers on a small number of well-known molecules/mechanisms leaving others underexplored. In accordance with this view, central to mechanistic toxicology is a narrow range of molecular pathways that are assumed to be involved in a significant part of the responses to toxicity. It is unclear, however, if there are other molecular mechanisms which play an important role in toxicity events but are overlooked by toxicology. To identify overlooked genes sensitive to chemical exposures, we used publicly available databases. First, we used data on the published chemical-gene interactions for 17,338 genes to estimate their sensitivity to chemical exposures. Next, we extracted data on publication numbers per gene for 19,243 human genes from the Find My Understudied Genes database. Thresholds were applied to both datasets using our algorithm to identify chemically sensitive and chemically insensitive genes and well-studied and underexplored genes. A total of 1110 underexplored genes highly sensitive to chemical exposures were used in GSEA and Shiny GO analyses to identify enriched biological categories. The metabolism of fatty acids, amino acids, and glucose were identified as underexplored molecular mechanisms sensitive to chemical exposures. These findings suggest that future effort is needed to uncover the role of xenobiotics in the current epidemics of metabolic diseases.

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In medical research, publication bias (PB) poses great challenges to the conclusions from systematic reviews and meta-analyses. The majority of efforts in methodological research related to classic PB have focused on examining the potential suppression of studies reporting effects close to the null or statistically non-significant results. Such suppression is common, particularly when the study outcome concerns the effectiveness of a new intervention. On the other hand, attention has recently been drawn to the so-called inverse publication bias (IPB) within the evidence synthesis community. It can occur when assessing adverse events because researchers may favor evidence showing a similar safety profile regarding an adverse event between a new intervention and a control group. In comparison to the classic PB, IPB is much less recognized in the current literature; methods designed for classic PB may be inaccurately applied to address IPB, potentially leading to entirely incorrect conclusions. This article aims to provide a collection of accessible methods to assess IPB for adverse events. Specifically, we discuss the relevance and differences between classic PB and IPB. We also demonstrate visual assessment through contour-enhanced funnel plots tailored to adverse events and popular quantitative methods, including Egger's regression test, Peters' regression test, and the trim-and-fill method for such cases. Three real-world examples are presented to illustrate the bias in various scenarios, and the implementations are illustrated with statistical code. We hope this article offers valuable insights for evaluating IPB in future systematic reviews of adverse events.

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OBJECTIVES: To describe the characteristics and publication outcomes of clinical prediction model studies registered on clinicaltrials.gov since 2000. STUDY DESIGN AND SETTING: Observational studies registered on clinicaltrials.gov between January 1, 2000, and March 2, 2022, describing the development of a new clinical prediction model or the validation of an existing model for predicting individual-level prognostic or diagnostic risk were analyzed. Eligible clinicaltrials.gov records were classified by modeling study type (development, validation) and the model outcome being predicted (prognostic, diagnostic). Recorded characteristics included study status, sample size information, Medical Subject Headings, and plans to share individual participant data. Publication outcomes were analyzed by linking National Clinical Trial numbers for eligible records with PubMed abstracts. RESULTS: Nine hundred twenty-eight records were analyzed from a possible 89,896 observational study records. Publications searches found 170 matching peer-reviewed publications for 137 clinicaltrials.gov records. The estimated proportion of records with 1 or more matching publications after accounting for time since study start was 2.8% at 2 years (95% CI: 1.7%, 3.9%), 12.3% at 5 years (9.8% to 14.9%) and 27% at 10 years (23% to 33%). Stratifying records by study start year indicated that publication proportions improved over time. Records tended to prioritize the development of new prediction models over the validation of existing models (76%; 704/928 vs. 24%; 182/928). At the time of download, 27% of records were marked as complete, 35% were still recruiting, and 14.7% had unknown status. Only 7.4% of records stated plans to share individual participant data. CONCLUSION: Published clinical prediction model studies are only a fraction of overall research efforts, with many studies planned but not completed or published. Improving the uptake of study preregistration and follow-up will increase the visibility of planned research. Introducing additional registry features and guidance may improve the identification of clinical prediction model studies posted to clinical registries.

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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.

