Helicobacter pylori infection and colorectal cancer risk: a meta-analysis

BACKGROUND: Several studies suggested an association between Helicobacter pylori infection and colorectal carcinoma or adenoma risk. However, different authors reported quite varying estimates. We carried out a systematic review and meta-analysis of published studies investigating this association and paid special attention to the possibility of publication bias and sources of heterogeneity between studies. Materials and METHODS: An extensive literature search and cross-referencing were performed to identify all published studies. Summary estimates were obtained using random-effects models. The presence of possible publication bias was assessed using different statistical approaches. RESULTS: In a meta-analysis of the 11 identified human studies, published between 1991 and 2002, a summary odds ratio of 1.4 (95% CI, 1.1-1.8) was estimated for the association between H. pylori infection and colorectal cancer risk. The graphical funnel plot appeared asymmetrical, but the formal statistical evaluations did not provide strong evidence of publication bias. The proportion of variation of study results because of heterogeneity was small (36.5%). CONCLUSIONS: The results of our meta-analysis are consistent with a possible small increase in risk of colorectal cancer because of H. pylori infection. However, the possibility of some publication bias cannot be ruled out, although it could not be statistically confirmed. Larger, better designed and better controlled studies are needed to clarify the situation.     

Confidence intervals and P-values for meta-analysis with publication bias

We study publication bias in meta-analysis by supposing there is a population (y, sigma) of studies which give treatment effect estimates y approximately N(theta, sigma(2)). A selection function describes the probability that each study is selected for review. The overall estimate of theta depends on the studies selected, and hence on the (unknown) selection function. Our previous paper, Copas and Jackson (2004, Biometrics 60, 146-153), studied the maximum bias over all possible selection functions which satisfy the weak condition that large studies (small sigma) are as likely, or more likely, to be selected than small studies (large sigma). This led to a worst-case sensitivity analysis, controlling for the overall fraction of studies selected. However, no account was taken of the effect of selection on the uncertainty in estimation. This article extends the previous work by finding corresponding confidence intervals and P-values, and hence a new sensitivity analysis for publication bias. Two examples are discussed.     

Inferences about the between-study variance in meta-analysis with normally distributed outcomes

This paper presents a new approach for confidence interval estimation of the between-study variance in meta-analysis with normally distributed responses based on the concepts of generalized variables. Simulation study shows that the coverage probabilities of the proposed confidence intervals are generally satisfactory. Moreover, the proposed approach can easily provide P -values for hypothesis testing. For meta-analysis of controlled clinical trials or epidemiological studies, within which the responses are normally distributed, the proposed approach is an ideal candidate for making inference about the between-study variance.     

An association between − 866G/A polymorphism in the promoter of UCP2 and obesity: A meta-analysis

− 866G/A polymorphism in the promoter of UCP2 gene has been reported to be associated with obesity, but the results remain inconclusive. To assess the relation of UCP2 − 866G/A polymorphism and obesity susceptibility, a meta-analysis was performed. PubMed, ISI, Wanfang database, VIP and CBM were searched to identify relevant studies up to July 31, 2012. Odds ratios (OR) and 95% confidence interval (95% CI) were pooled using fixed or random effect models. Subgroup analysis was performed by ethnicity (categorized as Asian and European). Heterogeneity and publication bias evaluation were performed to validate the credibility. Meta-regression and the ‘leave one out’ sensitive analysis were used to explore the potential sources of between-study heterogeneity. 14 studies were included in this meta-analysis. After exclusion of articles that deviated from the HWE in controls, and were the key contributors to between-study heterogeneity, the meta-analysis showed a significant association of the A allele with reduced risk of obesity in overall analysis and in European in the dominant, codominant and additional models. In Asian, no significant association was found between the − 866G/A in UCP2 gene and obesity susceptibility. The meta-analysis suggested that UCP2 − 866G/A polymorphism was associated with obesity. The A allele may be an important protective factor for obesity in European, but not in Asian. Further studies are needed to elucidate the relationship.     

