Last year i wrote a post of 5 key things to know about metaanalysis. I think that the i 2 value is a measure of statistical heterogeneity which is not entirely synonymous with clinical or methodological heterogeneity. 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. In this article we present methods for the meta analysis of prevalence. M an aggregate statistic, to identify systematic heterogeneity patterns and their direction of effect in metaanalysis. The second analysis in is figure 2 identical to the analysis we saw a moment ago. To date, the impact of heterogeneity is assessed locally on an individual snp basis using q, i2 and. In addition to pooling effect sizes, meta analysis can also be used to estimate disease frequencies, such as incidence and prevalence. An alternative quantification of heterogeneity in a metaanalysis is the amongstudy variance often called. In the literature, there are various measures of heterogeneity, such as. Metaanalysis is the statistical combination of results from two or more separate studies. In this lecture we look at how to deal with it when we have it. Exploring sources of heterogeneity 2 metaregression form of subgroup analysis that allows consideration of continuous variables, e.
Both two and onestage approaches are evaluated via simulation study. Quantifying heterogeneity in individual participant data meta. Application of stata software to test heterogeneity in. Fixed effect fe model for metaanalysis the diamond at the bottom of the forest plot picturing a metaanalysis reports the overall point estimate and its 95%ci. Which is the preferred measure of heterogeneity in metaanalysis.
This is not a substitute for a thorough investigation of heterogeneity. Ideally, the studies whose results are being combined in the metaanalysis should all be. Hence i2 is percentage of the chisquared statistic which. Evolution of heterogeneity i2 estimates and their 95% confidence. Accounting for heterogeneity drives different statistical methods for summarizing data and, if heterogeneity is anticipated, a randomeffects model will be preferred to the fixedeffects model. A data set was set up according to the example in the study, and the corresponding commands of the methods in stata 9 software were applied to test the example. Interpretation of random effects metaanalyses the bmj. Although metaanalysis is widely used in epidemiology and evidencebased medicine today, a metaanalysis of a medical treatment was not published until 1955. Statsdirect calls statistics for measuring heterogentiy in meta analysis noncombinability statistics in order to help the user to interpret the results.
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. Metaanalysis is a popular and frequently used statistical technique. Variation across studies heterogeneity must be considered, although most. It can though be hampered by excessive heterogeneity among its participating studies. Heterogeneity in metaanalysis should be expected and appropriately quantified. Backgroundmeta analysis is the systematic and quantitative synthesis of effect sizes and the exploration of their diversity across different studies. Which is the preferred measure of heterogeneity juniper publishers. Methodological standards for metaanalyses and qualitative.
The increasing prevalence of childhood obesity and overweight is considered a public health issue in both developed and developing countries. Severe apparent heterogeneity can indicate that data have been incorrectly extracted or entered into metaanalysis software. With metaanalyses booming, including many that are. Draft please do not quote michael borenstein julian p. While there is some consensus on methods for investigating statistical and methodological heterogeneity, little attention has been paid to clinical aspects of heterogeneity. If confidence intervals for the results of individual studies generally depicted. So, if one brings together different studies for analysing them or doing a meta analysis, it is clear that there will be differences found.
The opposite of heterogeneity is homogeneity meaning that all studies show the same effect. Is there any statical software for calculation of heterogenity in a. So, if one brings together different studies for analysing them or doing a metaanalysis, it is clear that there will. In metaanalysis, heterogeneity occurs when variation between the study effect sizes cannot be explained by sampling variability alone. In meta analyses ma, effect estimates that are pooled together will often be heterogeneous. Mar 21, 2018 this is a post about the diamond ratio dr, a simple measure of the extent of heterogeneity in a metaanalysis. Systematic heterogeneity can arise in a meta analysis due to differences in the study characteristics of participating studies. Unlike other methods of measuring extent of heterogeneity, i2 has finite. Before you start collecting the actual data for the metaanalysis, decide which statistical software package will be used to analyze the data. Heterogeneity in metaanalyses of genomewide association. For this workshop, we will be using the meta analysis commands that were introduced in stata 16. The effects of clinical and statistical heterogeneity on. In metaanalysis, the fraction of variance that is due to heterogeneity is estimated by the statistic i2. Statsdirect calls statistics for measuring heterogentiy in metaanalysis non.
