Statistical properties of methods based on the Q-statistic for constructing a confidence interval for the between-study variance in meta-analysis

Oral mexiletine for ventricular tachyarrhythmias treatment in implantable cardioverter-defibrillator patients: a systematic review of the literature

INTRODUCTION

To evaluate the clinical outcomes of oral mexiletine (oMXT) to treat ventricular tachyarrhythmias (VTAs) in the era of implantable cardioverter-defibrillator (ICD) technology.

EVIDENCE ACQUISITION

A systematic search was conducted using PubMed, Embase and Cochrane databases following the PRISMA guidelines to collect literature data reporting oMXT efficacy and safety outcomes in treating VTAs in ICD recipients.

EVIDENCE SYNTHESIS

Final analysis included four studies accounting for a total of 91 patients with recurrent VTAs treated with oMXT. Amiodarone therapy was initially attempted in most patients (91.2%), while catheter ablation was performed in one-third of patients. VTA recurrences were observed in 55/91 patients (60.4%) during oMXT treatment compared to 91/91 (100%) before treatment (P<0.001). Appropriate therapies occurred in 55/88 ICD patients (62.5%) during oMXT treatment compared to 80/88 (90.9%) before treatment (P<0.001). After oMXT introduction, there was a significant reduction of the individual burden of VTA episodes and appropriate ICD therapies per patient, showing Hedges'g values of -1.103 (P=0.002) and -1.474 (P=0.008), respectively. Safety analysis showed a sample-weighted overall side-effect rate of 30%, while 21% of patients required drug reduction or discontinuation. Aggregated meta-regression analysis of the included studies and remote literature revealed a linear correlation between oMXT dosage and the overall side effects rate (r2 = 0.48; P=0.014).

CONCLUSIONS

Oral mexiletine provides an adjunctive treatment to manage VTAs and reduces appropriate therapies in ICD patients with moderate efficacy and acceptable safety profiles. These observations await confirmation through randomised clinical trials.

Impact of COVID-19 on health-related quality of life in the general population: A systematic review and meta-analysis

The World Health Organization declared coronavirus disease of 2019 as an epidemic and public health emergency of international concern on January 30th, 2020. Different factors during a pandemic can contribute to low quality of life in the general population. Quality of life is considered multidimensional and subjective and is assessed by using patient reported outcome measures. The aim and objective of this review is to assess the impact of coronavirus disease of 2019 and associated factors on the Quality of Life in the general population. This review was conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. A protocol was registered in the international Prospective Register of Systematic Reviews database(CRD42021269897). A comprehensive electronic search in PubMed, EBSCO Host Research Databases, MEDLINE and Google scholar search engine was conducted. A total number of 1,7000,074 articles were identified from electronic search. 25 full text articles were retained for qualitative synthesis and seventeen articles for quantitative analysis. Seven main quality of life scales were used to assess the quality of life of the general population; World Health Organization Quality of Life-bref, EuroQuality of Life-Five dimensions, Short Form, European Quality of Life Survey, coronavirus disease of 2019 Quality of Life, General Health Questionnaire12 and My Life Today Questionnaire. The mean World Health Organization Quality of Life-brief was found to be 53.38% 95% confidence interval [38.50-68.27] and EuroQuality of Life-Five dimensions was 0.89 95% confidence interval [0.69-1.07]. Several factors have been linked to the Coronavirus disease of 2019 such as sociodemographic factors, peoples living with chronic diseases, confinement and financial constraints. This review confirms that the Coronavirus disease of 2019 pandemic affected the quality of life of the general population worldwide. Several factors such as sociodemographic, peoples living with chronic diseases, confinement and financial constraints affected the quality of life.

Mortality of COVID-19 in patients with hematological malignancies versus solid tumors: a systematic literature review and meta-analysis

