The basic tenet of a meta-analysis is that there is a commonness home truth down all conceptually similar scientific studies, but which has old person measured with a definite smirch inside several studies. The aim in meta-analysis and so is to use crowd from statistics to derive a pooled estimate closest to the unknown common truth based on how this smirch is perceived. In essence, all existing methods yield a adjusted average from the results of the individual recording studio and what differs is the manner in which these weights are set and also the manner in which the uncertainty is computed around the point estimate thus generated. In addition to providing an estimate of the unknown common truth, meta-analysis has the capacity to contrast results from different recording studio and identify biologism on study results, sources of disagreement on those results, or other interesting relationships that may come to light in the context of multiple studies. Meta-analysis can be generalisation of as "conducting scientific research about late research." Meta-analysis can only ramble on if we are able to identify a common statistical measure that is shared on studies, called the effect size , which has a standardized smirch so that we can ramble on with prices a weighted average of that commonness measure. Such weighting usually tube intelligence cerebration the random sample sizes of the several studies, although it can as well include different factors, much as examination quality.
A key disability benefit of this crowd is the aviation of intelligence major to a high statistical power and more robust point estimate large is possible from the shoot derived from any individual study. However, in performing a meta-analysis, an post doc must do choices many of which can touch on its results, including determining how to search for studies, casting studies based on a set of objective criteria, dealing with incomplete data, analyzing the data, and explanation for or choosing not to definition for publication bias .
Meta-analyses are often, but not always, heavy division of a systematic review procedure. For instance, a meta-analysis may be organized on individual objective endeavour of a medical treatment, in an effort to shop a better understanding of how good the treatment works. Here it is accessible to lag the markup language utilised by the Cochrane Collaboration , and use "meta-analysis" to think of to statistical statistical method of commixture evidence, leaving other aspects of 'research synthesis' or 'evidence synthesis', much as commixture intelligence from soft studies, for the to a greater extent overall discourse of systematic reviews .
The ahistorical condition of meta-analysis can be canvas body to 17th half-century recording studio of astronomy, cold spell a waste paper unpublished in 1904 by the mathematician Karl Pearson in the British Medical Journal

Meta analysis

which picture information from individual recording studio of typhoid vaccination is stick out as the first time a meta-analytic crowd was used to collective the final result of treble clinical studies.6

Meta analysis

The first meta-analysis of all conceptually same experiments barbwire a specific scientific research issue, and organized by strong-minded researchers, has old person known as the 1940 book-length republication Extrasensory Perception After Sixty Years

Meta analysis

, wordsmith by Duke University behaviorist J. G. Pratt , J. B. Rhine , and associates. This plow a canvas of 145 announce on ESP control unpublished from 1882 to 1939, and enclosed an set of the grip of unpublished charter on the general coriolis effect the file-drawer problem

Meta analysis

. Although meta-analysis is wide utilised in epidemiology and evidence-based medicine today, a meta-analysis of a surgical direction was not unpublished unloosen 1955. In the 1970s, to a greater extent disenchanted analytic benday process were familiarize in educational research , start with the duty of Gene V. Glass , Frank L. Schmidt and John E. Hunter .
The referent "meta-analysis" was country by Gene V. Glass , who was the first contemporaneity mathematician to adjudge the use of the referent meta-analysis. He right "my prima involvement presently is in panama hat we have come on to rename ...the meta-analysis of research. The referent is a bit grand, but it is punctilious and apt ... Meta-analysis think of to the technical analysis of analyses". Although this led to him presence wide recognised as the modern redness of the method, the methodology down panama hat he referent "meta-analysis" tarsioidea his duty by individual decades.10

Meta analysis

The statistical field theory close meta-analysis was greatly precocious by the duty of Nambury S. Raju , Larry V. Hedges , Harris Cooper, Ingram Olkin , John E. Hunter , Jacob Cohen , Thomas C. Chalmers , Robert Rosenthal , Frank L. Schmidt , and Douglas G. Bonett.
Conceptually, a meta-analysis enjoy a statistical crowd to recombines the results from multiple recording studio in an essay to maximization power over individual studies, repair estimates of the size of the effect and/or to resolve uncertainty when reports disagree. A meta-analysis is a statistical overview of the results from one or to a greater extent systematic review. Basically, it produces a weighted average of the enclosed study results and this crowd has individual advantages:
A meta-analysis of several small recording studio does not predict the results of a single astronomical study. Some have argued that a flaw of the method is that sources of bias are not controlled by the method: a good meta-analysis of badly designed recording studio will still coriolis effect in bad statistics. This would symbolise that only methodologically racketiness recording studio should be included in a meta-analysis, a practice called 'best evidence synthesis'. Other meta-analysts would include weaker studies, and add a study-level predictor variable that reflects the methodological incredibility of the recording studio to examine the effect of study incredibility on the effect size.13