Adjusting for publication bias in meta-analysis via inverse probability weighting using clinical trial registries

Publication bias is a major concern in conducting systematic reviews and meta-analyses. Various sensitivity analysis or bias-correction methods have been developed based on selection models, and they have some advantages over the widely used trim-and-fill bias-correction method. However, likelihood methods based on selection models may have difficulty in obtaining precise estimates and reasonable confidence intervals, or require a rather complicated sensitivity analysis process. Herein, we develop a simple publication bias adjustment method by utilizing the information on conducted but still unpublished trials from clinical trial registries. We introduce an estimating equation for parameter estimation in the selection function by regarding the publication bias issue as a missing data problem under the missing not at random assumption. With the estimated selection function, we introduce the inverse probability weighting (IPW) method to estimate the overall mean across studies. Furthermore, the IPW versions of heterogeneity measures such as the between-study variance and the I² measure are proposed. We propose methods to construct confidence intervals based on asymptotic normal approximation as well as on parametric bootstrap. Through numerical experiments, we observed that the estimators successfully eliminated bias, and the confidence intervals had empirical coverage probabilities close to the nominal level. On the other hand, the confidence interval based on asymptotic normal approximation is much wider in some scenarios than the bootstrap confidence interval. Therefore, the latter is recommended for practical use.     

Presenilin-2 polymorphisms and risk of sporadic AD: Evidence from a meta-analysis

Association studies of presenilin-2 (PSEN2) polymorphisms and sporadic Alzheimer's disease (AD) have yielded inconsistent results, possibly because single studies often lack sufficient statistical power. In this study, we performed a meta-analysis to evaluate the association of the two most extensively studied PSEN2 polymorphisms, rs8383 and 5′indel, with the risk of sporadic AD. We systematically reviewed relevant studies retrieved by Medline, Pubmed, Embase, AlzGene, and CNKI. Data were analyzed using the Stata (v11.0) software package. The fixed effects model or random-effects model were applied depending on between-study heterogeneity. Publication bias was evaluated using Egger's test and Begg's funnel plots. Overall, the meta-analysis included 6 case–control studies for each polymorphism with 2186 confirmed AD cases and 2507 healthy controls in total. Analysis suggested a significant association between SNP rs8383 polymorphism and AD risk with no evidence of between-study heterogeneity or publication bias. In contrast, we found no evidence for an association between the 5′indel polymorphism and AD risk. Further stratified analyses by apolipoprotein ε4 status or ethnicity also failed to reveal a statistically significant association between the 5′indel polymorphism of PSEN2 and AD risk. Our analysis supports the hypothesis that the PSEN2 rs8383 polymorphism is associated with an enlarged risk of sporadic AD. However, larger scale association studies are necessary to further validate the association of PSEN2 polymorphisms with sporadic AD risk and to define potential gene–gene interactions.     

Systematic review of the empirical evidence of study publication bias and outcome reporting bias

Background

The increased use of meta-analysis in systematic reviews of healthcare interventions has highlighted several types of bias that can arise during the completion of a randomised controlled trial. Study publication bias has been recognised as a potential threat to the validity of meta-analysis and can make the readily available evidence unreliable for decision making. Until recently, outcome reporting bias has received less attention.

Methodology/Principal Findings

We review and summarise the evidence from a series of cohort studies that have assessed study publication bias and outcome reporting bias in randomised controlled trials. Sixteen studies were eligible of which only two followed the cohort all the way through from protocol approval to information regarding publication of outcomes. Eleven of the studies investigated study publication bias and five investigated outcome reporting bias. Three studies have found that statistically significant outcomes had a higher odds of being fully reported compared to non-significant outcomes (range of odds ratios: 2.2 to 4.7). In comparing trial publications to protocols, we found that 40–62% of studies had at least one primary outcome that was changed, introduced, or omitted. We decided not to undertake meta-analysis due to the differences between studies.

Conclusions

Recent work provides direct empirical evidence for the existence of study publication bias and outcome reporting bias. There is strong evidence of an association between significant results and publication; studies that report positive or significant results are more likely to be published and outcomes that are statistically significant have higher odds of being fully reported. Publications have been found to be inconsistent with their protocols. Researchers need to be aware of the problems of both types of bias and efforts should be concentrated on improving the reporting of trials.     

