The impact of correction methods on rare-event meta-analysis.

Meta-analyses have become the gold standard for synthesizing evidence from multiple clinical trials, and they are especially useful when outcomes are rare or adverse since individual trials often lack sufficient power to detect a treatment effect. However, when zero events are observed in one or both treatment arms in a trial, commonly used meta-analysis methods can perform poorly. Continuity corrections (CCs), and numerical adjustments to the data to make computations feasible, have been proposed to ameliorate this issue. While the impact of various CCs on meta-analyses with rare events has been explored, how this impact varies based on the choice of pooling method and heterogeneity variance estimator is not widely understood. We compare several correction methods via a simulation study with a variety of commonly used meta-analysis methods. We consider how these method combinations impact important meta-analysis results, such as the estimated overall treatment effect, 95% confidence interval coverage, and Type I error rate. We also provide a website application of these results to aid researchers in selecting meta-analysis methods for rare-event data sets. Overall, no one-method combination can be consistently recommended, but some general trends are evident. For example, when there is no heterogeneity variance, we find that all pooling methods can perform well when paired with a specific correction method. Additionally, removing studies with zero events can work very well when there is no heterogeneity variance, while excluding single-zero studies results in poorer method performance when there is non-negligible heterogeneity variance and is not recommended.

Association between Toxoplasma gondii Infection and Type-1 Diabetes Mellitus: A Systematic Review and Meta-Analysis.

Type-1 diabetes, an autoimmune disease characterized by damage to pancreatic insulin-producing beta cells, is associated with adverse renal, retinal, cardiovascular, and cognitive outcomes, possibly including dementia. Moreover, the protozoal parasite Toxoplasma gondii has been associated with type-1 diabetes. To better characterize the association between type-1 diabetes and Toxoplasma gondii infection, we conducted a systematic review and meta-analysis of published studies that evaluated the relationship between type-1 diabetes and Toxoplasma gondii infection. A random-effects model based on nine primary studies (total number of participants = 2655) that met our inclusion criteria demonstrated a pooled odds ratio of 2.45 (95% confidence interval, 0.91-6.61). Removing one outlying study increased the pooled odds ratio to 3.38 (95% confidence interval, 2.09-5.48). These findings suggest that Toxoplasma gondii infection might be positively associated with type-1 diabetes, although more research is needed to better characterize this association. Additional research is required to determine whether changes in immune function due to type-1 diabetes increase the risk of infection with Toxoplasma gondii, infection with Toxoplasma gondii increases the risk of type-1 diabetes, or both processes occur.

Association between Toxoplasma gondii and systemic lupus erythematosus: A systematic review and meta-analysis.

Infecting approximately one-third of the world's population, the intraneuronal parasite Toxoplasma gondii has been associated with several autoimmune diseases. While Toxoplasma gondii may be protective against multiple sclerosis, other findings have negatively associated Toxoplasma gondii with different autoimmune diseases, including systemic lupus erythematosus. To further characterize the association between Toxoplasma gondii and systemic lupus erythematosus, we completed a systematic review and meta-analysis of published studies looking at the association between Toxoplasma gondii and systemic lupus erythematosus. The primary results of a random-effects model showed an odds ratio of 2.34 (95% confidence interval 1.17-4.69, P = 0.017), indicating the odds of Toxoplasma gondii seropositivity were 2.34 times higher in the group with systemic lupus erythematosus than in the healthy control group. Few available source studies, an overall lack of information about immunosuppressive status, and little information about sex composition and assays limit this finding and indicate the need for additional research to further characterize the association between systemic lupus erythematosus and Toxoplasma gondii.

A comparison of confidence distribution approaches for rare event meta-analysis.

Meta-analysis of rare event data has recently received increasing attention due to the challenging issues rare events pose to traditional meta-analytic methods. One specific way to combine information and analyze rare event meta-analysis data utilizes confidence distributions (CDs). While several CD methods exist, no comparisons have been made to determine which method is best suited for homogeneous or heterogeneous meta-analyses with rare events. In this article, we review several CD methods: Fisher's classic P-value combination method, one that combines P-value functions, another that combines confidence intervals, and one that combines confidence log-likelihood functions. We compare these CD approaches, and we propose and compare variations of these methods to determine which method produces reliable results for homogeneous or heterogeneous rare event meta-analyses. We find that for homogeneous rare event data, most CD methods perform very well. On the other hand, for heterogeneous rare event data, there is a clear split in performance between some CD methods, with some performing very poorly and others performing reasonably well.

A permutation-based approach for heterogeneous meta-analyses of rare events.

The increasingly widespread use of meta-analysis has led to growing interest in meta-analytic methods for rare events and sparse data. Conventional approaches tend to perform very poorly in such settings. Recent work in this area has provided options for sparse data, but these are still often hampered when heterogeneity across the available studies differs based on treatment group. We propose a permutation-based approach based on conditional logistic regression that accommodates this common contingency, providing more reliable statistical tests when such patterns of heterogeneity are observed. We find that commonly used methods can yield highly inflated Type I error rates, low confidence interval coverage, and bias when events are rare and non-negligible heterogeneity is present. Our method often produces much lower Type I error rates and higher confidence interval coverage than traditional methods in these circumstances. We illustrate the utility of our method by comparing it to several other methods via a simulation study and analyzing an example data set, which assess the use of antibiotics to prevent acute rheumatic fever.