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Aim: The matrix metalloproteinases (MMPs) inhibit tissue inhibitors of metalloproteinases (TIMPs), playing a notable role in various biological processes, and mutations in TIMP2 genes impact a variety of urinary cancers. In this study, we analyze and evaluate the potential involvement of the TIMP2 418 G/C and MMP gene polymorphism in the etiology of urinary cancer. Methodology: For suitable case-control studies, a literature search was undertaken from various database sources such as PubMed, EMBASE, and Google Scholar. Incorporated into the analysis were case-control or cohort studies that documented the correlation between TIMP2 418 G/C and urological cancers. MetaGenyo served as the tool for conducting the meta-analysis, employing a fixed-effects model. The collective odds ratios, along with their corresponding 95% confidence intervals, were calculated and presented to assess the robustness of the observed associations. Results: A total of seven studies involving controls and cases out of recorded 1265 controls and 1154 cases were analyzed to ascertain the significant association of the TIMP2 gene with urologic cancer. No statistically significant correlation was observed between allelic, recessive, dominant, and overdominant models for the genetic variant under investigation. A 95% confidence interval (CI) and odds ratio (OR) were computed for each model, considering p-values <0.05. The OR and 95% CI for the allelic model were 0.99 and 0.77-1.27, respectively, whereas the respective values were 1.00 and 0.76-1.32 for the recessive model. In the dominant contrast model, OR and 95% CI were 1.09 and 0.62-1.90, while the same were 0.93 and 0.77-1.12 for the overdominant model. A funnel plot was used to reanalyze and detect the results as statically satisfactory. Conclusions: As a result of the data obtained, the TIMP2 gene polymorphism does not correlate statistically with cancer risk. The significance of this finding can only be confirmed using a large population, extensive epidemiological research, a comprehensive survey, and a better understanding of the molecular pathways associated.

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BACKGROUND: Reporting bias refers to the phenomenon in which the reporting of research findings is influenced by the nature of the results. Without the totality of evidence, clinical practice may be misguided. The objective of this work was to examine the extent of reporting bias in clinical trials of breast reconstruction surgery. METHODS: We searched and extracted data from all completed breast reconstruction clinical trials published in ClinicalTrials.gov from database inception to August 2020. Investigators sought to identify published full manuscripts of the registered trials. The primary outcome was classified as positive or nonpositive and trials were classified as industry or nonindustry funded. Time to publication in a peer-reviewed journal was computed and compared using time-to-event analysis. Trial characteristics associated with publication were evaluated using logistic regression. RESULTS: A total of 156 clinical trials were identified, of which, 53 trials were published. The median time to publication was 22 months (IQR, 13-35 months). Industry-funded studies were associated with a longer time to publication (HR = 2.4, p = 0.023) and publication in lower-impact journals (OR = 3.7, p = 0.048). Randomized clinical trials were associated with faster times to publication than nonrandomized studies (aHR = 3.2, p = 0.030). Statistical significance and the effect size were not associated with time to publication. CONCLUSIONS: We found no evidence that industry-funded trials were more likely to report a positive primary outcome. However, industry-funded trials were associated with a longer time to publication and publication in lower-impact journals.

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OBJECTIVES: Advances in informatics research come from academic, nonprofit, and for-profit industry organizations, and from academic-industry partnerships. While scientific studies of commercial products may offer critical lessons for the field, manuscripts authored by industry scientists are sometimes categorically rejected. We review historical context, community perceptions, and guidelines on informatics authorship. PROCESS: We convened an expert panel at the American Medical Informatics Association 2022 Annual Symposium to explore the role of industry in informatics research and authorship with community input. The panel summarized session themes and prepared recommendations. CONCLUSIONS: Authorship for informatics research, regardless of affiliation, should be determined by International Committee of Medical Journal Editors uniform requirements for authorship. All authors meeting criteria should be included, and categorical rejection based on author affiliation is unethical. Informatics research should be evaluated based on its scientific rigor; all sources of bias and conflicts of interest should be addressed through disclosure and, when possible, methodological mitigation.

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Glyphosate (GLY)-based herbicides (GBHs) are the most commonly applied pesticide worldwide, and non-target organisms (e.g., animals) are now regularly exposed to GLY and GBHs due to the accumulation of these chemicals in many environments. Although GLY/GBH was previously considered to be non-toxic, growing evidence indicates that GLY/GBH negatively affects some animal taxa. However, there has been no systematic analysis quantifying its toxicity to animals. Therefore, we used a meta-analytical approach to determine whether there is a demonstrable effect of GLY/GBH toxicity across animals. We further addressed whether the effects of GLY/GBH vary due to (1) taxon (invertebrate vs. vertebrate), (2) habitat (aquatic vs. terrestrial), (3) type of biological response (behavior vs. physiology vs. survival), and (4) dosage or concentration of GLY/GBH. Using this approach, we also determined whether adjuvants (e.g., surfactants) in commercial formulations of GBHs increased toxicity for animals relative to exposure to GLY alone. We analyzed 1282 observations from 121 articles. We conclude that GLY is generally sub-lethally toxic for animals, particularly for animals in aquatic or marine habitats, and that toxicity did not exhibit dose-dependency. Yet, our analyses detected evidence for widespread publication bias so we encourage continued experimental investigations to better understand factors influencing GLY/GBH toxicity to animals.