Meta-analysis of genetic-linkage analysis of quantitative-trait loci

Meta-analysis is an important tool in linkage analysis. The pooling of results across primary linkage studies allows greater statistical power to detect quantitative-trait loci (QTLs) and more-precise estimation of their genetic effects and, hence, yields conclusions that are stronger relative to those of individual studies. Previous methods for the meta-analysis of linkage studies have been proposed, and, although some methods address the problem of between-study heterogeneity, most methods still require linkage analysis at the same marker or set of markers across studies, whereas others do not result in an estimate of genetic variance. In this study, we present a meta-analytic procedure to evaluate evidence from several studies that report Haseman-Elston statistics for linkage to a QTL at multiple, possibly distinct, markers on a chromosome. This technique accounts for between-study heterogeneity and estimates both the location of the QTL and the magnitude of the genetic effect more precisely than does an individual study. We also provide standard errors for the genetic effect and for the location (in cM) of the QTL, using a resampling method. The approach can be applied under other conditions, provided that the various studies use the same linkage statistic.     

An alternative model for bivariate random-effects meta-analysis when the within-study correlations are unknown

Multivariate meta-analysis models can be used to synthesize multiple, correlated endpoints such as overall and disease-free survival. A hierarchical framework for multivariate random-effects meta-analysis includes both within-study and between-study correlation. The within-study correlations are assumed known, but they are usually unavailable, which limits the multivariate approach in practice. In this paper, we consider synthesis of 2 correlated endpoints and propose an alternative model for bivariate random-effects meta-analysis (BRMA). This model maintains the individual weighting of each study in the analysis but includes only one overall correlation parameter, rho, which removes the need to know the within-study correlations. Further, the only data needed to fit the model are those required for a separate univariate random-effects meta-analysis (URMA) of each endpoint, currently the common approach in practice. This makes the alternative model immediately applicable to a wide variety of evidence synthesis situations, including studies of prognosis and surrogate outcomes. We examine the performance of the alternative model through analytic assessment, a realistic simulation study, and application to data sets from the literature. Our results show that, unless rho is very close to 1 or -1, the alternative model produces appropriate pooled estimates with little bias that (i) are very similar to those from a fully hierarchical BRMA model where the within-study correlations are known and (ii) have better statistical properties than those from separate URMAs, especially given missing data. The alternative model is also less prone to estimation at parameter space boundaries than the fully hierarchical model and thus may be preferred even when the within-study correlations are known. It also suitably estimates a function of the pooled estimates and their correlation; however, it only provides an approximate indication of the between-study variation. The alternative model greatly facilitates the utilization of correlation in meta-analysis and should allow an increased application of BRMA in practice.     

Systematic Review of the Empirical Evidence of Study Publication Bias and Outcome Reporting Bias — An Updated Review

Background

The increased use of meta-analysis in systematic reviews of healthcare interventions has highlighted several types of bias that can arise during the completion of a randomised controlled trial. Study publication bias and outcome reporting bias have been recognised as a potential threat to the validity of meta-analysis and can make the readily available evidence unreliable for decision making.

Methodology/Principal Findings

In this update, we review and summarise the evidence from cohort studies that have assessed study publication bias or outcome reporting bias in randomised controlled trials. Twenty studies were eligible of which four were newly identified in this update. Only two followed the cohort all the way through from protocol approval to information regarding publication of outcomes. Fifteen of the studies investigated study publication bias and five investigated outcome reporting bias. Three studies have found that statistically significant outcomes had a higher odds of being fully reported compared to non-significant outcomes (range of odds ratios: 2.2 to 4.7). In comparing trial publications to protocols, we found that 40–62% of studies had at least one primary outcome that was changed, introduced, or omitted. We decided not to undertake meta-analysis due to the differences between studies.

