Heterogeneity can manifest in two ways, with corresponding procedures. Impact of multidrug resistant bacteria on economic and. Heterogeneity in metaanalysis q, isquare statsdirect. These interventions include acupressure, massage, tai chi, qi gong, electroacupuncture and use of chinese herbal medicinesfor example, in enemas, foot massage and compressing the umbilicus. M an aggregate statistic, to identify systematic heterogeneity patterns and their direction of effect in metaanalysis. Recommended softwarepackages for metaanalysis of diagnostic.
Evidencebased mapping of design heterogeneity prior to. Meta analysis is a statistical technique, or set of statistical techniques, for summarising the results of several studies into a single estimate. A metaanalysis integrates the quantitative findings from separate but similar studies and provides a numerical estimate of the overall effect of interest petrie et al. For simplicity, we use the term metaanalysis in the remainder of the article. In contrast, a fixedeffect analysis assumes that a single common effect underlies every study included in the metaanalysis. Because of loss of power, nonsignificant heterogeneity within a subgroup may. Sep 06, 2003 an alternative quantification of heterogeneity in a metaanalysis is the amongstudy variance often called. This strategy effectively documents design heterogeneity, thus improving the practice of metaanalysis by aiding in.
Stata module to quantify heterogeneity in a meta analysis, statistical software components s449201, boston college department of economics, revised 25 jan 2006. Metaanalysis has become a popular tool for increasing power in genetic association studies, yet it remains a methodological challenge. Most metaanalysis programs perform inversevariance metaanalyses. Metaanalysis in biological sciences, especially in ecology and evolution which we refer to as biological metaanalysis faces somewhat different methodological problems from its counterparts in medical and social sciences, where metaanalytic. Oct 28, 2010 the randomeffects meta analysis attempts to account for this distribution of effects and provides a more conservative estimate of the effect. There are 3 types of heterogeneity commonly considered in meta analysis. Methodological and clinical heterogeneity and extraction. Methodological heterogeneity refers to differences in the way that studies were. Is there any statical software for calculation of heterogenity in a.
This meta analysis evaluated the use of adjuvant chemotherapy for resectable gastric cancer including a total of 3781 patients with a cer 69%, rrr 9% and 24% heterogeneity reported by the meta analysis. Methodological issues and advances in biological metaanalysis. These random effects models and software packages mentioned above. It is the method in which multiple interventions that is, three or more are compared using both direct comparisons of interventions within randomized controlled trials and indirect comparisons. This module should be installed from within stata by typing ssc install heterogi. We searched databases medline, embase, cinahl, cochrane library, and consort, to. Conversely, q has too much power as a test of heterogeneity if the number of studies is large higgins et al. My own view is that any amount of heterogeneity is acceptable, providing both that the predefined eligibility criteria for the metaanalysis are sound and that the data are correct. Since then, the statistical methods evolved from simply following the approaches used for intervention meta analyses to the summary roc sroc model also known as moseslittenberg model which takes in to account the threshold effect, and then to more advanced. In contrast, a fixedeffect analysis assumes that a single common effect underlies every study included in the meta analysis.
Q is included in each statsdirect metaanalysis function because it forms part of the dersimonianlaird random effects pooling method dersimonian and laird 1985. Metaanalysis seeks to understand heterogeneity in addition to computing a summary risk estimate. For example, when there are many studies in a metaanalysis, one may obtain a tight confidence interval around the randomeffects estimate of the mean effect even when there is a large amount of heterogeneity. In statistics, study heterogeneity is a problem that can arise when attempting to undertake a meta analysis.
Exploring sources of heterogeneity 2 metaregression form of subgroup analysis that allows consideration of continuous variables, e. In common with other meta analysis software, revman presents an estimate of the betweenstudy variance in a randomeffects meta analysis. Stata module to quantify heterogeneity in a metaanalysis, statistical software components s449201, boston college department of economics, revised 25 jan 2006. The greek root meta means with, along, after, or later, so here we have an. An alternative quantification of heterogeneity in a meta analysis is the amongstudy variance often called. Software packages supporting clinical metaanalyses include the excel. I am doing a meta analysis for my thesis on 3 treatment options in treating achalasia. Assessment of the betweenstudy heterogeneity is an essential component of metaanalysis. Quantifying systematic heterogeneity in metaanalysis. The last of these is quantified by the i 2statistic.