Cancer patients are more vulnerable to COVID-19 compared to the general population, but it remains unclear which types of cancer have the highest risk of COVID-19-related mortality. This study examines mortality rates for those with hematological malignancies (Hem) versus solid tumors (Tumor). PubMed and Embase were systematically searched for relevant articles using Nested Knowledge software (Nested Knowledge, St Paul, MN). Articles were eligible for inclusion if they reported mortality for Hem or Tumor patients with COVID-19. Articles were excluded if they were not published in English, non-clinical studies, had insufficient population/outcomes reporting, or were irrelevant. Baseline characteristics collected included age, sex, and comorbidities. Primary outcomes were all-cause and COVID-19-related in-hospital mortality. Secondary outcomes included rates of invasive mechanical ventilation (IMV) and intensive care unit (ICU) admission. Effect sizes from each study were computed as logarithmically transformed odds ratios (ORs) with random-effects, Mantel-Haenszel weighting. The between-study variance component of random-effects models was computed using restricted effects maximum likelihood estimation, and 95% confidence intervals (CIs) around pooled effect sizes were calculated using Hartung-Knapp adjustments. In total, 12,057 patients were included in the analysis, with 2,714 (22.5%) patients in the Hem group and 9,343 (77.5%) patients in the Tumor group. The overall unadjusted odds of all-cause mortality were 1.64 times higher in the Hem group compared to the Tumor group (95% CI: 1.30-2.09). This finding was consistent with multivariable models presented in moderate- and high-quality cohort studies, suggestive of a causal effect of cancer type on in-hospital mortality. Additionally, the Hem group had increased odds of COVID-19-related mortality compared to the Tumor group (OR = 1.86 [95% CI: 1.38-2.49]). There was no significant difference in odds of IMV or ICU admission between cancer groups (OR = 1.13 [95% CI: 0.64-2.00] and OR = 1.59 [95% CI: 0.95-2.66], respectively). Cancer is a serious comorbidity associated with severe outcomes in COVID-19 patients, with especially alarming mortality rates in patients with hematological malignancies, which are typically higher compared to patients with solid tumors. A meta-analysis of individual patient data is needed to better assess the impact of specific cancer types on patient outcomes and to identify optimal treatment strategies.

Advanced methods and implementations for the meta-analyses of animal models: Current practices and future recommendations

Meta-analytic techniques have been widely used to synthesize data from animal models of human diseases and conditions, but these analyses often face two statistical challenges due to complex nature of animal data (e.g., multiple effect sizes and multiple species): statistical dependency and confounding heterogeneity. These challenges can lead to unreliable and less informative evidence, which hinders the translation of findings from animal to human studies. We present a literature survey of meta-analysis using animal models (animal meta-analysis), showing that these issues are not adequately addressed in current practice. To address these challenges, we propose a meta-analytic framework based on multilevel (linear mixed-effects) models. Through conceptualization, formulations, and worked examples, we illustrate how this framework can appropriately address these issues while allowing for testing new questions. Additionally, we introduce other advanced techniques such as multivariate models, robust variance estimation, and meta-analysis of emergent effect sizes, which can deliver robust inferences and novel biological insights. We also provide a tutorial with annotated R code to demonstrate the implementation of these techniques.

Identifying the relative impact of process- and outcome-related team performance antecedents: a meta-analysis

Purpose

The purpose of this paper is to identify, through an exploratory meta-analysis, which process- and outcome-related antecedents have the strongest relationship to overall team performance. The secondary objective is to create an understanding of the extent to which relative research interest in each construct to date has aligned with its reported effects.

Design/methodology/approach

This study uses a random-effects meta-analysis on studies that have measured the relationship between at least one process or outcome factor and overall team performance. The number of studies, effect size and between-study variances are captured and analyzed for each process/outcome factor. Prior literature has explored relationships between various process/outcome factors and overall team performance. This study expands on previous literature by examining a comprehensive set of process/outcome factors and their relative impact on overall team performance.

Findings

A meta-analysis of 190 effect sizes extracted from 52 empirical studies over the past two decades (1999–2020) showed the specific process and outcome factors that most strongly contributed to overall team performance were efficiency, schedule and innovation. In addition, only a weak correlation was found between process and outcome factors’ relationships with overall team performance and how often they are studied in the research community.

Originality/value

This study contributes to the body of knowledge on team performance by examining prior research to identify the relevant impact of various process and outcome factors on overall team performance. In addition, this study also assesses the extent to which research interest in these factors has appeared to match their relative impact. Analyzing the relative impact of various process and outcome factors allows researchers and practitioners to better identify methods to create improvement in overall team performance. Based on the findings, prioritizing efficiency, schedule and innovation may promote overall team performance.

A systematic review and meta-analysis of anesthesia type on hip fracture post-surgery outcomes

Objective

To compare technical, clinical, and safety outcomes among hip fracture patients treated with procedures supplemented by general anesthesia (GA) or spinal/regional anesthesia (S/R).

Data sources

We searched for original studies on PubMed, Ovid MEDLINE, Ovid Embase, and Cochrane databases.