Conclusions

This update does not change the conclusions of the review in which 16 studies were included. Direct empirical evidence for the existence of study publication bias and outcome reporting bias is shown. There is strong evidence of an association between significant results and publication; studies that report positive or significant results are more likely to be published and outcomes that are statistically significant have higher odds of being fully reported. Publications have been found to be inconsistent with their protocols. Researchers need to be aware of the problems of both types of bias and efforts should be concentrated on improving the reporting of trials.     

Meta-analysis of studies with missing data

Summary .  Consider a meta-analysis of studies with varying proportions of patient-level missing data, and assume that each primary study has made certain missing data adjustments so that the reported estimates of treatment effect size and variance are valid. These estimates of treatment effects can be combined across studies by standard meta-analytic methods, employing a random-effects model to account for heterogeneity across studies. However, we note that a meta-analysis based on the standard random-effects model will lead to biased estimates when the attrition rates of primary studies depend on the size of the underlying study-level treatment effect. Perhaps ignorable within each study, these types of missing data are in fact not ignorable in a meta-analysis. We propose three methods to correct the bias resulting from such missing data in a meta-analysis: reweighting the DerSimonian–Laird estimate by the completion rate; incorporating the completion rate into a Bayesian random-effects model; and inference based on a Bayesian shared-parameter model that includes the completion rate. We illustrate these methods through a meta-analysis of 16 published randomized trials that examined combined pharmacotherapy and psychological treatment for depression.     

Using journal impact factors to correct for the publication bias of medical studies

Publication bias of the results of medical studies can invalidate evidence-based medicine. The existing methodology for modeling this essentially relies upon the symmetry of the funnel plot. We present a new method of modeling publication bias that uses this information plus the impact factors of the publishing journals. A simple model of the publication process enables the estimation of bias-corrected intervention effects. The procedure is illustrated using a meta-analysis of the effectiveness of single-dose oral aspirin for acute pain, and results are also obtained for five other meta-analyses. The method enables the fitting of a wide range of models and is considered more flexible than other ways of compensating for publication bias. The model also provides the basis of a statistical test for the existence of publication bias. Use of the new methodology to supplement existing methods is recommended, in the context of a sensitivity analysis.     

A quantitative synthesis of pollen supplementation experiments highlights the contribution of resource reallocation to estimates of pollen limitation

Our understanding of pollen limitation depends on a realistic view of its magnitude. Previous reviews of pollen supplementation experiments concluded that a majority of plant species suffers from pollen limitation and that its magnitude is high. Here, we perform a meta-analysis and find evidence that publication bias, experimental design, and the response variable chosen all influence the magnitude of pollen limitation. Fail-safe numbers indicate that publication bias exists for some measures of pollen limitation; significant results are more likely to be published and therefore available for review. Moreover, experiments conducted on only a fraction of a plant's flowers and reproductive episodes report ~8-fold higher effect sizes than those on all flowers produced over the entire lifetime, likely because resource reallocation among flowers and across years contributes to estimates of pollen limitation. Studies measuring percentage fruit set report higher values of pollen limitation than those measuring other response variables, such as seeds per fruit, perhaps because many plant species will not produce fruits unless adequate pollen receipt occurs to fertilize most ovules. We offer suggestions for reducing the bias introduced by methodology in pollen supplementation experiments and discuss our results in the context of optimality theory.     

Multivariate Meta-Analysis of Preference-Based Quality of Life Values in Coronary Heart Disease

Background

There are numerous health-related quality of life (HRQol) measurements used in coronary heart disease (CHD) in the literature. However, only values assessed with preference-based instruments can be directly applied in a cost-utility analysis (CUA).

Objective

To summarize and synthesize instrument-specific preference-based values in CHD and the underlying disease-subgroups, stable angina and post-acute coronary syndrome (post-ACS), for developed countries, while accounting for study-level characteristics, and within- and between-study correlation.

Methods

A systematic review was conducted to identify studies reporting preference-based values in CHD. A multivariate meta-analysis was applied to synthesize the HRQoL values. Meta-regression analyses examined the effect of study level covariates age, publication year, prevalence of diabetes and gender.

