Need help with a homework or test question? Three systematic reviews published in the BMJ, including one in this issue, have referred to heterogeneity and dealt with it in three different ways.1 Differences between outcomes would only be due to measurement error (and studies would hence be homogeneous). 2

Judgments about clinical heterogeneity …. Overall, it appears that heterogeneity is being consistently underestimated in meta-analyses. A high P value is good news because it suggests that the heterogeneity is insignificant and that one can go ahead and summarise the results. Your first 30 minutes with a Chegg tutor is free! Meta-analysis increases the power of statistical analyses by pooling the results of all available trials.

Please note: your email address is provided to the journal, which may use this information for marketing purposes. Differences between outcomes would only be due to measurement error (and studies would hence be homogeneous). Access this article for 1 day for:£30 / $37 / €33 (excludes VAT). T-Distribution Table (One Tail and Two-Tails), Variance and Standard Deviation Calculator, Permutation Calculator / Combination Calculator, The Practically Cheating Statistics Handbook, https://www.statisticshowto.com/heterogeneity/, Blobbogram / Forest Plot: Definition, Simple Example. statistical heterogeneity is not a problem and that the message is a consistent one (fig 2). As one tries to use the meta-analysis to estimate a combined effect from a group of similar studies, there needs to be a check that the effects found in the individual studies are similar enough that one can be confident that a combined estimate will be a meaningful description of the set of studies. ", "Measuring the statistical validity of summary meta-analysis and meta-regression results for use in clinical practice", Cochrane Handbook for Systematic Reviewers, Appraising the Quality of Systematic Reviews, https://en.wikipedia.org/w/index.php?title=Study_heterogeneity&oldid=982380188, Creative Commons Attribution-ShareAlike License, This page was last edited on 7 October 2020, at 19:42. In statistics, (between-) study heterogeneity is a phenomenon that commonly occurs when attempting to undertake a meta-analysis. Tumor heterogeneity was known 35 years ago. With Chegg Study, you can get step-by-step solutions to your questions from an expert in the field. In real life, heterogeneous populations are extremely common. Comments? If you are unable to import citations, please contact if they all agree or disagree) include: A forest plot showing odds ratios, confidence intervals, and a summary measure. A heterogeneous population or sample is one where every member has a different value for the characteristic you’re interested in. Heterogeneity in statistics means that your populations, samples or results are different. Its analysis is crucial for defining whether selected primary studies pooling is fit for meta-analysis. Descriptive Statistics: Charts, Graphs and Plots. https://doi.org/10.1136/bmj.39057.406644.68, Government of Jersey General Hospital: Consultant in Child & Adolescent Psychiatry (CAMHS), Isle of Wight NHS Trust: Clincial Director for Planned Care, University Hospitals Bristol and Weston NHS Foundation Trust: Consultant and Locum Consultant in Emergency Medicine, Claremont Bank Surgery: Partnership vacancy, Milton Keynes University Hospital NHS Foundation Trust: Consultant Cellular Pathologist, Women’s, children’s & adolescents’ health. This is an example of clinical heterogeneity. Copyright © 2020 BMJ Publishing Group Ltd     京ICP备15042040号-3. The size of individual clinical trials is often too small to detect treatment effects reliably. Number of studies, heterogeneity, generalisability, and the choice of method for meta-analysis. Please post a comment on our Facebook page. Ways to figure out if the results are homogeneous or not (i.e. Some studies might show favorable results, while others show unfavorable results. [4] When heterogeneity is substantial, a prediction interval rather than a confidence interval can help have a better sense of the uncertainty around the effect estimate. If you have a subscription to The BMJ, log in: Subscribe and get access to all BMJ articles, and much more. It is the opposite of homogeneity , which means that the population/data/results are the same. It is important to consider to what extent the results of studies are consistent. To determine whether significant heterogeneity exists, look for the P value for the χ2 test of heterogeneity. Need to post a correction? When this excessive variation occurs, it is called statistical heterogeneity, or just heterogeneity.

NEED HELP NOW with a homework problem? Heterogeneity in statistics means that your populations, samples or results are different.

