Meta-analysis Of Binary Data Research Articles

Burden of dental caries in individuals experiencing food insecurity: a systematic review and meta-analysis.

Food insecurity affects approximately 2.37 billion people worldwide. Individuals experiencing food insecurity are more likely to exhibit poor health-related endpoints. Dental caries, a highly prevalent noncommunicable disease, is modulated by an interplay between biological, behavioral, and environmental factors. This systematic review and meta-analysis aimed to assess whether individuals experiencing food insecurity were more likely to exhibit dental caries than individuals facing food security. The Web of Science, PubMed, Scopus, Embase, Ovid, CINAHL, LILACS, and APA PsycINFO databases were checked from inception to November 2021. Grey literature and Google Scholar were also examined. An updated search was conducted in August 2022. Observational studies were included if they evaluated the association between dental caries and food insecurity status. Data extraction was performed by two reviewers. Random-effects meta-analyses were conducted using R language. In total, 514 references were retrieved from databases, of which 14 articles were included in qualitative synthesis and 7 were merged into meta-analysis. The results of an inverse-variance meta-analysis (OR = 1.62; 95%CI, 1.01-2.60) and a meta-analysis of binary data (OR = 1.66; 95%CI, 1.36-2.02) demonstrated that food-insecure individuals were more likely to exhibit dental caries than food-secure individuals. Inverse-variance meta-analyses appraising multiple strata of food security also showed that individuals experiencing marginal food security (OR = 1.48; 95%CI, 1.28-1.72), individuals experiencing low food security (OR = 1.26; 95%CI, 1.01-1.57), and those experiencing very low food security (OR = 1.33; 95%CI, 1.04-1.71) were more likely to exhibit dental caries than individuals experiencing full food security. Dental caries is associated with food insecurity. Individuals living with food insecurity are more likely to exhibit dental caries than those who have food security. PROSPERO registration number CRD42021268582.

Random-effects meta-analysis models for the odds ratio in the case of rare events under different data-generating models: A simulation study.

Meta-analysis of binary data is challenging when the event under investigation is rare, and standard models for random-effects meta-analysis perform poorly in such settings. In this simulation study, we investigate the performance of different random-effects meta-analysis models in terms of point and interval estimation of the pooled log odds ratio in rare events meta-analysis. First and foremost, we evaluate the performance of a hypergeometric-normal model from the family of generalized linear mixed models (GLMMs), which has been recommended, but has not yet been thoroughly investigated for rare events meta-analysis. Performance of this model is compared to performance of the beta-binomial model, which yielded favorable results in previous simulation studies, and to the performance of models that are frequently used in rare events meta-analysis, such as the inverse variance model and the Mantel-Haenszel method. In addition to considering a large number of simulation parameters inspired by real-world data settings, we study the comparative performance of the meta-analytic models under two different data-generating models (DGMs) that have been used in past simulation studies. The results of this study show that the hypergeometric-normal GLMM is useful for meta-analysis of rare events when moderate to large heterogeneity is present. In addition, our study reveals important insights with regard to the performance of the beta-binomial model under different DGMs from the binomial-normal family. In particular, we demonstrate that although misalignment of the beta-binomial model with the DGM affects its performance, it shows more robustness to the DGM than itscompetitors.

Meta analysis of binary data with excessive zeros in two-arm trials

We present a novel Bayesian approach to random effects meta analysis of binary data with excessive zeros in two-arm trials. We discuss the development of likelihood accounting for excessive zeros, the prior, and the posterior distributions of parameters of interest. Dirichlet process prior is used to account for the heterogeneity among studies. A zero inflated binomial model with excessive zero parameters were used to account for excessive zeros in treatment and control arms. We then define a modified unconditional odds ratio accounting for excessive zeros in two arms. The Bayesian inference is carried out using Markov chain Monte Carlo (MCMC) sampling techniques. We illustrate the approach using data available in published literature on myocardial infarction and death from cardiovascular causes. Bayesian approaches presented here use all the data, including the studies with zero events and capture heterogeneity among study effects, and produce interpretable estimates of overall and study-level odds-ratios, over the commonly used frequentist’s approaches. Results from the data analysis and the model selection also indicate that the proposed Bayesian method, while accounting for zero events, adjusts for excessive zeros and provides better fit to the data resulting in the estimates of overall odds-ratio and study-level odds-ratios that are based on the totality of the information.

Meta-Analysis of Binary Data Based upon Dichotomized Criteria

Abstract. This paper considers meta-analysis of binary data that use a dichotomized continuous score. Classification into two categories, e.g., qualified or not qualified, is often based upon a threshold or cut-off value. This threshold might vary between studies since intentionally different values are used. However, conventional meta-analysis methodology analyzing sensitivity and specificity separately might then be confounded by a potentially unknown variation of the cut-off value. In order to cope with varying thresholds, an overall estimate of the misclassification error is suggested instead, which is equivalent to the well-known Youden index. It is argued that this index is less prone to between-study variation of cut-off values. To adjust for potential study effects a Mantel-Haenszel estimator of the overall misclassification error is suggested. Arguments are illustrated using, as an example, the diagnosis of alcoholism using the Alcohol Use Disorders Identification Test (AUDIT).

Meta-analysis of binary data: which within study variance estimate to use?

We applied a mixed effects model to investigate between- and within-study variation in improvement rates of 180 schizophrenia outcome studies. The between-study variation was explained by the fixed study characteristics and an additional random study effect. Both rate difference and logit models were used. For a binary proportion outcome p(i) with sample size n(i) in the ith study, (circumflexp(i)(1-circumflexp(i))n)(-1) is the usual estimate of the within-study variance sigma(i)(2) in the logit model, where circumflexpi) is the sample mean of the binary outcome for subjects in study i. This estimate can be highly correlated with logit(circumflexp(i)). We used (macronp(i)(1-macronp)n(i))(-1) as an alternative estimate of sigma(i)(2), where macronp is the weighted mean of circumflexp(i)'s. We estimated regression coefficients (beta) of the fixed effects and the variance (tau(2)) of the random study effect using a quasi-likelihood estimating equations approach. Using the schizophrenia meta-analysis data, we demonstrated how the choice of the estimate of sigma(2)(i) affects the resulting estimates of beta and tau(2). We also conducted a simulation study to evaluate the performance of the two estimates of sigma(2)(i) in different conditions, where the conditions vary by number of studies and study size. Using the schizophrenia meta-analysis data, the estimates of beta and tau(2) were quite different when different estimates of sigma(2)(i) were used in the logit model. The simulation study showed that the estimates of beta and tau(2) were less biased, and the 95 per cent CI coverage was closer to 95 per cent when the estimate of sigma(2)(i) was (macronp(1-macronp)n(i))(-1) rather than (circumflexp(i)(1-circumflexp)n(i))(-1). Finally, we showed that a simple regression analysis is not appropriate unless tau(2) is much larger than sigma(2)(i), or a robust variance is used.