Results

A total of 40 studies providing preference-based values were detected. Synthesized estimates of HRQoL in post-ACS ranged from 0.64 (Quality of Well-Being) to 0.92 (EuroQol European”tariff”), while in stable angina they ranged from 0.64 (Short form 6D) to 0.89 (Standard Gamble). Similar findings were observed in estimates applying to general CHD. No significant improvement in model fit was found after adjusting for study-level covariates. Large between-study heterogeneity was observed in all the models investigated.

Conclusions

The main finding of our study is the presence of large heterogeneity both within and between instrument-specific HRQoL values. Current economic models in CHD ignore this between-study heterogeneity. Multivariate meta-analysis can quantify this heterogeneity and offers the means for uncertainty around HRQoL values to be translated to uncertainty in CUAs.     

Statin Use and Risk of Lung Cancer: a Meta-Analysis of Observational Studies and Randomized Controlled Trials

Clinical studies have shown that statin use may alter the risk of lung cancer. However, these studies yielded different results. To quantify the association between statin use and risk of lung cancer, we performed a detailed meta-analysis. A literature search was carried out using MEDLINE, EMBASE and COCHRANE database between January 1966 and November 2012. Before meta-analysis, between-study heterogeneity and publication bias were assessed using adequate statistical tests. Fixed-effect and random-effect models were used to calculate the pooled relative risks (RR) and corresponding 95% confidence intervals (CIs). Subgroup analyses, sensitivity analysis and cumulative meta-analysis were also performed. A total of 20 (five randomized controlled trials, eight cohorts, and seven case–control) studies contributed to the analysis. Pooled results indicated a non-significant decrease of total lung cancer risk among all statin users (RR = 0.89, 95% CI [0.78, 1.02]). Further, long-term statin use did not significantly decrease the risk of total lung cancer (RR = 0.80, 95% CI [0.39 , 1.64]). In our subgroup analyses, the results were not substantially affected by study design, participant ethnicity, or confounder adjustment. Furthermore, sensitivity analysis confirmed the stability of results. The findings of this meta-analysis suggested that there was no significant association between statin use and risk of lung cancer. More studies, especially randomized controlled trials and high quality cohort studies are warranted to confirm this association.     

The file-drawer problem revisited: a general weighted method for calculating fail-safe numbers in meta-analysis

Quantitative literature reviews such as meta-analysis are becoming common in evolutionary biology but may be strongly affected by publication biases. Using fail-safe numbers is a quick way to estimate whether publication bias is likely to be a problem for a specific study. However, previously suggested fail-safe calculations are unweighted and are not based on the framework in which most meta-analyses are performed. A general, weighted fail-safe calculation, grounded in the meta-analysis framework, applicable to both fixed- and random-effects models, is proposed. Recent meta-analyses published in Evolution are used for illustration.     

Cumulative meta-analysis: a new tool for detection of temporal trends and publication bias in ecology

Temporal changes in the magnitude of research findings have recently been recognized as a general phenomenon in ecology, and have been attributed to the delayed publication of non-significant results and disconfirming evidence. Here we introduce a method of cumulative meta-analysis which allows detection of both temporal trends and publication bias in the ecological literature. To illustrate the application of the method, we used two datasets from recently conducted meta-analyses of studies testing two plant defence theories. Our results revealed three phases in the evolution of the treatment effects. Early studies strongly supported the hypothesis tested, but the magnitude of the effect decreased considerably in later studies. In the latest studies, a trend towards an increase in effect size was observed. In one of the datasets, a cumulative meta-analysis revealed publication bias against studies reporting disconfirming evidence; such studies were published in journals with a lower impact factor compared to studies with results supporting the hypothesis tested. Correlation analysis revealed neither temporal trends nor evidence of publication bias in the datasets analysed. We thus suggest that cumulative meta-analysis should be used as a visual aid to detect temporal trends and publication bias in research findings in ecology in addition to the correlative approach.     