Although the interventions try to achieve the same end result (to improve patients' ability to make choices), they are different in nature. However, simulations have shown that methods are relatively robust even under extreme distributional assumptions, both in estimating heterogeneity,[2] and calculating an overall effect size. [1], When there is heterogeneity that cannot readily be explained, one analytical approach is to incorporate it into a random effects model. [6] As a statistic used to test the null hypothesis of statistical validity it has its limitations but this can be partly overcome by using it in conjunction with the Q statistic.[7]. You can download a PDF version for your personal record. This is the first in a series of occasional articles explaining statistical and epidemiological tests used in research papers in the BMJ Three systematic reviews published in the BMJ , including one in this issue, have referred to heterogeneity and dealt with it in three different ways.1 2 3 So what is heterogeneity, and how do we assess its importance in a systematic review? Each result was different. NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. In statistics, (between-) study heterogeneity is a phenomenon that commonly occurs when attempting to undertake a meta-analysis. 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. However, the individual estimates of treatment effect will vary by chance; some variation is expected. [3], However, most meta-analyses include between 2-4 studies and such a sample is more often than not inadequate to accurately estimate heterogeneity. 3 So what is heterogeneity, and how do we assess its importance in a systematic review? CLICK HERE! For example, patients are typically a very heterogeneous population as they differ with many factors including demographics, diagnostic test results, and medical histories. Heterogeneity can manifest in two ways, with corresponding procedures: • Clinical heterogeneity: It requires assessment based on clinical grounds For example, some studies may say that sugar is linked to obesity, while others report that sugar isn’t linked to obesity (outside of it being a source of calories). Image: James Grellier | Wikimedia Commons. The centre of this symmetric distribution describes the average of the effects, while its width describes the degree of heterogeneity. What is heterogeneity and is it important? In a simple scenario, studies whose results are to be combined in the meta-analysis would all be undertaken in the same way and to the same experimental protocols. It is the opposite of homogeneity, which means that the population/data/results are the same. Statistical heterogeneity only comes to light after results from studies are analyzed. Primary studies heterogeneity caused by between-study differences is an expected circumstance. The model represents the lack of knowledge about why real, or apparent, treatment effects differ by treating the differences as if they were random. [5], The presence of heterogeneity may affect the statistical validity of the summary estimate of effect. In clinical trials and meta-analysis, heterogeneity of results means that studies have widely varying outcomes. A heterogeneous population or sample is one where every member has a different value for the characteristic you’re interested in. Meta-analysis is a method used to combine the results of different trials in order to obtain a quantitative synthesis. Thus it appears that in small meta-analyses, an incorrect zero between study variance estimate is obtained, leading to a false homogeneity assumption. Study heterogeneity denotes the variabilityin outcomes that goes beyond what would be expected (or co…

In a simple scenario, studies whose results are to be combined in the meta-analysis would all be undertaken in the same way and to the same experimental protocols.

The interventions are so different that combining them does not make clinical sense. The conventional choice of distribution is a normal distribution. Other circumstances that may give rise to clinical heterogeneity include differences in selection of patients, severity of disease, and management. Sometimes trials are just looking at different concepts.

[2], One measure of heterogeneity is I2, a statistic that indicates the percentage of variance in a meta-analysis that is attributable to study heterogeneity. Study heterogeneity denotes the variability in outcomes that goes beyond what would be expected (or could be explained) due to measurement error alone. A random effects meta-analysis model involves an assumption that the effects being estimated in the different studies are not identical, but follow some distribution. and tau 2 = estimate of between-study heterogeneity Although this can be difficult to establish, the Vn statistic represents a direct measure of statistical validity. I could homogenous a sample of the tumor, separate it into 3 parts and test against a panel of drugs. Would the averaged number apply to all these diverse interventions? This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. In theory, we could add all the trials in this review together and come up with a number, but would this be useful? It is difficult to establish the validity of any distributional assumption, and this is a common criticism of random effects meta-analyses. ). Accounting for heterogeneity: Random effects inverse-variance weighted model • In addition to weighting studies by the inverse of their variance, an estimate of between-study heterogeneity is also accounted for • Using notation from above = ∑ ∗ ∑ ∗ where ˘∗ˇ= ˆ˙˝ ˜ ˙! "