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.

暂无摘要。

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METHODS: All prospective interventional clinical trials for SMM, including published studies, meeting abstracts, and unpublished trials listed on ClinicalTrials.gov up to April 1, 2023, were identified.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

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.

BACKGROUND: The effects of mesenchymal stem cells (MSCs) on heart failure (HF) have been controversial. This study was conducted to investigate whether the transplantation of MSCs after HF could help improve clinical outcomes and myocardial performance indices.
METHODS: Using a systematic approach, electronic databases were searched for randomized controlled trials (RCTs), which evaluated the transplantation of MSCs after HF. The outcomes owf interest included clinical outcomes and myocardial function indices. We also assessed the role of age, cause of heart failure, cell origin, cell number, type of donor (autologous/allogeneic), and route of cell delivery on these outcomes. Using the random-effects method, a relative risk (RR) or mean difference (MD) and their corresponding 95% confidence intervals (CI) were pooled.
RESULTS: Seventeen RCTs including 1684 patients (927 and 757 patients in the intervention and control arms, respectively) were enrolled. The RR (95% CI) of mortality was 0.78 (0.62; 0.99, p = 0.04) in the MSC group compared to the controls. HF rehospitalization decreased in the MSC group (RR = 0.85 (0.71-1.01), p = 0.06), but this was only significant in those who received autologous MSCs (RR = 0.67 (0.49; 0.90), p = 0.008). LVEF was significantly increased among those who received MSC (MD = 3.38 (1.89; 4.87), p < 0.001). LVESV (MD = -9.14 (-13.25; -5.03), p < 0.001), LVEDV (MD = -8.34 -13.41; -3.27), p < 0.001), and scar size (standardized MD = -0.32 (-0.60; -0.05), p = 0.02) were significantly decreased. NYHA class (MD = -0.19 (-0.34; -0.06), p = 0.006), BNP level (standardized MD = -0.28 (-0.50; -0.06), p = 0.01), and MLHFQ (MD = -11.55 (-16.77; -6.33), p = 0.005) significantly decreased and 6-min walk test significantly improved (MD = 36.86 (11.22; 62.50), p = 0.001) in the MSC group. Trials were not affected by the participants\' etiology of heart failure, while trials with the autologous source of cells, MSC doses lower than 100 million cells, and intracoronary injection performed significantly better in some of the outcomes.
CONCLUSIONS: Transplantation of MSCs for ischemic or dilated heart failure patients may reduce all-cause mortality and improve clinical condition. Moreover, this treatment would improve left ventricular function indices and reduce scar size.

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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

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.

BACKGROUND: The commonly used frequentist paradigm of null hypothesis statistics testing with its reliance on the p-value and the corresponding notion of \'statistical significance\' has been under ongoing criticism. Misinterpretation and misuse of the p-value have contributed to publication bias, unreliable studies, frequent false positives, fraud and mistrust in results of scientific studies. While p-values themselves are still useful, part of the problem may be the confusion between statistical and clinical significance. In randomised controlled trials of health interventions, this confusion could lead to erroneous conclusions about treatment efficacy, research waste and compromised patient outcomes. The extent to which clinical and statistical significance of published randomised clinical trials do not match is not known. This is a protocol for a methodological study to understand the extent of the problem of disparities between statistical and clinical significance in published clinical trials, and to identify and assess the factors associated with discrepant results in these studies.
METHODS: A methodological survey of published randomised controlled trials is planned. Trials published between 2018 and 2022 and their protocols will be searched and screened for inclusion, with a planned sample size of 500 studies. The reported minimum clinically important difference, the study effect size and confidence intervals will be used to assess clinical importance of trial results. Comparison of statistical significance and clinical importance of the trial results will be used to determine disparity. Data will be analysed to estimate the outcomes, and factors associated with disparate study results will be assessed using logistic regression analysis.
BACKGROUND: Ethical approval for the study has been granted by Stellenbosch University\'s Health Research Ethics Committee. This is part of a larger study towards a PhD in Biostatistics and will be disseminated as a thesis, conference abstract and peer-reviewed manuscript.

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.