Statistical issues in a metaregression analysis of randomized trials: impact on the dietary sodium intake and blood pressure relationship

In a meta-analysis of randomized trials of the effects of dietary sodium interventions on blood pressure, we found substantial heterogeneity among the studies. We were interested in evaluating whether measurement error, known to be a problem for dietary sodium measures, publication bias, or confounding factors could be responsible for the heterogeneity. A measurement error correction was developed that corrects both the slope and the intercept and takes into account the sample size of each study and the number of measurements taken on an individual. The measurement error correction had a minimal effect on the estimates, although it performed well in simulated data. A smoothed scatter plot was used to assess publication bias. Metaregressions provide a convenient way to jointly assess the effects of several factors, but care must be taken to fit an appropriate model.     

Human fluctuating asymmetry in relation to health and quality: a meta-analysis

Developmental instability (DI) reflects the inability of a developing organism to buffer its development against random perturbations, due either to frequent, large perturbations or to a poor buffering system. The primary measure used to assess DI experienced by an individual organism is fluctuating asymmetry (FA), asymmetry of bilateral features that are, on average in a population, symmetrical. A large literature on FA in humans in relation to measures of health and quality (close to 100 studies and nearly 300 individual effect size estimates) has accumulated. This paper presents the first quantitative meta-analysis of this literature. The mean effect size (scaled as Pearson r) was about 0.2. Effect sizes covaried negatively with sample size, consistent with effects of publication bias, the tendency for significant effects to be published. Conservative correction for this bias reduced the mean effect to about 0.1. Associations with FA underestimate effects of underlying DI due to imprecise measurement of the latter. A model-based best estimate of the mean effect of DI on outcomes is about 0.3, a theoretically meaningful, relatively large effect, albeit of moderate absolute size. The data are consistent, however, with a range of true effect sizes between 0.08 and 0.67, partly due to large study effects. Study-specific effect sizes in DI ranged between −0.2 and 1.0. A humbling and perhaps sobering conclusion is that, in spite of a large body of literature involving nearly 50 000 participants, we can only confidently state that there is on average a robust positive average effect size. An accurate estimate of that effect size was not possible, and between-study variation remained largely unexplained. We detected no robust variation across six broad categories of outcomes (health and disease, fetal outcomes, psychological maladaptation, reproduction, attractiveness and hormonal effects), though examination of narrower domains reveal some corrected effects close to 0.2 and others near zero. The meta-analysis suggests fruitful directions for future research and theory.     

Association between 3801T>C Polymorphism of CYP1A1 and Idiopathic Male Infertility Risk: A Systematic Review and Meta-Analysis

Background

Epidemiological studies have evaluated the association between 3801T>C polymorphism of CYP1A1 gene and the risk for idiopathic male infertility, but the results are inconclusive. We aimed to derive a more precise estimation of the relationship by conducting a meta-analysis of case-control studies.

Methods

This study conformed to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, Embase and CNKI databases were searched through November 2013 to identify relevant studies. Pooled odds ratios with 95% confidence intervals were used to assess the strength of the association between CYP1A1 3801T>C polymorphism and idiopathic male infertility risk. Q-test was performed to evaluate between-study heterogeneity and publication bias was appraised using funnel plots. Sensitivity analyses were conducted to evaluate the robustness of meta-analysis findings.

Results

Six studies involving 1,060 cases and 1,225 controls were included in this meta-analysis. Overall, significant associations between 3801T>C polymorphism and idiopathic male infertility risk were observed in allelic comparison (OR = 1.36, 95% CI: 1.01–1.83), homozygous model (OR = 2.18, 95% CI: 1.15–4.12), and recessive model (OR = 1.86, 95% CI: 1.09–3.20), with robust findings according to sensitivity analyses. However, subgroup analyses did not further identify the susceptibility to idiopathic male infertility in all comparisons. Funnel plot inspections did not reveal evidence of publication bias.

Conclusions

The current meta-analysis provides evidence of a significant association between CYP1A1 3801T>C polymorphism and idiopathic male infertility risk. Considering the limitation inherited from the eligible studies, further confirmation in large-scale and well-designed studies is needed.