A Simplification and Implementation of Random-effects Meta-analyses Based on the Exact Distribution of Cochran’s Q

Drug Efficacy in the Treatment of Antipsychotic-Induced Akathisia: A Systematic Review and Network Meta-Analysis

Importance

Antipsychotic-induced akathisia (AIA) occurs in 14% to 35% of patients treated with antipsychotics and is associated with increased suicide and decreased adherence in patients with schizophrenia. However, no comprehensive review and network meta-analysis has been conducted to compare the efficacy of treatments for AIA.

Objective

To compare the efficacy associated with AIA treatments.

Data Sources

Three databases (MEDLINE, Web of Science, and Google Scholar) were systematically searched by multiple researchers for double-blind randomized clinical trials (RCTs) comparing active drugs for the treatment of AIA with placebo or another treatment between May 30 and June 18, 2023.

Study Selection

Selected studies were RCTs that compared adjunctive drugs for AIA vs placebo or adjunctive treatment in patients treated with antipsychotics fulfilling the criteria for akathisia, RCTs with sample size of 10 patients or more, only trials in which no additional drugs were administered during the study, and RCTs that used a validated akathisia score. Trials with missing data for the main outcome (akathisia score at the end points) were excluded.

Data Extraction and Synthesis

Data extraction and synthesis were performed, estimating standardized mean differences (SMDs) through pairwise and network meta-analysis with a random-effects model. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was followed.

Main Outcomes and Measures

The primary outcome was the severity of akathisia measured by a validated scale at the last available end point.

Results

Fifteen trials involving 492 participants compared 10 treatments with placebo. Mirtazapine (15 mg/d for ≥5 days; SMD, -1.20; 95% CI, -1.83 to -0.58), biperiden (6 mg/d for ≥14 days; SMD, -1.01; 95% CI, -1.69 to -0.34), vitamin B6 (600-1200 mg/d for ≥5 days; SMD, -0.92; 95% CI, -1.57 to -0.26), trazodone (50 mg/d for ≥5 days; SMD, -0.84; 95% CI, -1.54 to -0.14), mianserin (15 mg/d for ≥5 days; SMD, -0.81; 95% CI, -1.44 to -0.19), and propranolol (20 mg/d for ≥6 days; SMD, -0.78; 95% CI, -1.35 to -0.22) were associated with greater efficacy than placebo, with low to moderate heterogeneity (I2 = 34.6%; 95% CI, 0.0%-71.1%). Cyproheptadine, clonazepam, zolmitriptan, and valproate did not yield significant effects. Eight trials were rated as having low risk of bias; 2, moderate risk; and 5, high risk. Sensitivity analyses generally confirmed the results for all drugs except for cyproheptadine and propranolol. No association between effect sizes and psychotic severity was found.

Conclusions and Relevance

In this systematic review and network meta-analysis, mirtazapine, biperiden, and vitamin B6 were associated with the greatest efficacy for AIA, with vitamin B6 having the best efficacy and tolerance profile. Trazodone, mianserin, and propranolol appeared as effective alternatives with slightly less favorable efficacy and tolerance profiles. These findings should assist prescribers in selecting an appropriate medication for treating AIA.

Causal relationship between cannabis use and cancer: a genetically informed perspective

PURPOSE

Cannabis use is increasing legally worldwide, while its impact on cancer risk is unclear. This study was performed to investigate the relationship between cannabis use and the risk of several types of cancer.

METHODS

We conducted a two-sample Mendelian randomization (MR) study to explore the causality of cannabis use on 9 site-specific types of cancer including breast cancer, cervical cancer, melanoma, colorectal cancer, laryngeal cancer, oral cancer, oropharyngeal cancer, esophageal cancer, and glioma. Genome-wide significant genetic instruments (P < 5E-06) for cannabis use were extracted from a large-scale genome-wide association meta-analysis of European ancestry, whereas cancer genetic instruments were extracted from the UK Biobank (UKB) cohort and GliomaScan consortium in the OpenGWAS database. The inverse-variance weighted (IVW) was considered the main method for MR analysis, and sensitivity analyses including MR-Egger, weighted median, MR pleiotropy residual sum, and outlier test (MR-PRESSO) were conducted to evaluate the robustness of the results.

RESULTS

Cannabis use was a significant promoting factor for cervical cancer (OR = 1.001265, 95% CI 1.000375-1.002155, P = 0.0053). And we also detected suggestive evidence of the causality of cannabis use on laryngeal cancer (OR = 1.000350, 95% CI 1.000027-1.000672, P = 0.0336) and breast cancer (OR = 1.003741, 95% CI 1.000052-1.007442, P = 0.0467). No evidence of a causal association of cannabis use with other site-specific types of cancer was detected. Additionally, no pleiotropy or heterogeneity was found in the sensitivity analysis.

