A meta-analysis is a survey in which the results of the studies included in the review are statistically similar and are combined and analyzed as if they were one study. From: Botanical Medicine for Women's Health, diagnostic meta-analysis, simply assuring a high quality gold standard, independent assessment of reference and study tests, and blinding may be adequate. Too often, the quality assessment is done, then ignored! Ideally, the results of the quality.

The first step in a meta-analysis is to find all of the pertinent articles on your topic. Important sources of information for a meta-analysis include:. It also has an excellent feature called clinical queries. The Cochrane Collaboration Controlled Trials Register, established inis an important source of studies for a meta-analysis. The Register includes abstracts of thousands of trials. Remember Index Medicus? I'm old enough to recall the intimidating row of thick and I mean THICK books that were my only way to find medical research articles as a medical student in the 's.

They can still be useful when it is important to search for articles published beforewhen MEDLINE and the other electronic databases were established. Finally, there are other sources of "fugitive literature" that may be important for the author of a meta-analysis some of which may be found in the Cochrane Controlled Trials Register :. It's important to know that different search strategies can result in different results Table 4.

Topic Cochrane. If you are thinking about doing a meta-analysis of your own, it is important to enlist the aid of an expert Medline searcher such as a medical librarian. The above table also highlights the importance of using the Cochrane Controlled Trials Register. Once the author of a meta-analysis has assembled a large number of studies, it is important to select the right ones!

There are a variety of possible inclusion also called eligibility criteria:. Once an appropriate group of studies has been identified, the author s have to abstract the relevant data from each study.

There are many sources of potential error in data abstraction:. A good meta-analysis will take some or all of the following steps to minimize errors:. Bias can also creep into a meta-analysis.

For example, the authors may be biased in favor of or against! Also, prominent journals may be given greater weight or authority rightly or wrongly. It is therefore best although not often done to have identifiers eliminated from articles. Finally, part of the data abstraction phase is an assessment of study quality. Chalmers has proposed a fairly complex set of criteria which apply well to randomized controlled trials.

Simpler criteria may be sufficient. For example, in a diagnostic meta-analysis, simply assuring a high quality gold standard, independent assessment of reference and study tests, and blinding may be adequate. Too often, the quality assessment is done, then ignored! Ideally, the results of the quality assessment should inform the analysis and interpretation of results. There are many issues and controversies in the analysis of meta-analytic data. First, let's define some important terms:. Homogeneity and heterogeneity describe the degree of between-study variability in a group of studies.

It is probably appropriate to combine the results from a homogenous set of studies, but many would argue that results from heterogeneous studies should not be combined.

The Q statistic, interpreted using a chi-square distribution, is often used as a test of homogeneity. Fixed effects models consider only within-study variability. The assumption is that studies use identical methods, patients, and measurements; that they should produce identical results; and that differences are only due to how to close session in php variation.

By using a fixed effects model, the researcher answers the question: "Did the treatment produce benefit on average in the studies at hand? Random effects models consider both between-study and within-study variability.

The assumption is that studies are a random sample from the universe of all possible studies. Fixed and random effects models can give very different answers, and you can create examples where either model gives counterintuitive results see Petitti, page Usually, though, the answers provided by these different modeling assumptions are similar. Differences only arise when studies are not homogenous. In a comparison of 22 meta-analyses, fixed and random effects models gave the same answer in 19 out of In 3 cases, fixed effects models were significant while random effects models were not Berlin, in Petitti textbook, pg When there is significant heterogeneitythe between-study variance becomes much larger than the within, and studies of different sample size receive relatively similar weight.

When there is homogeneitysample size dominates, and both models give similar results. Random effects model s are therefore more "conservative" and generate a wider confidence interval.

Put another how to clean dust from laptop fan, a random effects model is less likely to show a significant treatment effect than a fixed effects model. In general, if the studies are homogenous, the researchers should use a fixed effects model. If the studies are heterogeneous, the researchers and you, the reader should first ask why! While it may be appropriate to do a random effects analysis on all of the studies, it may be better to identify an important subgroup difference i.

A term you will encounter in many meta-analyses is "sensitivity analysis". A sensitivity analysis is a way of looking at only certain studies, certain groups of patients, or certain interventions.

For example, what are life goals and objectives meta-analysis of aspirin in prevention of acute MI might first analyze all studies, but then also look separately at only studies of men and studies of women.

The article by Hasselblad is an excellent starting point for budding meta-analysts, with lots of examples and formulae. Meta-analysis of diagnostic tests is another area of growing interest - how do you combine sensitivities, specificities, and so on. However, details of calculations for homogeneity, fixed effects models, and random effects models are beyond the scope of this course. Advanced students and those with a special interest in this topic may wish to review the *what is meta analysis study* sections:.

Steps in a Meta-Analysis. There are four basic steps to any good meta-analysis: Identification Selection Abstraction Analysis We will discuss each of these steps below. Identification The first step in a meta-analysis is to find all of the pertinent articles on your topic. Finally, there are other sources of "fugitive literature" that may be important for the author of a meta-analysis some of which may be found in the Cochrane Controlled Trials Register : unpublished studies - you would have to contact the authors themselves dissertations - there are national indexes of dissertations at university libraries drug company studies - you may have to contact the company directly non-indexed studies - remember to search the bibliographies and Cochrane pre-MEDLINE - use Index Medicus It's important to know that different search strategies can result in different results Table 4.

Cochrane CTR.

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A subset of systematic reviews; a method for systematically combining pertinent qualitative and quantitative study data from several selected studies to develop a single conclusion that has greater statistical power.

This conclusion is statistically stronger than the analysis of any single study, due to increased numbers of subjects, greater diversity among subjects, or accumulated effects and results. If the individual studies utilized randomized controlled trials RCT , combining several selected RCT results would be the highest-level of evidence on the evidence hierarchy, followed by systematic reviews, which analyze all available studies on a topic. The studies pooled for review should be similar in type i.

The analysis should include published and unpublished results to avoid publication bias. Do individuals who wear sunscreen have fewer cases of melanoma than those who do not wear sunscreen? A MEDLINE search was conducted using the terms melanoma, sunscreening agents, and zinc oxide, resulting in 8 randomized controlled studies, each with between and subjects.

All of the studies showed a positive effect between wearing sunscreen and reducing the likelihood of melanoma. The subjects from all eight studies total: subjects were pooled and statistically analyzed to determine the effect of the relationship between wearing sunscreen and melanoma. Goyal, A. Impact of obesity on outcomes following lumbar spine surgery: A systematic review and meta-analysis.

Clinical Neurology and Neurosurgery, , Nakamura, A. Physical activity during pregnancy and postpartum depression: Systematic review and meta-analysis. Journal of Affective Disorders, , A phenomenon in which studies with positive results have a better chance of being published, are published earlier, and are published in journals with higher impact factors.

Therefore, conclusions based exclusively on published studies can be misleading. A Meta-Analysis pools together the sample populations from different studies, such as Randomized Controlled Trials, into one statistical analysis and treats them as one large sample population with one conclusion.

One potential design pitfall of Meta-Analyses that is important to pay attention to is:. Ask us. Now test yourself! One potential design pitfall of Meta-Analyses that is important to pay attention to is: a Whether it is evidence-based. If you experience a barrier that affects your ability to access content on this page, let us know via the Accessibility Feedback Form.

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