From the within study results, i can see that results from two of the studies are in the same direction while the results from the 3rd study is null. The effects of clinical and statistical heterogeneity on the. So, if one brings together different studies for analysing them or doing a metaanalysis, it is clear that there will be differences found. The dilemma of heterogeneity tests in metaanalysis. There are methods for assessing and addressing heterogeneity that we discuss in detail in. Also seemeta meta esize for how to compute various effect sizes in a metaanalysis. From the within study results, i can see that results from two of the studies are in the same direction while the. Metaanalysis is a quantitative technique that uses specific measures e. Genetic association studies can differ from each other in terms of environmental conditions, study design, population types and sizes, statistical noise, and analytical use of covariates. Ideally, the studies whose results are being combined in the metaanalysis should all be undertaken in the same way and to the same experimental protocols. The randomeffects metaanalysis attempts to account for this distribution of effects and provides a more conservative estimate of the effect. M quantitatively describes systematic nonrandom heterogeneity patterns acting across multiple variants in a gwas metaanalysis. Nov 16, 2016 metaanalysis, complexity, and heterogeneity.
Q is included in each statsdirect meta analysis function because it forms part of the dersimonianlaird random effects pooling method dersimonian and laird 1985. Network metaanalysis nma is an extension of pairwise metaanalysis that facilitates comparisons of multiple interventions over a single analysis. The opposite of heterogeneity is homogeneity meaning that all studies show the same effect. First, like primary research studies synthesized in a meta analysis, methods used in a meta analysis should be fully transparent and reproducible.
Dealing with heterogeneity in metaanalyses is often tricky, and there is only limited advice for authors on what to do. Dealing with heterogeneity in meta analyses is often tricky, and there is only limited advice for authors on what to do. Impact of heterogeneity and effect size on the estimation of. Variance between studies in a metaanalysis will exist.
Hi all, i am using metal for metaanalysis of some specific snps 6 snps of interest across three studies. Methodological considerations in network metaanalysis. Statistical heterogeneity is the term given to differences in the effects of interventions and comes about because of clinical andor methodological differences between studies ie it is a consequence. The historical roots of meta analysis can be traced back to 17th century studies of astronomy, while a paper published in 1904 by the statistician karl pearson in the british medical journal which collated data from several studies of typhoid inoculation is seen as the first time a meta analytic approach was used to aggregate the outcomes of multiple clinical studies. The core mission of this kind of test is to identify data sets from. By convention, the null hypothesis is rejected if the chisquare test has p heterogeneity is not something to be afraid of, it just means that there is variability in your data. Figure 3 shows the six casecontrol studies of magnetic fields and leukaemia broken down into two subgroups based on assessment of their quality. Heterogeneity, metaanalysis and metaregression modules facilitate.
In statistics, study heterogeneity is a problem that can arise when attempting to undertake a metaanalysis. This is more useful for comparisons of heterogeneity among subgroups, but values depend on the treatment effect scale. In recent years, a number of new methods have been developed to meet these challenges. In this lecture we look at how to deal with it when we have it. Quantifying systematic heterogeneity in metaanalysis view on github. Metaanalysis in biological sciences, especially in ecology and evolution which we refer to as biological metaanalysis faces somewhat different methodological problems from its counterparts in medical and. Hi all, i am using metal for meta analysis of some specific snps 6 snps of interest across three studies. However, heterogeneity is still the threat to the validity and quality of such studies. Meta analysis is a quantitative technique that uses specific measures e. Sensitivity of betweenstudy heterogeneity in metaanalysis. Another important consideration for metaanalysis is that of the underlying model.
It is typically a result of clinical heterogeneity, methodological heterogeneity, or both. The core mission of this kind of test is to identify data sets from similar. Currently, q and its descendant i2 i square tests are widely used as the tools for heterogeneity evaluation. It has been almost 30 years since the publication of the first metaanalysis of diagnostic test accuracy dta. In common with other metaanalysis software, revman presents an estimate of the betweenstudy variance in a randomeffects metaanalysis. Study heterogeneity an overview sciencedirect topics. This article provides an introduction to the metaanalysis literature and discusses the challenges of applying metaanalysis to human dimensions research. A metaanalysis is a statistical analysis that combines the results of multiple scientific studies. Methodological standards for metaanalyses and qualitative. Openmee was developed to make advanced methods for statistical research synthesis, based on best practices, available without cost to the scientific. We investigated how authors addressed different degrees of heterogeneity, in particular whether they used a fixed effect model, which assumes that all the included studies are estimating the same true effect, or a random effects model where this is. First, like primary research studies synthesized in a metaanalysis, methods used in a metaanalysis should be fully transparent and reproducible. Heterogeneity is not something to be afraid of, it just means that there is variability in your data. Ideally, the studies whose results are being combined in the meta analysis should all be undertaken in the same way and to the same experimental protocols.