CONCLUSION

This study indicates a causative association of cannabis use on cervical cancer, while cannabis use may increase the odds of breast cancer and laryngeal cancer, which require further evaluation in large-scale population-based studies.

Interval estimation of the overall treatment effect in random-effects meta-analyses: Recommendations from a simulation study comparing frequentist, Bayesian, and bootstrap methods

There exists a variety of interval estimators for the overall treatment effect in a random-effects meta-analysis. A recent literature review summarizing existing methods suggested that in most situations, the Hartung-Knapp/Sidik-Jonkman (HKSJ) method was preferable. However, a quantitative comparison of those methods in a common simulation study is still lacking. Thus, we conduct such a simulation study for continuous and binary outcomes, focusing on the medical field for application. Based on the literature review and some new theoretical considerations, a practicable number of interval estimators is selected for this comparison: the classical normal-approximation interval using the DerSimonian-Laird heterogeneity estimator, the HKSJ interval using either the Paule-Mandel or the Sidik-Jonkman heterogeneity estimator, the Skovgaard higher-order profile likelihood interval, a parametric bootstrap interval, and a Bayesian interval using different priors. We evaluate the performance measures (coverage and interval length) at specific points in the parameter space, that is, not averaging over a prior distribution. In this sense, our study is conducted from a frequentist point of view. We confirm the main finding of the literature review, the general recommendation of the HKSJ method (here with the Sidik-Jonkman heterogeneity estimator). For meta-analyses including only two studies, the high length of the HKSJ interval limits its practical usage. In this case, the Bayesian interval using a weakly informative prior for the heterogeneity may help. Our recommendations are illustrated using a real-world meta-analysis dealing with the efficacy of an intramyocardial bone marrow stem cell transplantation during coronary artery bypass grafting.

Methods to estimate the between-study variance and its uncertainty in meta-analysis

Meta-analyses are typically used to estimate the overall/mean of an outcome of interest. However, inference about between-study variability, which is typically modelled using a between-study variance parameter, is usually an additional aim. The DerSimonian and Laird method, currently widely used by default to estimate the between-study variance, has been long challenged. Our aim is to identify known methods for estimation of the between-study variance and its corresponding uncertainty, and to summarise the simulation and empirical evidence that compares them. We identified 16 estimators for the between-study variance, seven methods to calculate confidence intervals, and several comparative studies. Simulation studies suggest that for both dichotomous and continuous data the estimator proposed by Paule and Mandel and for continuous data the restricted maximum likelihood estimator are better alternatives to estimate the between-study variance. Based on the scenarios and results presented in the published studies, we recommend the Q-profile method and the alternative approach based on a 'generalised Cochran between-study variance statistic' to compute corresponding confidence intervals around the resulting estimates. Our recommendations are based on a qualitative evaluation of the existing literature and expert consensus. Evidence-based recommendations require an extensive simulation study where all methods would be compared under the same scenarios.

Improving Bridging from Informatics Theory to Practice

BACKGROUND

In 1962, Methods of Information in Medicine (MIM) began to publish papers on the methodology and scientific fundamentals of managing data, information, and knowledge in biomedicine and health care. Meeting an increasing demand for research about practical implementation of health information systems, the journal Applied Clinical Informatics (ACI) was launched in 2009. Both journals are official journals of the International Medical Informatics Association (IMIA).

OBJECTIVES

Based on prior analyses, we aimed to describe major topics published in MIM during 2014 and to explore whether theory of MIM influenced practice of ACI. Our objectives were further to describe lessons learned and to discuss possible editorial policies to improve bridging from theory to practice.

METHODS

We conducted a retrospective, observational study reviewing MIM articles published during 2014 (N=61) and analyzing reference lists of ACI articles from 2014 (N=70). Lessons learned and opinions about MIM editorial policies were developed in consensus by the two authors. These have been influenced by discussions with the journal's associate editors and editorial board members.

RESULTS

The publication topics of MIM in 2014 were broad, covering biomedical and health informatics, medical biometry and epidemiology. Important topics discussed were biosignal interpretation, boosting methodologies, citation analysis, health-enabling and ambient assistive technologies, health record banking, safety, and standards. Nine ACI practice articles from 2014 cited eighteen MIM theory papers from any year. These nine ACI articles covered mainly the areas of clinical documentation and medication-related decision support. The methodological basis they cited from was almost exclusively related to evaluation. We could show some direct links where theory impacted practice. These links are however few in relation to the total amount of papers published.

CONCLUSIONS

Editorial policies such as publishing systematic methodological reviews and clarification of possible practical impact of theory-focused articles may improve bridging.