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The meta‐analysis of rare events presents unique methodological challenges owing to the small number of events. Bayesian methods are often used to combine rare events data to inform decision‐making, as they can incorporate prior information and handle studies with zero events without the need for continuity corrections. However, the comparative performances of different Bayesian models in pooling rare events data are not well understood. We conducted a simulation to compare the statistical properties of four parameterizations based on the binomial‐normal hierarchical model, using two different priors for the treatment effect: weakly informative prior (WIP) and non‐informative prior (NIP), pooling randomized controlled trials with rare events using the odds ratio metric. We also considered the beta‐binomial model proposed by Kuss and the random intercept and slope generalized linear mixed models. The simulation scenarios varied based on the treatment effect, sample size ratio between the treatment and control arms, and level of heterogeneity. Performance was evaluated using median bias, root mean square error, median width of 95% credible or confidence intervals, coverage, Type I error, and empirical power. Two reviews are used to illustrate these methods. The results demonstrate that the WIP outperforms the NIP within the same model structure. Among the compared models, the model that included the treatment effect parameter in the risk model for the control arm did not perform well. Our findings confirm that rare events meta‐analysis faces the challenge of being underpowered, highlighting the importance of reporting the power of results in empirical studies. [ABSTRACT FROM AUTHOR]

Rare events meta‐analyses of randomized controlled trials (RCTs) are often underpowered because the outcomes are infrequent. Real‐world evidence (RWE) from non‐randomized studies may provide valuable complementary evidence about the effects of rare events, and there is growing interest in including such evidence in the decision‐making process. Several methods for combining RCTs and RWE studies have been proposed, but the comparative performance of these methods is not well understood. We describe a simulation study that aims to evaluate an array of alternative Bayesian methods for including RWE in rare events meta‐analysis of RCTs: the naïve data synthesis, the design‐adjusted synthesis, the use of RWE as prior information, the three‐level hierarchical models, and the bias‐corrected meta‐analysis model. The percentage bias, root‐mean‐square‐error, mean 95% credible interval width, coverage probability, and power are used to measure performance. The various methods are illustrated using a systematic review to evaluate the risk of diabetic ketoacidosis among patients using sodium/glucose co‐transporter 2 inhibitors as compared with active‐comparators. Our simulations show that the bias‐corrected meta‐analysis model is comparable to or better than the other methods in terms of all evaluated performance measures and simulation scenarios. Our results also demonstrate that data solely from RCTs may not be sufficiently reliable for assessing the effects of rare events. In summary, the inclusion of RWE could increase the certainty and comprehensiveness of the body of evidence of rare events from RCTs, and the bias‐corrected meta‐analysis model may be preferable. [ABSTRACT FROM AUTHOR]

Efficacy of acupuncture-related therapy in the treatment of knee osteoarthritis: a network meta-analysis of randomized controlled trials. Knee osteoarthritis (KOA) is a degenerative disease in which articular cartilage and synovial fluid in the knee are lost, leading to lower limb disability, particularly in elderly individuals.[1] While nonsteroidal anti-inflammatory drugs (NSAIDs) can improve the physical function of KOA patients, they pose a significant risk of gastrointestinal adverse events that hinder disease management.[2] Acupuncture (AT) is one of the most common non-pharmacological therapies for the treatment of musculoskeletal disease-related symptoms.[3] Although evidence supports the potential effectiveness of acupuncture in treating KOA-related symptoms, the findings are controversial.[4] Therefore, we conducted a systematic review, quantitative analysis, and summary of existing evidence to investigate the safety and effectiveness of acupuncture for KOA. Acupuncture Trialists C. The persistence of the effects of acupuncture after a course of treatment: a meta-analysis of patients with chronic pain. [Extracted from the article]

Objective: Time‐varying treatments are common in observational studies. However, when assessing treatment effects, the methodological framework has not been systematically established for handling time‐varying treatments. This study aimed to examine the current methods for dealing with time‐varying treatments in observational studies and developed practical recommendations. Methods: We searched PubMed from 2000 to 2021 for methodological articles about time‐varying treatments, and qualitatively summarized the current methods for handling time‐varying treatments. Subsequently, we developed practical recommendations through interactive internal group discussions and consensus by a panel of external experts. Results: Of the 36 eligible reports (22 methodological reviews, 10 original studies, 2 tutorials and 2 commentaries), most examined statistical methods for time‐varying treatments, and only a few discussed the overarching methodological process. Generally, there were three methodological components to handle time‐varying treatments. These included the specification of treatment which may be categorized as three scenarios (i.e., time‐independent treatment, static treatment regime, or dynamic treatment regime); definition of treatment status which could involve three approaches (i.e., intention‐to‐treat, per‐protocol, or as‐treated approach); and selection of analytic methods. Based on the review results, a methodological workflow and a set of practical recommendations were proposed through two consensus meetings. Conclusions: There is no consensus process for assessing treatment effects in observational studies with time‐varying treatments. Previous efforts were dedicated to developing statistical methods. Our study proposed a stepwise workflow with practical recommendations to assist the practice. [ABSTRACT FROM AUTHOR]