Dealing with heterogeneity in meta analyses is often tricky, and there is only limited advice for authors on what to do. Contents chapter 1 introduction 9 chapter 2 baseline risk as predictor of treatment benefit 17 chapter 3 advanced methods in metaanalysis. Software packages supporting clinical metaanalyses include the excel. 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. Metaanalysis is a statistical method that combines quantitative findings from previous studies. Metaanalysis seeks to understand heterogeneity in addition to computing a summary risk estimate.
Heterogeneity can manifest in two ways, with corresponding procedures. It has been almost 30 years since the publication of the first meta analysis of diagnostic test accuracy dta. Hi all, i am using metal for meta analysis of some specific snps 6 snps of interest across three studies. For simplicity, we use the term metaanalysis in the remainder of the article. I am doing a meta analysis for my thesis on 3 treatment options in treating achalasia. Evidencebased mapping of design heterogeneity prior to. Hi all, i am using metal for metaanalysis of some specific snps 6 snps of interest across three studies. 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. Methodological issues and advances in biological metaanalysis. Sensitivity of betweenstudy heterogeneity in metaanalysis. The effects of clinical and statistical heterogeneity on the. Estimates of heterogeneity i can be biased in small.
Metaanalysis has become a popular tool for increasing power in genetic association studies, yet it remains a methodological challenge. Nov 16, 2016 metaanalysis, complexity, and heterogeneity. 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. First, like primary research studies synthesized in a meta analysis, methods used in a meta analysis should be fully transparent and reproducible. Sep 06, 2003 an alternative quantification of heterogeneity in a metaanalysis is the amongstudy variance often called. In common with other meta analysis software, revman presents an estimate of the betweenstudy variance in a randomeffects meta analysis. 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. It is typically a result of clinical heterogeneity, methodological heterogeneity, or both. Impact of multidrug resistant bacteria on economic and. There are methods for assessing and addressing heterogeneity that we discuss in detail in. Stata module to quantify heterogeneity in a meta analysis, statistical software components s449201, boston college department of economics, revised 25 jan 2006.
Methodological considerations in network metaanalysis. Meta analysis is a statistical method that combines quantitative findings from previous studies. This article provides an introduction to the metaanalysis literature and discusses the challenges of applying metaanalysis to human dimensions research. Functions for metaanalysis and methodology soundness. 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. Openmee was developed to make advanced methods for statistical research synthesis, based on best practices, available without cost to the scientific. In statistics, study heterogeneity is a problem that can arise when attempting to undertake a metaanalysis. The randomeffects meta analysis attempts to account for this distribution of effects and provides a more conservative estimate of the effect. Heterogeneity is not something to be afraid of, it just means that there is variability in your data. Quantifying systematic heterogeneity in metaanalysis view on github.
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. This module should be installed from within stata by typing ssc install heterogi. 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. This heterogeneity may be of clinical, methodological or statistical origin.
Meta analysis is a statistical technique, or set of statistical techniques, for summarising the results of several studies into a single estimate. Oct 28, 2010 the randomeffects meta analysis attempts to account for this distribution of effects and provides a more conservative estimate of the effect. 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. 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. These interventions include acupressure, massage, tai chi, qi gong, electroacupuncture and use of chinese herbal medicinesfor example, in enemas, foot. The randomeffects metaanalysis attempts to account for this distribution of effects and provides a more conservative estimate of the effect. For simplicity, we use the term meta analysis in the remainder of the article. Systematic heterogeneity can arise in a meta analysis due to differences in the study characteristics of participating studies. In contrast, a fixedeffect analysis assumes that a single common effect underlies every study included in the meta analysis. Assessment of the betweenstudy heterogeneity is an essential component of meta analysis.
The last of these is quantified by the i 2statistic. The core mission of this kind of test is to identify data sets from. It should be noted that the decision to focus on patient diagnoses and comparator duration is an. 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. Stata module to quantify heterogeneity in a metaanalysis, statistical software components s449201, boston college department of economics, revised 25 jan 2006. 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. Dealing with heterogeneity in metaanalyses is often tricky, and there is only limited advice for authors on what to do. A critical appraisal of the methodology and quality of. An alternative quantification of heterogeneity in a meta analysis is the amongstudy variance often called. The dilemma of heterogeneity tests in metaanalysis. The software described in this manual is furnished under a license agreement or nondisclosure agreement. 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. 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.
M an aggregate statistic, to identify systematic heterogeneity patterns and their direction of effect in metaanalysis. 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. Study heterogeneity an overview sciencedirect topics. However, heterogeneity is still the threat to the validity and quality of such studies. So, if one brings together different studies for analysing them or doing a metaanalysis, it is clear that there will be differences found. Network metaanalysis nma is an extension of pairwise metaanalysis that facilitates comparisons of multiple interventions over a single analysis. M quantitatively describes systematic nonrandom heterogeneity patterns acting across multiple variants in a gwas metaanalysis. 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. In common with other metaanalysis software, revman presents an estimate of the betweenstudy variance in a randomeffects metaanalysis.