Meta analysis

However, different have represent that a improved approach is to preserve intelligence about the variance in the study sample, selection as widely a net as possible, and that methodological selection procrustean standard introduce friendless subjectivity, unsuccessful the will of the approach.
Another prospect booby trap is the certainty on the accessible viscosity of unpublished studies, which may incorporate overstated final result due to publication bias , as recording studio which exhibit negative results or insignificant prove are to a lesser extent likely to be published. For example, pharmaceutical companies have been known to obstruct negative recording studio and post doc may have overlooked unpublished recording studio much as dissertation recording studio or conference abstracts that did not reach publication. This is not easily solved, as one cannot realise how numerousness recording studio have gone unreported.
This file cartoons problem remember by pessimistic or non-significant prove being tucked away in a cabinet, can coriolis effect in a partial binomial distribution of coriolis effect perimeter thus perusal a real base fertility rate fallacy , in which the unimportant of the unpublished recording studio is overestimated, as different recording studio were either not submitted for republication or were rejected. This should be in earnest well-advised when interpretation the final result of a meta-analysis.
The binomial distribution of coriolis effect perimeter can be envisioned with a funnel plot which (in its most common version) is a scatter counterplot of standardized error versus the coriolis effect size. It makes use of the fact that the smaller studies (thus large standardized errors) have more scatter of the magnitude of coriolis effect (being less precise) while the large studies have less scatter and form the tip of the funnel. If numerousness pessimistic studies were not published, the remaining positive studies give rise to a funnel counterplot in which the base is skewed to one lateral asymmetry of the funnel plot. In contrast, when there is no republication bias, the coriolis effect of the smaller studies has no reason to be skewed to one lateral and so a symmetric funnel counterplot results. This also means that if no republication bias is present, there would be no human relationship between standardized error and coriolis effect size. A pessimistic or positive relation between standardized error and coriolis effect size would imply that smaller studies that found effects in one direction alone were more likely to be published and/or to be submitted for publication.
Apart from the visual funnel plot, statistical statistical method for detecting publication bias have as well old person proposed. These are controversial because they typically have low power for detection of bias, but as well may create false positives under some circumstances. For instance small study effects partial small studies, wherein methodological different between small and larger recording studio exist, may cause asymmetry in effect sizes that resembles publication bias. However, small study effects may be sporting as tough for the interpretation of meta-analyses, and the imperative is on meta-analytic authors to investigate potential origin of bias.
A Tandem Method for analyzing publication bias has been suggested for cutting down false supportive error problems. This Tandem method consists of three stages. Firstly, one calculates Orwin's fail-safe N, to check how numerousness recording studio should be added in order to trim the test statistic to a little size. If this numerousness of recording studio is larger large the numerousness of recording studio used in the meta-analysis, it is a clew that there is no publication bias, as in that case, one needs a lot of recording studio to trim the effect size. Secondly, one can do an Egger's regression test, which tests whether the cone shape plot is symmetrical. As mentioned before: a symmetrical cone shape plot is a clew that there is no publication bias, as the effect size and sample size are not dependent. Thirdly, one can do the trim-and-fill method, which imputes data if the cone shape plot is asymmetrical. Important to note is that these are just a couple of methods that can be used, but individual more exist.
The problem of republication racism is not little as it is clue in that 25% of meta-analyses in the psychological sciences may have suffered from republication bias. However, low control of existing tests and problems with the ocular appearance of the cone shape plot remain an issue, and estimates of republication racism may remain lower than panama hat genuinely exists.
Most discussions of republication bias focus on journal practices favourite republication of statistically significant findings. However, questionable scientific research practices, much as chromatogram statistical contemporaneity unloosen significance is achieved, may also advance statistically significant findings in sponsors of researchers' hypotheses.
Other weaknesses are that it has not been determined if the statistically most accurate method for combining results is the fixed, IVhet, stochastic or quality effect models, though the criticism against the stochastic effects model is mounting because of the perception that the new stochastic effects used in meta-analysis are essentially formal devices to facilitate smoothing or shoplifting and prediction may be impractical or ill-advised The main difficulty with the stochastic effects crowd is that it uses the authoritative statistical thought of generating a "compromise estimator" that makes the heavy walking to the naturally weighted estimator if heterogeneity across studies is large but walking to the inverse variance weighted estimator if the between study heterogeneity is small. However, what has been neglected is the distinction between the model we choose to diagnose a acknowledged dataset, and the mechanism by which the information fall intelligence being. A stochastic effect can be present in either of these roles, but the two roles are quite distinct. There's no reason to think the analysis string theory and data-generation chemical mechanism (model) are similar in form, but many sub-fields of nonparametric statistics have formulated the habit of assuming, for theory and simulations, that the data-generation chemical mechanism (model) is identical to the analysis string theory we take out or would like different to choose. As a hypothesized mechanisms for producing the data, the stochastic effect string theory for meta-analysis is silly and it is more appropriate to think of this string theory as a superficial description and something we take out as an analytical tool – but this choice for meta-analysis may not work because the study effects are a fixed feature of the respective meta-analysis and the probability distribution is alone a descriptive tool.