Study selection

Studies that reported clinical outcomes in patients that underwent hip fracture surgery, had available data on type of anesthesia administered, and clinical follow-up data were selected for data extraction.

Data extraction

The primary outcomes of interest were odds of mortality, including in-hospital, 30-day, 90-day, and 1-year mortality. Various adverse events (AEs) were also compared.

Data synthesis

Twenty-eight studies met our selection criteria, including 190,394 patients. A total of 107,314 (56.4%) patients were treated with procedures involving GA while 83,080 (43.6%) were treated with procedures involving S/R. There was no difference in 30-day or >1-year mortality rates between the GA and SR groups; however, compared to S/R group, the GA group had a significantly higher odds of in-hospital (P = .004) and 90-day mortality (P = .004). There was no difference in odds of adverse events between the GA and the S/R group.

Conclusions

Patients administered S/R for hip fracture procedures demonstrate lower risk of in-hospital mortality and 90-day mortality compared to patients administered GA.

Level of evidence

Therapeutic level III.

Responses of soil mineral-associated and particulate organic carbon to carbon input: A meta-analysis

A minor change of soil organic carbon (SOC) greatly influences atmospheric carbon dioxide concentration and climate change. Exogenous carbon (C) input into soils can induce SOC decomposition or sequestration. The response of SOC to C input can be better understood when SOC is separated into mineral-associated (MAOC) and particulate (POC) organic carbon. The objective of this study is to explore whether exogenous C input promote MAOC and POC increase or decrease and the controlling factors. We gained 1181 observations from 17 studies for this meta-analysis. The effect sizes of exogenous C input on MAOC and POC content, and MAOC decomposition were calculated. The key factors influencing the effect sizes were explored through subgroup analysis. Potential publication bias was explored by using funnel plots, trim and fill method, and Egger's test. Exogenous C input significantly increased MAOC and POC content, although promoted MAOC decomposition. The effect sizes were larger for MAOC content than for POC content irrespective of soil and substrate properties and experiment methods. The effects of C input on MAOC and POC content were more pronounced in forest soils, and depended on the C and nitrogen (N) content in soil and substrates as well as experiment methods. The effect size of C input on MAOC decomposition were larger with substrate input of below 200 g C kg^-1 in specific soils. The sensitivity analysis carried out by removing one observation indicated our results were robust. In conclusion, exogenous C input increases MAOC and POC content although stimulate MAOC decomposition, and the effect sizes were influenced mainly by ecosystem type, carbon and nitrogen content of substrates and soils, and fractionation methods. The findings indicate the importance of C and N content in substrates and soils in controlling the response of SOC rather than the ratio of C to N.

Bootstrap-Based Between-Study Heterogeneity Tests in Meta-Analysis

Meta-analysis combines pertinent information from existing studies to provide an overall estimate of population parameters/effect sizes, as well as to quantify and explain the differences between studies. However, testing between-study heterogeneity is one of the most challenging tasks in meta-analysis research. Existing methods for testing heterogeneity, such as the Q test and likelihood ratio (LR) test, have been criticized for their failure to control Type I error rate and/or failure to attain enough statistical power. Although better reference distribution approximations have been proposed in the literature, their application is limited. Additionally, when the interest is to test whether the size of the heterogeneity is larger than a specific level, existing methods are far from mature. To address these issues, we propose new heterogeneity tests. Specifically, we combine bootstrap methods with existing heterogeneity tests (i.e., the maximum LR test, the restricted maximum LR test, and the Q test) to overcome the reference distribution issue and denote them as B-ML-LRT, B-REML-LRT, and B-Q, respectively. Simulation studies were conducted to examine and compare the performance of the proposed methods with the regular LR test, the regular Q test, and the Kulinskaya's improved Q test in both random- and mixed-effects meta-analyses. Based on the results of Type I error rates and statistical power, B-REML-LRT is recommended. Additionally, the improved Q test is also recommended when it is applicable. An R package boot.heterogeneity is provided to facilitate the implementation of the proposed tests.

Comparison of Balloon Guide Catheters and Standard Guide Catheters for Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

BACKGROUND

Balloon guide catheters (BGCs) are designed to induce flow arrest during mechanical thrombectomy procedures for acute ischemic stroke due to large-vessel occlusion and have been associated with improved clinical and angiographic outcomes. We conducted a systematic review and meta-analysis evaluating the relative technical and clinical outcomes associated with BGC versus non-BGC approaches.