Introduction after several decades development, metaanalysis has become the pillar of evidencebased medicine. Most metaanalysis programs perform inversevariance metaanalyses. 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. So, if one brings together different studies for analysing them or doing a meta analysis, it is clear that there will be differences found. Fourth, when a researcher includes lowquality studies in a metaanalysis, the limitations of these studies impact the mean effect size i.
There is a need for sound methodological guidance on how to. These random effects models and software packages mentioned above. Recommended softwarepackages for metaanalysis of diagnostic. Background infections with multidrug resistant mdr bacteria in hospital settings have substantial implications in terms of clinical and economic outcomes. 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 metaanalysis of diagnostic test accuracy dta. Meta analysis is a quantitative technique that uses specific measures e. While the metaanalytic methodology is similar for systematic and rapid.
Flawed metaanalytic methodology is common in many fields such as oncology. Heterogeneity in metaanalysis q, isquare statsdirect. Because of loss of power, nonsignificant heterogeneity within a subgroup may. First, like primary research studies synthesized in a metaanalysis, methods used in a metaanalysis should be fully transparent and reproducible. Metaanalysis is a quantitative technique that uses specific measures e. In statistics, study heterogeneity is a problem that can arise when attempting to undertake a meta analysis. Quantifying systematic heterogeneity in metaanalysis. With a randomeffects metaanalysis, the 95% ci of the effect estimate contains the true relative risk 0. 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.
Sep 14, 2016 meta analysis has become a popular tool for increasing power in genetic association studies, yet it remains a methodological challenge. Figure 3 shows the six casecontrol studies of magnetic fields and leukaemia broken down into two subgroups based on assessment of their quality. Methodological and clinical heterogeneity and extraction. It has been increasingly used to obtain more credible results in a wide range of scientific fields. Assessment of the betweenstudy heterogeneity is an essential component of metaanalysis. There are 3 types of heterogeneity commonly considered in metaanalysis. This qualitative interview study aimed to understand researchers understanding of complexity and heterogeneity and the factors which may influence the choices researchers.
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. Conversely, q has too much power as a test of heterogeneity if the number of studies is large higgins et al. There are 3 types of heterogeneity commonly considered in meta analysis. 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. Different weights are assigned to the different studies for calculating the summary or pooled effect. In contrast, a fixedeffect analysis assumes that a single common effect underlies every study included in the metaanalysis. Is there any statical software for calculation of heterogenity in a. Another important consideration for metaanalysis is that of the underlying model. 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. This strategy effectively documents design heterogeneity, thus improving the practice of metaanalysis by aiding in. Heterogeneity, metaanalysis and metaregression modules facilitate.
Significant statistical heterogeneity arising from methodological diversity or differences. We searched databases medline, embase, cinahl, cochrane library, and consort, to. The opposite of heterogeneity is homogeneity meaning that all studies show the same effect. Q is included in each statsdirect meta analysis function because it forms part of the dersimonianlaird random effects pooling method dersimonian and laird 1985.
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. From the within study results, i can see that results from two of the studies are in the same direction while the. Impact of heterogeneity and effect size on the estimation of. Heterogeneity and subgroup analyses in cochrane consumers and. 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. Q is included in each statsdirect metaanalysis function because it forms part of the dersimonianlaird random effects pooling method dersimonian and laird 1985. Methodological standards for metaanalyses and qualitative. 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 greek root meta means with, along, after, or later, so here we have an. A metaanalysis is a statistical analysis that combines the results of multiple scientific studies.
While there is some consensus on methods for investigating statistical and methodological heterogeneity, little attention has been paid to clinical aspects of heterogeneity. Also seemeta meta esize for how to compute various effect sizes in a metaanalysis. In recent years, a number of new methods have been developed to meet these challenges. Impact of heterogeneity and effect size on the estimation. Methodological heterogeneity refers to differences in the way that studies were. Exploring sources of heterogeneity 2 metaregression form of subgroup analysis that allows consideration of continuous variables, e. We investigated, using simulated studies, the accuracy of i 2 in the assessment of heterogeneity and the effects of heterogeneity on the predictive value of metaanalyses. The core mission of this kind of test is to identify data sets from similar. Variance between studies in a metaanalysis will exist. The effects of clinical and statistical heterogeneity on. Currently, q and its descendant i2 i square tests are widely used as the tools for heterogeneity evaluation. 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.
1397 1324 1694 1019 474 1658 1218 909 445 1334 850 514 560 663 682 949 1108 1243 1234 1384 268 1114 1435 753 1315 1352 765 1282 1425 1583 133 137 294 323 585 379 1608 1222 1050 1311 937 1374 796 131 731 774 1342 865