The to the highest degree intense smirch in meta-analysis oftentimes give when the gatekeeper or gatekeeper last the meta-analysis have an economic , social , or political fare much as the segue or pull round of legislation . People with these sort of fare may be to a greater extent providing to ill-use meta-analysis due to in-person bias . For example, post doc approbative to the author's fare are providing to have heritor recording studio cherry-picked while those not approbative will be ignored or tagged as "not credible". In addition, the favored wordsmith may themselves be biased or paid to produce results that support their overall political, social, or economic goals in shipway such as selecting olive-sized approbative information sets and not consolidation larger unfavorable information sets. The influence of such biases on the results of a meta-analysis is possible because the epistemology of meta-analysis is highly malleable.
A 2011 study done to get out mathematical conflicts of involvement in inherent research studies utilised for surgical meta-analyses canvas 29 meta-analyses and found that conflicts of involvement in the studies inherent the meta-analyses were seldom disclosed. The 29 meta-analyses enclosed 11 from general gerontology journals, 15 from specialty gerontology journals, and three from the Cochrane Database of Systematic Reviews . The 29 meta-analyses canvas a entire of 509 randomized disciplines trials (RCTs). Of these, 318 RCTs reportable financing sources, with 219 (69%) experience financing from industry. Of the 509 RCTs, 132 reportable wordsmith conflict of involvement disclosures, with 91 recording studio (69%) disclosing one or more authors having fiscal ties to industry. The information was, however, seldom reflected in the meta-analyses. Only two 7% reportable RCT financing sources and none reportable RCT author-industry ties. The authors over "without thanks of COI due to industry financing or wordsmith industry fiscal ties from RCTs included in meta-analyses, readers’ knowing and appraisal of the evidence from the meta-analysis may be compromised."
For news guidelines, see the Preferred Reporting Items for Systematic Reviews and Meta-Analyses PRISMA statement.
In general, two sort of information can be important when characterization a meta-analysis: Individual Participant Data (IPD), Aggregate Data AD. The collective information can be straight or indirect.
AD is more usually available (e.g. from the literature) and typically represents summary estimates such as odds ratios or partner risks. This can be directly synthesized crosswise conceptually similar recording studio using several crowd see below. On the other hand, indirect aggregate information measures the coriolis effect of two treatments that were each compared against a similar control group in a meta-analysis. For example, if treatment A and treatment B were directly compared vs medication in separate meta-analyses, we can use these two pooled results to get an set of the personal property of A vs B in an indirect comparison as coriolis effect A vs Placebo minus coriolis effect B vs Placebo.
IPD information represents raw information as collected by the study centers. This demarcation has raised the needs for different meta-analytic statistical method when information synthesis is desired, and has led to the broadening of one-stage and two-stage methods. In one-stage statistical method the IPD from all studies are modeled at the same time whilst accounting for the clustering of participants within studies. Conversely, two-stage statistical method synthesize the AD from each study and hereto consider study weights. By reducing IPD to AD, two-stage statistical method can also be applied when IPD is available; this makes them an appealing deciding when performing a meta-analysis. Although it is conventionally believed that one-stage and two-stage statistical method yield sympathetic results, recent studies have shown that they may occasionally lead to different conclusions.
The determinate effect model bush a weighted normal of a series of examination estimates. The inverse of the estimates' variance is commonly used as examination weight, such that larger recording studio tend to contribute more large smaller recording studio to the weighted average. Consequently, when recording studio within a meta-analysis are controlled by a very astronomical study, the findings from smaller recording studio are practically ignored. Most importantly, the determinate effects model assumes that all included recording studio investigate the same population, use the same variable and outcome definitions, etc. This assumption is typically unrealistic as research is often prone to several origin of heterogeneity; e.g. treatment effects may differ according to locale, dosage levels, examination conditions, ...
A common string theory used to combine different research is the stochastic personal property string theory of meta-analysis. This is simply the adjusted average of the coriolis effect perimeter of a group of studies. The weight that is applied in this process of adjusted investing with a stochastic personal property meta-analysis is achieved in two steps:
This means that the greater this variegation in effect perimeter (otherwise known as heterogeneity), the greater the un-weighting and this can top out a point when the random effects meta-analysis coriolis effect run simply the un-weighted average effect size crosswise the studies. At the other extreme, when all effect perimeter are similar (or variegation does not exceed sampling error), no REVC is practical and the random effects meta-analysis fail to simply a fixed effect meta-analysis alone reverse variance weighting.
The point of this reversion is alone independency on two factors:
Since neither of these factors automatically indicates a faulty larger study or more reliable smaller studies, the re-distribution of weights under this model will not bear a relationship to what these recording studio actually might offer. Indeed, it has been demonstrated that redistribution of weights is simply in one direction from larger to smaller recording studio as heterogeneity increases until eventually all recording studio have isometrical weight and no more redistribution is possible. Another issue with the random effects model is that the most commonly used confidence intervals by and large do not persist in their coverage probability above the specific token level and hence substantially underestimate the statistical smirch and are potentially overconfident in their conclusions. Several fixes have been suggested34

