Meta‐analysis of ratios of sample variances

When conducting a meta‐analysis of standardized mean differences (SMDs), it is common to use Cohen's d, or its variants, that require equal variances in the two arms of each study. While interpretation of these SMDs is simple, this alone should not be used as a justification for assuming equal variances. Until now, researchers have either used an F‐test for each individual study or perhaps even conveniently ignored such tools altogether. In this paper, we propose a meta‐analysis of ratios of sample variances to assess whether the equality of variances assumptions is justified prior to a meta‐analysis of SMDs. Quantile–quantile plots, an omnibus test for equal variances or an overall meta‐estimate of the ratio of variances can all be used to formally justify the use of less common methods when evidence of unequal variances is found. The methods in this paper are simple to implement and the validity of the approaches are reinforced by simulation studies and an application to a real data set. Copyright © 2016 John Wiley & Sons, Ltd.     

Generalizability analysis for clinical trials: a simulation study

Subjects are rarely selected on a random basis from a well‐defined patient population of interest into a clinical trial, with women, children, the elderly, and those with common comorbidities who are frequently underrepresented. Decades of clinical experience have demonstrated that the application of trial findings to individual patients is permissible by using efficacy as a measure of effectiveness and assuming that the characteristics of patients are sufficiently similar. In order to investigate this issue in greater depth, we simulated a patient population with treatment effect size of 0.5 (Cohen's d) and five covariates that included gender, health insurance, comorbidity, age, and motivation. To demonstrate how selection of patients for a clinical trial can bias the results when treatment effect varies across individuals, we created 50 nonrandom clinical trials based on this patient population and showed relative bias to range from 1.68% to 99.70%. We calculated and evaluated three indexes: C‐statistics, standardized mean difference (SMD), and Tipton's index (β) of generalization for the 50 nonrandom trials. Findings indicated that (i) the ranges were 0.56–0.98, 0.23–11.17, and 0.99–0.73 for C‐statistics, SMD, and β, respectively, when treatment effect bias increased from 1.68% to 99.70% and (ii) C‐statistics < 0.86, SMD < 1.95, and β > 0.91 when treatment effect bias <50%. Recommendations are made using existing generalization indexes on the basis of our simulation results. An example from a real clinical trial is provided for illustration. Copyright © 2017 John Wiley & Sons, Ltd.     

Misunderstandings about Q and ‘Cochran's Q test' in meta‐analysis

Many meta‐analyses report using ‘Cochran's Q test' to assess heterogeneity of effect‐size estimates from the individual studies. Some authors cite work by W. G. Cochran, without realizing that Cochran deliberately did not use Q itself to test for heterogeneity. Further, when heterogeneity is absent, the actual null distribution of Q is not the chi‐squared distribution assumed for ‘Cochran's Q test'. This paper reviews work by Cochran related to Q. It then discusses derivations of the asymptotic approximation for the null distribution of Q, as well as work that has derived finite‐sample moments and corresponding approximations for the cases of specific measures of effect size. Those results complicate implementation and interpretation of the popular heterogeneity index I². Also, it turns out that the test‐based confidence intervals used with I² are based on a fallacious approach. Software that outputs Q and I² should use the appropriate reference value of Q for the particular measure of effect size and the current meta‐analysis. Q is a key element of the popular DerSimonian–Laird procedure for random‐effects meta‐analysis, but the assumptions of that procedure and related procedures do not reflect the actual behavior of Q and may introduce bias. The DerSimonian–Laird procedure should be regarded as unreliable. Copyright © 2015 John Wiley & Sons, Ltd.     

Finding the power to reduce publication bias

The central purpose of this study is to document how a sharper focus upon statistical power may reduce the impact of selective reporting bias in meta‐analyses. We introduce the weighted average of the adequately powered (WAAP) as an alternative to the conventional random‐effects (RE) estimator. When the results of some of the studies have been selected to be positive and statistically significant (i.e. selective reporting), our simulations show that WAAP will have smaller bias than RE at no loss to its other statistical properties. When there is no selective reporting, the difference between RE's and WAAP's statistical properties is practically negligible. Nonetheless, when selective reporting is especially severe or heterogeneity is very large, notable bias can remain in all weighted averages. The main limitation of this approach is that the majority of meta‐analyses of medical research do not contain any studies with adequate power (i.e. > 80%). For such areas of medical research, it remains important to document their low power, and, as we demonstrate, an alternative unrestricted weighted least squares weighted average can be used instead of WAAP. Copyright © 2017 John Wiley & Sons, Ltd.     

Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods

Mendelian randomization is the use of genetic instrumental variables to obtain causal inferences from observational data. Two recent developments for combining information on multiple uncorrelated instrumental variables (IVs) into a single causal estimate are as follows: (i) allele scores, in which individual‐level data on the IVs are aggregated into a univariate score, which is used as a single IV, and (ii) a summary statistic method, in which causal estimates calculated from each IV using summarized data are combined in an inverse‐variance weighted meta‐analysis. To avoid bias from weak instruments, unweighted and externally weighted allele scores have been recommended. Here, we propose equivalent approaches using summarized data and also provide extensions of the methods for use with correlated IVs. We investigate the impact of different choices of weights on the bias and precision of estimates in simulation studies. We show that allele score estimates can be reproduced using summarized data on genetic associations with the risk factor and the outcome. Estimates from the summary statistic method using external weights are biased towards the null when the weights are imprecisely estimated; in contrast, allele score estimates are unbiased. With equal or external weights, both methods provide appropriate tests of the null hypothesis of no causal effect even with large numbers of potentially weak instruments. We illustrate these methods using summarized data on the causal effect of low‐density lipoprotein cholesterol on coronary heart disease risk. It is shown that a more precise causal estimate can be obtained using multiple genetic variants from a single gene region, even if the variants are correlated. © 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.     

Hartung–Knapp method is not always conservative compared with fixed‐effect meta‐analysis

A widely used method in classic random‐effects meta‐analysis is the DerSimonian–Laird method. An alternative meta‐analytical approach is the Hartung–Knapp method. This article reports results of an empirical comparison and a simulation study of these two methods and presents corresponding analytical results. For the empirical evaluation, we took 157 meta‐analyses with binary outcomes, analysed each one using both methods and performed a comparison of the results based on treatment estimates, standard errors and associated P‐values. In several simulation scenarios, we systematically evaluated coverage probabilities and confidence interval lengths. Generally, results are more conservative with the Hartung–Knapp method, giving wider confidence intervals and larger P‐values for the overall treatment effect. However, in some meta‐analyses with very homogeneous individual treatment results, the Hartung–Knapp method yields narrower confidence intervals and smaller P‐values than the classic random‐effects method, which in this situation, actually reduces to a fixed‐effect meta‐analysis. Therefore, it is recommended to conduct a sensitivity analysis based on the fixed‐effect model instead of solely relying on the result of the Hartung–Knapp random‐effects meta‐analysis. Copyright © 2016 John Wiley & Sons, Ltd.     

Inference for correlated effect sizes using multiple univariate meta‐analyses

Multivariate meta‐analysis, which involves jointly analyzing multiple and correlated outcomes from separate studies, has received a great deal of attention. One reason to prefer the multivariate approach is its ability to account for the dependence between multiple estimates from the same study. However, nearly all the existing methods for analyzing multivariate meta‐analytic data require the knowledge of the within‐study correlations, which are usually unavailable in practice. We propose a simple non‐iterative method that can be used for the analysis of multivariate meta‐analysis datasets, that has no convergence problems, and does not require the use of within‐study correlations. Our approach uses standard univariate methods for the marginal effects but also provides valid joint inference for multiple parameters. The proposed method can directly handle missing outcomes under missing completely at random assumption. Simulation studies show that the proposed method provides unbiased estimates, well‐estimated standard errors, and confidence intervals with good coverage probability. Furthermore, the proposed method is found to maintain high relative efficiency compared with conventional multivariate meta‐analyses where the within‐study correlations are known. We illustrate the proposed method through two real meta‐analyses where functions of the estimated effects are of interest. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.     