METHODS

A systematic review of clinical literature using the PubMed database was undertaken to identify multiarm studies published between 2010 and 2021 reporting the use of BGC versus non-BGC approaches for stroke treatment. Data collected included complete recanalization (thrombolysis in cerebral infarction, TICI), first-pass effect TICI 3, puncture-to recanalization time, number of endovascular attempts, distal embolization, symptomatic intracerebral hemorrhage, 90-day modified Rankin Scale score 0-2, and 90-day mortality. Subgroup analyses assessed the impact of treatment device (stent-retrievers, contact aspiration, combination therapy, and not specified/other). A random effects model was fit for each outcome measure.

RESULTS

Fifteen studies were included. Compared with non-BGC approaches, patients treated with BGCs had greater odds of TICI 3 (odds ratio [OR] 1.57; 95% confidence interval [95% CI] 1.08-2.29) and first-pass effect TICI 3 (OR 3.63; 95% CI 2.34-5.62), reduced puncture-to-revascularization time (mean difference -7.8; 95% CI -13.3 to -2.2), fewer endovascular attempts (mean difference -0.47; 95% CI -0.68 to -0.26), reduced odds of distal emboli (OR 0.34; 95% CI 0.17-0.71) and symptomatic intracerebral hemorrhage (OR 0.66; 95% CI 0.51-0.86), greater odds of 90-day modified Rankin Scale score 0-2 (OR 1.51; 95% CI 1.27-1.79), and reduced odds of mortality (OR 0.69; 95% CI 0.57-0.82).

CONCLUSIONS

BGCs yield superior technical and clinical outcomes while reducing patient complications.

Corticosteroid therapy for COVID-19: A systematic review and meta-analysis of randomized controlled trials

BACKGROUND

Corticosteroid treatment is an effective and common therapeutic strategy for various inflammatory lung pathologies and may be an effective treatment for coronavirus disease 2019 (COVID-19). The purpose of this systematic review and meta-analysis of current literature was to investigate the clinical outcomes associated with corticosteroid treatment of COVID-19.

METHODS

We systematically searched PubMed, medRxiv, Web of Science, and Scopus databases through March 10, 2021 to identify randomized controlled trials (RCTs) that evaluated the effects of corticosteroid therapies for COVID-19 treatment. Outcomes of interest were mortality, need for mechanical ventilation, serious adverse events (SAEs), and superinfection.

RESULTS

A total of 7737 patients from 8 RCTs were included in the quantitative meta-analysis, of which 2795 (36.1%) patients received corticosteroids plus standard of care (SOC) while 4942 (63.9%) patients received placebo and/or SOC alone. The odds of mortality were significantly lower in patients that received corticosteroids as compared to SOC (odds ratio [OR] = 0.85 [95% CI: 0.76; 0.95], P = .003). Corticosteroid treatment reduced the odds of a need for mechanical ventilation as compared to SOC (OR = 0.76 [95% CI: 0.59; 0.97], P = .030). There was no significant difference between the corticosteroid and SOC groups with regards to SAEs and superinfections.

CONCLUSION

Corticosteroid treatment can reduce the odds for mortality and the need for mechanical ventilation in severe COVID-19 patients.

Outcomes of acute respiratory distress syndrome in COVID-19 patients compared to the general population: a systematic review and meta-analysis

INTRODUCTION

Acute respiratory distress syndrome (ARDS) due to coronavirus disease 2019 (COVID-19) often leads to mortality. Outcomes of patients with COVID-19-related ARDS compared to ARDS unrelated to COVID-19 is not well characterized.

AREAS COVERED

We performed a systematic review of PubMed, Scopus, and MedRxiv 11/1/2019 to 3/1/2021, including studies comparing outcomes in COVID-19-related ARDS (COVID-19 group) and ARDS unrelated to COVID-19 (ARDS group). Outcomes investigated were duration of mechanical ventilation-free days, intensive care unit (ICU) length-of-stay (LOS), hospital LOS, and mortality. Random effects models were fit for each outcome measure. Effect sizes were reported as pooled median differences of medians (MDMs), mean differences (MDs), or odds ratios (ORs).