Bringing evidence to translational medicine heterogeneity. If confidence intervals for the results of individual studies generally depicted graphically using horizontal lines have poor overlap, this generally indicates the presence of statistical heterogeneity. Mar 12, 20 as clinical heterogeneity is a major cause of statistical heterogeneity, it is no surprise that in the meta. Stata module to quantify heterogeneity in a metaanalysis, statistical software components s449201, boston college department of economics, revised 25 jan 2006. Methodological issues and advances in biological metaanalysis. It was a great way to focus but it was hard keeping to only 5. If all studies in an analysis shared the same true effect. Another 5 things to know about metaanalysis absolutely maybe. Which is the preferred measure of heterogeneity in meta analysis and why. Measuring the spatial heterogeneity on the reduction of. We have developed a new quantity, i2, which we believe gives a better measure of the consistency between trials in a meta analysis. A subgroup analysis to identify sources of heterogeneity showed that the incidence varied from 5. Heterogeneity in metaanalysis q, isquare statsdirect.
The most commonly used heterogeneity measure, i2, provides an. Heterogeneity in metaanalysis refers to the variation in study outcomes between studies. A randomeffects meta analysis may be used to incorporate heterogeneity among studies. Quantifying systematic heterogeneity in metaanalysis view on github. In statistics, homogeneity and its opposite, heterogeneity, arise in describing the properties of a dataset, or several datasets. Computation modules regarding these two measures are available in the standard statistical software for metaanalysis like. To introduce the application of stata software to heterogeneity test in meta analysis. They have reported that q statistic is best among them. The last of these is quantified by the i 2statistic. Identifying systematic heterogeneity patterns in genetic. With a randomeffects metaanalysis, the 95% ci of the effect estimate contains the true relative risk 0. 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. This strategy effectively documents design heterogeneity, thus improving the practice of. Look at the help file for the command that you will be using.
Measuring inconsistency in metaanalysis request pdf. Hi all, i am using metal for metaanalysis of some specific snps 6 snps of interest across. Heterogeneity in meta analysis heterogeneity in meta analysis refers to the variation in study outcomes between studies. For a pharmacological vs placebocontrol metaanalysis measuring allcause mortality, the predictive distribution for gives little support to values above 0. Statsdirect calls statistics for measuring heterogentiy in metaanalysis. X2 the value which we expect chisquared to have if there is no heterogeneity is equal to its degrees of freedom. Heterogeneity in meta analysis describes differences in treatment effects between trials that exceed those we may expect through chance alone. Meta analysis is used to synthesise quantitative information from related studies and produce results that summarise a whole body of research. Different weights are assigned to the different studies for calculating the summary or pooled effect. Metaanalysis provides a way of quantitatively synthesising the results of medical.
In addition to pooling effect sizes, metaanalysis can also be used to estimate disease frequencies, such. Determining how substantial heterogeneity is is an important aspect of ma. Stata module to quantify heterogeneity in a metaanalysis, statistical software components s449201, boston college. As clinical heterogeneity is a major cause of statistical heterogeneity, it is no surprise that in the meta. The agreement or disagreement between the studies is examined using different measures of heterogeneity. Broadly speaking, any type of variability among the studies included in a metaanalysis can be called heterogeneity.
Metaanalysis is a method to obtain a weighted average of results from various studies. For each analysis, the lefthand plot shows the observed effects while the righthand plot shows the trueeffects. Most metaanalytical software routines including those in revman. Heterogeneity assessment is unavoidable under metaanalysis as it. Variance between studies in a metaanalysis will exist.