Meta‐analysis of clinical trials with early stopping: an investigation of potential bias

Clinical trials that stop early for benefit have a treatment difference that overestimates the true effect. The consequences of this fact have been extensively debated in the literature. Some researchers argue that early stopping, or truncation, is an important source of bias in treatment effect estimates, particularly when truncated studies are incorporated into meta‐analyses. Such claims are bound to lead some systematic reviewers to consider excluding truncated studies from evidence synthesis. We therefore investigated the implications of this strategy by examining the properties of sequentially monitored studies conditional on reaching the final analysis. As well as estimation bias, we studied information bias measured by the difference between standard measures of statistical information, such as sample size, and the actual information based on the conditional sampling distribution. We found that excluding truncated studies leads to underestimation of treatment effects and overestimation of information. Importantly, the information bias increases with the estimation bias, meaning that greater estimation bias is accompanied by greater overweighting in a meta‐analysis. Simulations of meta‐analyses confirmed that the bias from excluding truncated studies can be substantial. In contrast, when meta‐analyses included truncated studies, treatment effect estimates were essentially unbiased. Previous analyses comparing treatment effects in truncated and non‐truncated studies are shown not to be indicative of bias in truncated studies. We conclude that early stopping of clinical trials is not a substantive source of bias in meta‐analyses and recommend that all studies, both truncated and non‐truncated, be included in evidence synthesis. Copyright © 2013 John Wiley & Sons, Ltd.     

Ratio of geometric means to analyze continuous outcomes in meta-analysis: comparison to mean differences and ratio of arithmetic means using empiric data and simulation

Meta‐analyses pooling continuous outcomes can use mean differences (MD), standardized MD (MD in pooled standard deviation units, SMD), or ratio of arithmetic means (RoM). Recently, ratio of geometric means using ad hoc (RoGM _{ad hoc}) or Taylor series (RoGM _Taylor) methods for estimating variances have been proposed as alternative effect measures for skewed continuous data. Skewed data are suggested for summary measures of clinical parameters restricted to positive values which have large coefficients of variation (CV). Our objective was to compare performance characteristics of RoGM _{ad hoc} and RoGM _Taylor to MD, SMD, and RoM. We used empiric data from systematic reviews reporting continuous outcomes and selected from each the meta‐analysis with the most and at least 5 trials (Cochrane Database [2008, Issue 1]). We supplemented this with simulations conducted with representative parameters. Pooled results were calculated using each effect measure. Of the reviews, 232/5053 met the inclusion criteria. Empiric data and simulation showed that RoGM _{ad hoc} exhibits more extreme treatment effects and greater heterogeneity than all other effect measures. Compared with MD, SMD, and RoM, RoGM _Taylor exhibits similar treatment effects, more heterogeneity when CV ≤0.7, and less heterogeneity when CV > 0.7. In conclusion, RoGM _Taylor may be considered for pooling continuous outcomes in meta‐analysis when data are skewed, but RoGM _{ad hoc} should not be used. However, clinicians' lack of familiarity with geometric means combined with acceptable performance characteristics of RoM in most situations suggests that RoM may be the preferable ratio method for pooling continuous outcomes in meta‐analysis. Copyright © 2012 John Wiley & Sons, Ltd.     

One‐stage individual participant data meta‐analysis models: estimation of treatment‐covariate interactions must avoid ecological bias by separating out within‐trial and across‐trial information

Stratified medicine utilizes individual‐level covariates that are associated with a differential treatment effect, also known as treatment‐covariate interactions. When multiple trials are available, meta‐analysis is used to help detect true treatment‐covariate interactions by combining their data. Meta‐regression of trial‐level information is prone to low power and ecological bias, and therefore, individual participant data (IPD) meta‐analyses are preferable to examine interactions utilizing individual‐level information. However, one‐stage IPD models are often wrongly specified, such that interactions are based on amalgamating within‐ and across‐trial information. We compare, through simulations and an applied example, fixed‐effect and random‐effects models for a one‐stage IPD meta‐analysis of time‐to‐event data where the goal is to estimate a treatment‐covariate interaction. We show that it is crucial to centre patient‐level covariates by their mean value in each trial, in order to separate out within‐trial and across‐trial information. Otherwise, bias and coverage of interaction estimates may be adversely affected, leading to potentially erroneous conclusions driven by ecological bias. We revisit an IPD meta‐analysis of five epilepsy trials and examine age as a treatment effect modifier. The interaction is ?0.011 (95% CI: ?0.019 to ?0.003; p = 0.004), and thus highly significant, when amalgamating within‐trial and across‐trial information. However, when separating within‐trial from across‐trial information, the interaction is ?0.007 (95% CI: ?0.019 to 0.005; p = 0.22), and thus its magnitude and statistical significance are greatly reduced. We recommend that meta‐analysts should only use within‐trial information to examine individual predictors of treatment effect and that one‐stage IPD models should separate within‐trial from across‐trial information to avoid ecological bias. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.     