EXPERT OPINION

Ten studies with 2,281 patients met inclusion criteria (COVID-19: 861 [37.7%], ARDS: 1420 [62.3%]). There were no significant differences between the COVID-19 and ARDS groups for median number of mechanical ventilator-free days (MDM: -7.0 [95% CI: -14.8; 0.7], p = 0.075), ICU LOS (MD: 3.1 [95% CI: -5.9; 12.1], p = 0.501), hospital LOS (MD: 2.5 [95% CI: -5.6; 10.7], p = 0.542), or all-cause mortality (OR: 1.25 [95% CI: 0.78; 1.99], p = 0.361). Compared to the general ARDS population, results did not suggest worse outcomes in COVID-19-related ARDS.

Remdesivir therapy in patients with COVID-19: A systematic review and meta-analysis of randomized controlled trials

Purpose

To perform a systematic review and meta-analysis of randomized controlled trials that examined remdesivir treatment for COVID-19.

Materials and methods

A systematic literature search was performed using Pubmed, Embase, and ClinicalTrials.gov to identify studies published up to October 25, 2020 that examined COVID-19 treatment with remdesivir. A total of 3 randomized controlled trials that consisted of 1691 patients were included in the meta-analysis.

Results

The odds for mechanical ventilation (MV) or extracorporeal membrane oxygenation (ECMO) following treatment was significantly lower in the remdesivir group compared to the control group (OR = 0.48 [95% CI: 0.34; 0.69], p < 0.001). The odds of early (at day 14/15; OR = 1.42 [95% CI: 1.16; 1.74], p < 0.001) and late (at day 28/29; OR = 1.44 [95% CI: 1.16; 1.79], p = 0.001) hospital discharge were significantly higher in the remdesivir group compared to the control group. There was no difference in the odds for mortality in patients treated with remdesivir (OR = 0.77 [95% CI: 0.56; 1.06], p = 0.108).

Conclusions

Remdesivir attenuates disease progression, leading to lower odds of MV/ECMO and greater odds of hospital discharge for COVID-19 patients. However, remdesivir does not affect odds of mortality.

•

Remdesivir reduces the odds for mechanical ventilation or extracorporeal membrane oxygenation in COVID-19 patients.

•

Remdesivir improves the odds for hospital discharge.

•

Remdesivir does not lower the odds for mortality.

•

Remdesivir can attenuate COVID-19 progression, but it is less effective in treating critically-ill patients.

Statistical Methods for Quantifying Between-study Heterogeneity in Meta-analysis with Focus on Rare Binary Events

Meta-analysis, the statistical procedure for combining results from multiple independent studies, has been widely used in medical research to evaluate intervention efficacy and drug safety. In many practical situations, treatment effects vary notably among the collected studies, and the variation, often modeled by the between-study variance parameter τ 2, can greatly affect the inference of the overall effect size. In the past, comparative studies have been conducted for both point and interval estimation of τ 2. However, most are incomplete, only including a limited subset of existing methods, and some are outdated. Further, none of the studies covers descriptive measures for assessing the level of heterogeneity, nor are they focused on rare binary events that require special attention. We summarize by far the most comprehensive set including 11 descriptive measures, 23 estimators, and 16 confidence intervals. In addition to providing synthesized information, we further categorize these methods according to their key features. We then evaluate their performance based on simulation studies that examine various realistic scenarios for rare binary events, with an illustration using a data example of a gestational diabetes meta-analysis. We conclude that there is no uniformly "best" method. However, methods with consistently better performance do exist in the context of rare binary events, and we provide practical guidelines based on numerical evidences.

Statistical properties of methods based on the Q-statistic for constructing a confidence interval for the between-study variance in meta-analysis

The effect sizes of studies included in a meta‐analysis do often not share a common true effect size due to differences in for instance the design of the studies. Estimates of this so‐called between‐study variance are usually imprecise. Hence, reporting a confidence interval together with a point estimate of the amount of between‐study variance facilitates interpretation of the meta‐analytic results. Two methods that are recommended to be used for creating such a confidence interval are the Q‐profile and generalized Q‐statistic method that both make use of the Q‐statistic. These methods are exact if the assumptions underlying the random‐effects model hold, but these assumptions are usually violated in practice such that confidence intervals of the methods are approximate rather than exact confidence intervals. We illustrate by means of two Monte‐Carlo simulation studies with odds ratio as effect size measure that coverage probabilities of both methods can be substantially below the nominal coverage rate in situations that are representative for meta‐analyses in practice. We also show that these too low coverage probabilities are caused by violations of the assumptions of the random‐effects model (ie, normal sampling distributions of the effect size measure and known sampling variances) and are especially prevalent if the sample sizes in the primary studies are small.