Systematic heterogeneity can arise in a meta analysis due to differences in the study characteristics of participating studies. The results for the test of heterogeneity for the meta analysis of fall related injuries are displayed towards the bottom of the forest plot in the line test for heterogeneity. However, due to clinical and methodological heterogeneity, estimates about the attributable economic and clinical effects of healthcareassociated infections hai due to mdr microorganisms mdr hai remain unclear. Functions for metaanalysis and methodology soundness. Assessment of the betweenstudy heterogeneity is an essential component of meta analysis. Metadisc also allows exploration of heterogeneity chisquare, cochranq and. These interventions include acupressure, massage, tai chi, qi gong, electroacupuncture and use of chinese herbal medicinesfor example, in enemas, foot. For example, when there are many studies in a meta analysis, one may obtain a tight confidence interval around the randomeffects estimate of the mean effect even when there is a large amount of heterogeneity. This qualitative interview study aimed to understand researchers understanding of complexity and heterogeneity and the factors which may influence the choices researchers.
With a randomeffects metaanalysis, the 95% ci of the effect estimate contains the true relative risk 0. The randomeffects meta analysis attempts to account for this distribution of effects and provides a more conservative estimate of the effect. It should be noted that the decision to focus on patient diagnoses and comparator duration is an. From the standpoint that heterogeneity is inevitable in a metaanalysis, we are left with the question of whether there is an acceptable degree of heterogeneity. Meta analysis is a statistical method that combines quantitative findings from previous studies. While there is some consensus on methods for investigating statistical and methodological heterogeneity, little attention has been paid to clinical aspects of heterogeneity.
While the metaanalytic methodology is similar for systematic and rapid. A critical appraisal of the methodology and quality of. Third, some scientists argue that the objective coding procedure used in metaanalysis ignores the context of each individual study, such as its methodological rigor. Different weights are assigned to the different studies for calculating the summary or pooled effect. The software described in this manual is furnished under a license agreement or nondisclosure agreement. Sep 14, 2016 meta analysis has become a popular tool for increasing power in genetic association studies, yet it remains a methodological challenge.
So, if one brings together different studies for analysing them or doing a meta analysis, it is clear that there will be differences found. For simplicity, we use the term meta analysis in the remainder of the article. Significant statistical heterogeneity arising from methodological diversity or differences. Its analysis is crucial for defining whether selected primary studies pooling is fit for metaanalysis. It has been almost 30 years since the publication of the first meta analysis of diagnostic test accuracy dta.
Background infections with multidrug resistant mdr bacteria in hospital settings have substantial implications in terms of clinical and economic outcomes. This qualitative interview study aimed to understand researchers understanding of complexity and heterogeneity and the factors which may influence the choices researchers make in synthesising complex data. Flawed metaanalytic methodology is common in many fields such as oncology. This heterogeneity may be of clinical, methodological or statistical origin. There are 3 types of heterogeneity commonly considered in metaanalysis. Introduction after several decades development, metaanalysis has become the pillar of evidencebased medicine. There is a need for sound methodological guidance on how to.
Since then, the statistical methods evolved from simply following the approaches used for intervention metaanalyses to the summary roc sroc model also known as moseslittenberg model which takes in to account the threshold effect, and. It has been increasingly used to obtain more credible results in a wide range of scientific fields. We investigated how authors addressed different degrees of heterogeneity, in particular whether they used a fixed effect model, which assumes that all the included studies are estimating the same true effect, or a random effects model where this is not assumed. Fourth, when a researcher includes lowquality studies in a metaanalysis, the limitations of these studies impact the mean effect size i. Contents chapter 1 introduction 9 chapter 2 baseline risk as predictor of treatment benefit 17 chapter 3 advanced methods in metaanalysis.
844 1450 1309 1215 771 1181 1163 637 807 1261 43 665 641 92 1494 1287 1185 836 760 592 223 461 604 1349 1233 775 627 1230 159 467 1488 1464 106 735 139 1093 891 1481 1113