Implementing informative priors for heterogeneity in meta‐analysis using meta‐regression and pseudo data

Many meta‐analyses combine results from only a small number of studies, a situation in which the between‐study variance is imprecisely estimated when standard methods are applied. Bayesian meta‐analysis allows incorporation of external evidence on heterogeneity, providing the potential for more robust inference on the effect size of interest. We present a method for performing Bayesian meta‐analysis using data augmentation, in which we represent an informative conjugate prior for between‐study variance by pseudo data and use meta‐regression for estimation. To assist in this, we derive predictive inverse‐gamma distributions for the between‐study variance expected in future meta‐analyses. These may serve as priors for heterogeneity in new meta‐analyses. In a simulation study, we compare approximate Bayesian methods using meta‐regression and pseudo data against fully Bayesian approaches based on importance sampling techniques and Markov chain Monte Carlo (MCMC). We compare the frequentist properties of these Bayesian methods with those of the commonly used frequentist DerSimonian and Laird procedure. The method is implemented in standard statistical software and provides a less complex alternative to standard MCMC approaches. An importance sampling approach produces almost identical results to standard MCMC approaches, and results obtained through meta‐regression and pseudo data are very similar. On average, data augmentation provides closer results to MCMC, if implemented using restricted maximum likelihood estimation rather than DerSimonian and Laird or maximum likelihood estimation. The methods are applied to real datasets, and an extension to network meta‐analysis is described. The proposed method facilitates Bayesian meta‐analysis in a way that is accessible to applied researchers. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.     

Predictive distributions for between‐study heterogeneity and simple methods for their application in Bayesian meta‐analysis

Numerous meta‐analyses in healthcare research combine results from only a small number of studies, for which the variance representing between‐study heterogeneity is estimated imprecisely. A Bayesian approach to estimation allows external evidence on the expected magnitude of heterogeneity to be incorporated. The aim of this paper is to provide tools that improve the accessibility of Bayesian meta‐analysis. We present two methods for implementing Bayesian meta‐analysis, using numerical integration and importance sampling techniques. Based on 14 886 binary outcome meta‐analyses in the Cochrane Database of Systematic Reviews, we derive a novel set of predictive distributions for the degree of heterogeneity expected in 80 settings depending on the outcomes assessed and comparisons made. These can be used as prior distributions for heterogeneity in future meta‐analyses. The two methods are implemented in R, for which code is provided. Both methods produce equivalent results to standard but more complex Markov chain Monte Carlo approaches. The priors are derived as log‐normal distributions for the between‐study variance, applicable to meta‐analyses of binary outcomes on the log odds‐ratio scale. The methods are applied to two example meta‐analyses, incorporating the relevant predictive distributions as prior distributions for between‐study heterogeneity. We have provided resources to facilitate Bayesian meta‐analysis, in a form accessible to applied researchers, which allow relevant prior information on the degree of heterogeneity to be incorporated. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.     

Development,validation and reproducibility of a food frequency questionnaire to measure flavonoid intake in older Australian adults

Aim

To develop and assess the validity and reproducibility of a food frequency questionnaire (FFQ) to measure total flavonoid intake, and individual flavonoid subclasses, in older adults.

Methods

Retrospective analysis of flavonoid intake in older adults informed the development of a FFQ to measure flavonoid intake and determine the flavonoid subclasses consumed (anthocyanins, flavan‐3‐ols, flavones, flavonols and flavanones). Older adults (n = 42, mean age 75.3 ± 8.6 years) attended two interviews 1 month apart where anthropometrics (height and weight), blood pressure (BP), demographic data and a 93‐item self‐administered FFQ were collected. A 4‐day food record (FR) was randomly administered between the two interview dates, and each food item was assigned a flavonoid and flavonoid subclass content using the United States Department of Agriculture flavonoid database. The criterion validity and reproducibility of the FFQ was assessed against a 4‐day FR using the Wilcoxon signed‐rank sum test, Spearman's correlation coefficient (r), Bland‐Altman Plots and Cohen's kappa.