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PURPOSE: Evidence-based medicine requires evaluation of the medical literature to guide clinical reasoning and treatment recommendations. The presence of publication bias towards exclusion of non-statistically significant clinical trials may be leading to an incomplete evaluation of the literature and cause potentially incomplete guidance for patients. We aimed to compare publication rates and impact of publications of positive and negative outcome clinical trials. METHODS: We queried the US National Library of Medicine Clinical Trials database identifying clinical trials with reported results on the topics of pancreatic, liver, and gastric cancer. A "positive" trial was defined as having a statistically significant difference between the treatment arms, while a "negative" did not. Data collected included termination cause, intervention, funding type, publication rates, and journal characteristics. RESULTS: In total, 535 clinical trials were examined, across all pathologies clinical trials with significant findings for the primary outcome were published at a higher rate (99%) compared to those with non-significant findings (77%) (p < 0.01). Significantly, more studies with significant findings reached at least 80% of their estimated enrollment goal versus non-significant studies, 72% and 53% respectively (p < 0.01). Three of four metrics for impact of publication showed no difference between significant and non-significant studies once they reached publication. CONCLUSION: These findings suggest that clinical trials of three of the most common upper gastrointestinal malignancies have a publication bias towards studies with significant primary outcome findings. This study has implications to the way evidence-based medicine is practiced as the medical literature appears to be failing to capture important data for consideration of clinical decision making.

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The LFK index has been promoted as an improved method to detect bias in meta-analysis. Putatively, its performance does not depend on the number of studies in the meta-analysis. We conducted a simulation study, comparing the LFK index test to three standard tests for funnel plot asymmetry in settings with smaller or larger group sample sizes. In general, false positive rates of the LFK index test markedly depended on the number and size of studies as well as the between-study heterogeneity with values between 0% and almost 30%. Egger's test adhered well to the pre-specified significance level of 5% under homogeneity, but was too liberal (smaller groups) or conservative (larger groups) under heterogeneity. The rank test was too conservative for most simulation scenarios. The Thompson-Sharp test was too conservative under homogeneity, but adhered well to the significance level in case of heterogeneity. The true positive rate of the LFK index test was only larger compared with classic tests if the false positive rate was inflated. The power of classic tests was similar or larger than the LFK index test if the false positive rate of the LFK index test was used as significance level for the classic tests. Under ideal conditions, the false positive rate of the LFK index test markedly and unpredictably depends on the number and sample size of studies as well as the extent of between-study heterogeneity. The LFK index test in its current implementation should not be used to assess funnel plot asymmetry in meta-analysis.

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The reality that volumes of published biomedical research are not reproducible is an increasingly recognized problem. Spurious results reduce trustworthiness of reported science, increasing research waste. While science should be self-correcting from a philosophical perspective, that in insolation yields no information on efforts required to nullify suspect findings or factors shaping how quickly science may be corrected. There is also a paucity of information on how perverse incentives in the publishing ecosystem favouring novel positive findings over null results shape the ability of published science to self-correct. Knowledge of factors shaping self-correction of science remain obscure, limiting our ability to mitigate harms. This modelling study introduces a simple model to capture dynamics of the publication ecosystem, exploring factors influencing research waste, trustworthiness, corrective effort and time to correction. Results from this work indicate that research waste and corrective effort are highly dependent on field-specific false positive rates and time delays to corrective results to spurious findings are propagated. The model also suggests conditions under which biomedical science is self-correcting and those under which publication of correctives alone cannot stem propagation of untrustworthy results. Finally, this work models a variety of potential mitigation strategies, including researcher- and publisher-driven interventions.

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Using a sample of 70,399 published p-values from 192 meta-analyses, we empirically estimate the counterfactual distribution of p-values in the absence of any biases. Comparing observed p-values with counterfactually expected p-values allows us to estimate how many p-values are published as being statistically significant when they should have been published as non-significant. We estimate the extent of selectively reported p-values to range between 57.7% and 71.9% of the significant p-values. The counterfactual p-value distribution also allows us to assess shifts of p-values along the entire distribution of published p-values, revealing that particularly very small p-values (p < 0.001) are unexpectedly abundant in the published literature. Subsample analysis suggests that the extent of selective reporting is reduced in research fields that use experimental designs, analyze microeconomics research questions, and have at least some adequately powered studies.

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Publication selection bias undermines the systematic accumulation of evidence. To assess the extent of this problem, we survey over 68,000 meta-analyses containing over 700,000 effect size estimates from medicine (67,386/597,699), environmental sciences (199/12,707), psychology (605/23,563), and economics (327/91,421). Our results indicate that meta-analyses in economics are the most severely contaminated by publication selection bias, closely followed by meta-analyses in environmental sciences and psychology, whereas meta-analyses in medicine are contaminated the least. After adjusting for publication selection bias, the median probability of the presence of an effect decreased from 99.9% to 29.7% in economics, from 98.9% to 55.7% in psychology, from 99.8% to 70.7% in environmental sciences, and from 38.0% to 29.7% in medicine. The median absolute effect sizes (in terms of standardized mean differences) decreased from d = 0.20 to d = 0.07 in economics, from d = 0.37 to d = 0.26 in psychology, from d = 0.62 to d = 0.43 in environmental sciences, and from d = 0.24 to d = 0.13 in medicine.

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