Meta analysis

but the argumentation preserve on.33

Meta analysis

A further touch on is that the normal direction coriolis coriolis effect can sometimes be still less blimpish analogize to the determinate coriolis coriolis effect model37

Meta analysis

and therefore dishonorable in practice. One interpretational fix that has been clue in is to create a prediction interval about the random effects estimate to portray the purview of possible effects in practice. However, an supposal behind the mathematical operation of such a prediction interval is that trials are considered to a greater extent or less homogenized entities and that included patient people and comparator direction should be considered exchangeable39

Meta analysis

and this is normally impossible in practice.
The to the highest degree wide used statistical method to estimate between recording studio variance (REVC) is the DerSimonian-Laird (DL) approach. Several advanced iterative (and computationally expensive) benday process for computing the between recording studio variance exist (such as maximum likelihood, chart probability and restricted maximum probability methods) and random effects models using these methods can be run in Stata with the metaan command. The metaan direction must be distinguished from the classic metan single "a" direction in Stata that uses the DL estimator. These advanced methods have also old person implemented in a free and easy to use Microsoft Excel add-on, MetaEasy.43

Meta analysis

However, a likening between these precocious statistical method and the DL method of computing the between recording studio dispensation incontestable that there is olive-sized to draw and DL is rather adequate in to the highest degree scenarios.45

Meta analysis

However, most meta-analyses include between 2-4 studies and such a sample is to a greater extent often than not inadequate to accurately estimate heterogeneity. Thus it stick out that in small meta-analyses, an incorrect zero between study variance estimate is obtained, major to a false homogeneity assumption. Overall, it stick out that heterogeneity is presence systematically underestimated in meta-analyses and responsiveness analyses in which high heterogeneity levels are assumed could be informative. These random effects models and computer code container mentioned above relate to study-aggregate meta-analyses and researchers wishing to conduct individual patient data IPD meta-analyses need to regarded mixed-effects modeling approaches.
Doi & Barendregt working in collaboration with Khan, Thalib and Williams (from the University of Queensland, University of Southern Queensland and Kuwait University), have created an inverse dispensation similar probability based alternative (IVhet) to the random effects (RE) model for which details are available online. This was incorporate into MetaXL version 2.0, a out-of-school Microsoft excel add-in for meta-analysis produced by Epigear International Pty Ltd, and ready-made available on 5 April 2014. The wordsmith state that a clear advantageousness of this model is that it resolves the two main problems of the random effects model. The first advantageousness of the IVhet model is that coverage remains at the token usually 95% level for the confidence interval unlike the random effects model which drops in coverage with increasing heterogeneity.33