Results

Total flavonoid intake was determined (median intake FFQ = 919.3 mg/day, FR = 781.4 mg/day). Tests of validity indicated that the FFQ consistently overestimated total flavonoid intake compared with the 4‐day FR. There was a significant difference in estimates between the FFQ and the 4‐day FR for total flavonoid intake (Wilcoxon signed‐rank sum P < 0.001; Bland‐Altman plots indicated large bias and wide limits of agreement), but they were well correlated (Spearman's r 0.93, P < 0.001; Cohen's kappa κ = 0.619, P < 0.001). For individual flavonoid subclasses, the tests of validity indicated greater discrepancy compared with 4‐day FR. The FFQ showed high reproducibility for estimating total flavonoid intake (FFQ1vsFFQ2: Wilcoxon signed‐rank sum test, P > 0.05; Spearman's r 0.91, P < 0.001; Bland‐Altman plots visually showed small, non‐significant bias and wide limits of agreement; and Cohen's kappa κ = 0.619, P < 0.001), with a small mean percentage difference (6.7%). For individual flavonoid subclasses, the tests of reproducibility between FFQ1 and FFQ2 showed similarly high reproducibility.

Conclusions

The developed FFQ appears suitable for satisfactorily ranking individuals according to total flavonoid intake. The FFQ shows limitations for estimating absolute total flavonoid intake and intake of flavonoid subclasses in comparison to a 4‐day FR in terms of overestimating intake. Refinement and further validation of this tool may be required.     

Beta‐binomial model for meta‐analysis of odds ratios

In meta‐analysis of odds ratios (ORs), heterogeneity between the studies is usually modelled via the additive random effects model (REM). An alternative, multiplicative REM for ORs uses overdispersion. The multiplicative factor in this overdispersion model (ODM) can be interpreted as an intra‐class correlation (ICC) parameter. This model naturally arises when the probabilities of an event in one or both arms of a comparative study are themselves beta‐distributed, resulting in beta‐binomial distributions. We propose two new estimators of the ICC for meta‐analysis in this setting. One is based on the inverted Breslow‐Day test, and the other on the improved gamma approximation by Kulinskaya and Dollinger (2015, p. 26) to the distribution of Cochran's Q. The performance of these and several other estimators of ICC on bias and coverage is studied by simulation. Additionally, the Mantel‐Haenszel approach to estimation of ORs is extended to the beta‐binomial model, and we study performance of various ICC estimators when used in the Mantel‐Haenszel or the inverse‐variance method to combine ORs in meta‐analysis. The results of the simulations show that the improved gamma‐based estimator of ICC is superior for small sample sizes, and the Breslow‐Day‐based estimator is the best for . The Mantel‐Haenszel‐based estimator of OR is very biased and is not recommended. The inverse‐variance approach is also somewhat biased for ORs≠1, but this bias is not very large in practical settings. Developed methods and R programs, provided in the Web Appendix, make the beta‐binomial model a feasible alternative to the standard REM for meta‐analysis of ORs. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.     

An alternative pseudolikelihood method for multivariate random‐effects meta‐analysis

Recently, multivariate random‐effects meta‐analysis models have received a great deal of attention, despite its greater complexity compared to univariate meta‐analyses. One of its advantages is its ability to account for the within‐study and between‐study correlations. However, the standard inference procedures, such as the maximum likelihood or maximum restricted likelihood inference, require the within‐study correlations, which are usually unavailable. In addition, the standard inference procedures suffer from the problem of singular estimated covariance matrix. In this paper, we propose a pseudolikelihood method to overcome the aforementioned problems. The pseudolikelihood method does not require within‐study correlations and is not prone to singular covariance matrix problem. In addition, it can properly estimate the covariance between pooled estimates for different outcomes, which enables valid inference on functions of pooled estimates, and can be applied to meta‐analysis where some studies have outcomes missing completely at random. Simulation studies show that the pseudolikelihood method provides unbiased estimates for functions of pooled estimates, well‐estimated standard errors, and confidence intervals with good coverage probability. Furthermore, the pseudolikelihood method is found to maintain high relative efficiency compared to that of the standard inferences with known within‐study correlations. We illustrate the proposed method through three meta‐analyses for comparison of prostate cancer treatment, for the association between paraoxonase 1 activities and coronary heart disease, and for the association between homocysteine level and coronary heart disease. © 2014 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.     