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. Heterogeneity is not something to be afraid of, it just means that there is variability in your data. The center columns show what happens if we apply these formulas. Contents chapter 1 introduction 9 chapter 2 baseline risk as predictor of treatment benefit 17 chapter 3 advanced methods in metaanalysis. We calculate the bias of i2, focusing on the situation where the number of studies in the. To assess the heterogeneity in metaanalysis is a crucial issue because the. In statistics, study heterogeneity is a problem that can arise when attempting to undertake a meta analysis. Most metaanalysis programs perform inversevariance metaanalyses. Heterogeneity can be seen on the forest plot when the confidence intervals do not all overlap but the studies might have similar clinical and demographic characteristics or visa versa.
Quantifying heterogeneity in individual participant data. Moreover, the betweenstudy heterogeneity in the magnitude of this relationship was partially explained by two sample and study factors, namely proportion. 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. Author summary metaanalysis of genomewide association studies gwas is a valuable tool for the discovery of genes that protect or predispose individuals to common complex diseases. An extended discussion of this option appears in section 9. Introduction to metaanalysis borenstein, hedges, higgins. Statistical synthesis of research findings via metaanalysis is widely used to assess the relative effectiveness of competing interventions. Metaanalysis seeks to understand heterogeneity in addition to computing a summary risk estimate. 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. For example, if standard errors have mistakenly been entered as sds for continuous outcomes, this could manifest itself in overly narrow confidence intervals with poor overlap and hence substantial heterogeneity. This heterogeneity may be of clinical, methodological or statistical origin. This is a post about the diamond ratio dr, a simple measure of the extent of heterogeneity in a meta analysis.
Meta analyses are increasingly applied to synthesize data from genomewide association gwa studies and from other teams that try to replicate the genetic variants that emerge from such investigations. Why sources of heterogeneity in metaanalysis should be. In statistics, study heterogeneity is a problem that can arise when attempting to undertake a metaanalysis. Need for consistency assessment of the consistency of effects across studies is an essential part of meta analysis. Measuring the spatial heterogeneity on the reduction of vaginal fistula burden in ethiopia between 2005 and 2016. Where figure 1 displayed one metaanalysis, figure 2 displays a series of metaanalyses. Quantifying, displaying and accounting for heterogeneity in the meta. Another 5 things to know about metaanalysis absolutely. This is a post about the diamond ratio dr, a simple measure of the extent of heterogeneity in a metaanalysis.
The citations, search methods, type of metaanalysis, inclusionexclusion criteria for individual studies, use of quality assessment tools, pooling methods, methods for evaluating heterogeneity, use of subgroup, sensitivity analysis, and metaregression, methods for detection of publication bias, and type of software used were extracted from. We developed an alternative approach that quantifies the effect of heterogeneity, providing a measure of the degree of inconsistency in the studies results. We discuss the logit and double arcsine transformations to stabilise the variance. The methods used were qtest and i2 statistic attach. The effects of clinical and statistical heterogeneity on the.
The results from the meta analysis of eight primary studies of hurricane katrina study 1 confirmed that there was a smalltomedium positive relationship between exposure severity and pts. They relate to the validity of the often convenient assumption that the statistical. A handson practical tutorial on performing metaanalysis. Heterogeneity can be seen on the forest plot when the. Metaanalysis of prevalence journal of epidemiology. Predicting the extent of heterogeneity in metaanalysis. Heterogeneity in metaanalysis heterogeneity in metaanalysis refers to the variation in study outcomes between studies.
Evolution of heterogeneity i2 estimates and their 95%. Jun 30, 2015 you can read about methods for measuring heterogeneity in metaanalyses here and here pdf. We searched databases medline, embase, cinahl, cochrane library, and consort, to. In metaanalyses ma, effect estimates that are pooled together will often be heterogeneous. Effect measures for dichotomous data are described in chapter 6, section 6. If theres not too much studytostudy variation in the. Dealing with heterogeneity in meta analyses is often tricky, and there is only limited advice for authors on what to do. Quantifying systematic heterogeneity in metaanalysis. The heterogeneity statistic i 2 can be biased in small. Meta analysis is a method to obtain a weighted average of results from various studies. Hence, only q statistic was included in this study for comparison. 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. I am doing a meta analysis for my thesis on 3 treatment options in treating. Evidencebased mapping of design heterogeneity prior to.
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