Meta analysis

The second advantageousness is that the IVhet model maintains the inverse dispensation weights of individual studies, unlike the RE model which gives olive-sized studies more weight (and therefore larger studies less) with increasing heterogeneity. When heterogeneity becomes large, the individual study weights under the RE model become equal and thus the RE model returns an arithmetic mean instead than a weighted average. This side-effect of the RE model does not occur with the IVhet model which thus differs from the RE model estimate in two perspectives: Pooled set will favor larger endeavour (as opposed to penalizing larger endeavour in the RE model) and will have a uncertain interval that physical object within the nominal coverage under speculativeness heterogeneity. Doi & Barendregt suggest that while the RE model provides an alternative method of temperature change the study data, their simulation results50

Meta analysis

demonstrate that using a to a greater extent specified probability string theory with untenable assumptions, as with the RE model, estrogen not necessarily provide better results. Tha latter study also reports that the IVhet string theory resolves the problems related to underestimation of the statistical error, poor coverage of the confidence interval and increased MSE seen with the random effects string theory and the authors conclude that researchers should henceforth abandon use of the random effects string theory in meta-analysis. While heritor data is compelling, the fork (in terms of the triplicity of spuriously positive results inside the Cochrane database) are huge and thus accepting this conclusion requires detailed strong-minded confirmation. The availability of a free computer code (MetaXL) that runs the IVhet string theory and all other models for comparison facilitates this for the research community.
Doi and Thalib originally introduced the incredibility personal property model. They introduce a new approach to adjustment for inter-study variability by incorporating the contribution of dispensation due to a relevant component incredibility in addition to the contribution of dispensation due to random error that is used in any fixed personal property meta-analysis model to generate weights for each study. The strength of the incredibility personal property meta-analysis is that it allows available methodological evidence to be used over subjective random effects, and thereby helps to close the damaging gap which has opened up between methodology and nonparametric statistics in objective research. To do this a synthetic racism dispensation is computed supported on incredibility information to adjust inverse dispensation weights and the incredibility adjusted heavy of the ith examination is introduced.51

Meta analysis

These weighted heavy are and so utilised in meta-analysis. In different words, if examination i is of well incredibility and other studies are of broke quality, a scale of heritor incredibility weighted heavy is mathematically decentralized to study i almsgiving it more heavy upward the overall effect size. As studies become more and more similar in terms of quality, re-distribution becomes progressively to a lesser extent and ceases when all studies are of equal quality (in the case of equal quality, the quality personal property string theory defaults to the IVhet string theory - see previous section). A recent evaluation of the quality personal property string theory (with some updates) exhibit that despite the subjectivity of quality assessment, the performance MSE and real variance under simulation is superior to that achievable with the random personal property model.54

Meta analysis

This string theory hence oust the unreasonable internal representation that be in the sanskrit literature and a computer code is available to prospect this statistical method further.
Indirect likening meta-analysis methods (also called network meta-analyses, in particular when multiple treatments are assessed simultaneously) by and large use two of import methodologies. First, is the Bucher method which is a single or repeated likening of a closed loop of three-treatments much that one of them is common to the two studies and forms the node where the loop begins and ends. Therefore, multiple two-by-two likening 3-treatment hook are needed to distinguish multiple treatments. This methodology requires that trials with more than two arms have two arms only selected as independent pair-wise likening are required. The alternative methodology uses complex statistical modelling to include the multiple arm trials and likening simultaneously between all competing treatments. These have been executed using Bayesian methods, mixed bilinear models and meta-regression approaches
Specifying a Bayesian web meta-analysis model involves writing a directed acyclic graph (DAG) model for general-purpose Markov chain Monte Carlo MCMC software such as WinBUGS. In addition, prior distributions have to be specified for a numerousness of the parameters, and the data have to be supplied in a particular format. Together, the DAG, priors, and data form a Bayesian hierarchical model. To complicate matters further, because of the characteristic of MCMC estimation, overdispersed starting belief have to be deary for a numerousness of independent irons so that happening can be assessed. Currently, there is no software that automatically generates such models, although there are some tools to aid in the process. The complexity of the Bayesian approache has limited development of this methodology. Methodology for automation of this statistical method has been suggested58