Measuring the statistical validity of summary meta‐analysis and meta‐regression results for use in clinical practice

An important question for clinicians appraising a meta‐analysis is: are the findings likely to be valid in their own practice—does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity—where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple (‘leave‐one‐out’) cross‐validation technique, we demonstrate how we may test meta‐analysis estimates for statistical validity using a new validation statistic, Vn , and derive its distribution. We compare this with the usual approach of investigating heterogeneity in meta‐analyses and demonstrate the link between statistical validity and homogeneity. Using a simulation study, the properties of Vn and the Q statistic are compared for univariate random effects meta‐analysis and a tailored meta‐regression model, where information from the setting (included as model covariates) is used to calibrate the summary estimate to the setting of application. Their properties are found to be similar when there are 50 studies or more, but for fewer studies Vn has greater power but a higher type 1 error rate than Q . The power and type 1 error rate of Vn are also shown to depend on the within‐study variance, between‐study variance, study sample size, and the number of studies in the meta‐analysis. Finally, we apply Vn to two published meta‐analyses and conclude that it usefully augments standard methods when deciding upon the likely validity of summary meta‐analysis estimates in clinical practice. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.     

Genome‐wide association scans for secondary traits using case‐control samples

Genome‐wide association studies (GWAS) require considerable investment, so researchers often study multiple traits collected on the same set of subjects to maximize return. However, many GWAS have adopted a case‐control design; improperly accounting for case‐control ascertainment can lead to biased estimates of association between markers and secondary traits. We show that under the null hypothesis of no marker‐secondary trait association, naïve analyses that ignore ascertainment or stratify on case‐control status have proper Type I error rates except when both the marker and secondary trait are independently associated with disease risk. Under the alternative hypothesis, these methods are unbiased when the secondary trait is not associated with disease risk. We also show that inverse‐probability‐of‐sampling‐weighted (IPW) regression provides unbiased estimates of marker‐secondary trait association. We use simulation to quantify the Type I error, power and bias of naïve and IPW methods. IPW regression has appropriate Type I error in all situations we consider, but has lower power than naïve analyses. The bias for naïve analyses is small provided the marker is independent of disease risk. Considering the majority of tested markers in a GWAS are not associated with disease risk, naïve analyses provide valid tests of and nearly unbiased estimates of marker‐secondary trait association. Care must be taken when there is evidence that both the secondary trait and tested marker are associated with the primary disease, a situation we illustrate using an analysis of the relationship between a marker in FGFR2 and mammographic density in a breast cancer case‐control sample. Genet. Epidemiol. 33:717–728, 2009. © 2009 Wiley‐Liss, Inc.     

Oral administration of mangiferin ameliorates diabetes in animal models: a meta-analysis and systematic review

Although mangiferin has a number of documented beneficial effects, there are no systematic reviews or meta-analyses of its effects in diabetic animal models. To investigate the effects of oral administration of mangiferin on blood glucose levels, body weight, and total cholesterol and triglycerides levels in diabetic animal models, a meta-analysis was conducted and the underlying mechanisms were reviewed. Studies from 6 databases (PubMed, Web of Science, Embase, Cochrane Library, and CNKI (China National Knowledge Infrastructure), and Wanfang Med) were searched from inception to April 2020. After article screening, a total of 19 articles were included in this meta-analysis. The meta-analysis was performed using RevMan 5.3 and STATA 14.0 software. The overall pooled estimate of standardized mean difference (SMD) of mangiferin's effect on blood glucose was −1.27 (95% confidence interval [CI]: −1.71, −0.82, P < .00001). Body weight increased in lean diabetic animals with an SMD of 1.41 (95% CI: 0.57, 2.25; P = .001), while it decreased in obese diabetic animals with an SMD of −0.92 (95% CI: −1.69, −0.14; P = .02). Mangiferin intake reduced serum total cholesterol and triglycerides levels with SMDs of −1.02 (95% CI: −1.43, −0.61; P < .001) and −1.24 (95% CI: −1.70, −0.79; P < .001), respectively. The meta-analysis suggests that oral intake of mangiferin has a significant antidiabetic effect in animal models, and the systematic review suggested that this function might be attributed to its anti-inflammatory and antioxidative properties, as well as to its function of improving glycolipid metabolism and enhancing insulin signaling.