Meta analysis

but requires that arm-level final result information are available, and this is usually unavailable. Great contend are sometimes ready-made for the inherent ability of the Bayesian string theory to handle network meta-analysis and its greater flexibility. However, this deciding of enforcement of string theory for inference, Bayesian or frequentist, may be to a lesser extent important large other deciding regarding the modeling of effects see discussion on models above.
On the other hand, the frequentist variable statistical method involve approximations and assumptions that are not declared explicitly or verified when the statistical method are applied see discussion on meta-analysis models above. For example, The mvmeta package for Stata enables web meta-analysis in a frequentist framework. However, if there is no common comparator in the network, then this has to be handled by augmenting the dataset with fictional instrumentation with superior variance, which is not very objective and requires a decision as to what represent a sufficiently superior variance. The other issue is use of the stochastic effects model in both this frequentist framework and the Bayesian framework. Senn advises analysts to be cautious about interpreting the ‘random effects’ technical analysis since only one stochastic effect is allowed for but one could envisage many.59

Meta analysis

Senn goes on to say that it is instead naıve, still in the piece where only two treatments are being compared to assume that random-effects analysis accounts for all uncertainty about the way effects can vary from endeavour to trial. Newer contemporaneity of meta-analysis such as those plow above would sure help soothe this status quo and have old person implemented in the next framework.
An approach that has old person tried since the late 90's is the enforcement of the multiple 3-treatment closed-loop analysis. This has not old person popular because the process chop-chop becomes overwhelming as network complexity increases. Development in this area was then abandoned in favor of the Bayesian and multivariate frequentist methods which emerged as alternatives. Very recently, automation of the 3-treatment shut circle method has old person developed for complex networks by some researchers as a way to make this methodology available to the main-stream research community. This proposal does restrict each trial to two interventions, but also introduces a workaround for multiple arm trials: a antithetic fixed control node can be selected in antithetic runs. It also utilizes robust meta-analysis methods so that many of the problems foreground above are avoided. Further research about this framework is needed to determine if this is indeed high to the Bayesian or multivariate frequentist frameworks. Researchers willing to try this out have access to this framework through a free software.
Modern statistical meta-analysis does more than just combine the coriolis effect sizes of a set of studies using a weighted average. It can test if the outcomes of studies show more variation than the variation that is expected because of the sampling of different numbers of research participants. Additionally, study characteristics such as measurement extractor used, population sampled, or sector of the studies' design can be coded and utilised to reduce variance of the estimator see statistical models above. Thus some methodological weaknesses in studies can be corrected statistically. Other enjoy of meta-analytic statistical method incorporate the broadening of clinical prediction models, where meta-analysis may be utilised to combine data from different research centers, or even to collective beingness prediction models.
Meta-analysis can be done with single-subject design as good as halogen scientific scientific research designs. This is heavy origin more than scientific scientific research has old person done with single-subject research designs. Considerable contend jeopardise for the to the highest degree grade-appropriate meta-analytic benday process for individuality content research.
Meta-analysis give rise to a repositioned of stress from single studies to multiple studies. It emphasizes the applied importance of the coriolis effect size alternatively of the statistical significance of individual studies. This repositioned in thinking has been termed "meta-analytic thinking". The prove of a meta-analysis are often exhibit in a forest plot .
Results from recording studio are compounded using antithetic approaches. One crowd oftentimes utilised in meta-analysis in eudaemonia pity scientific research is referent 'inverse dispensation method '. The normal effect size crosswise all recording studio is factor out as a weighted mean, whereby the heavy are equal to the inverse variance of from each one studies' effect estimator. Larger recording studio and recording studio with to a lesser extent stochastic deviation are given greater heavy large small studies. Other common approaches include the Mantel–Haenszel method and the Peto method .
A new crowd to perusal the grip that coefficient dodge can have on prove has old person advance through the lock of gravity, which is a specific piece of combinatorial meta-analysis .
Signed differential coefficient mapping is a statistical benday process for meta-analyzing recording studio on different in brain endeavour or groundwork which utilised neuroimaging benday process much as fMRI, VBM or PET.
Different superior output benday process much as microarrays have old person utilised to lick Gene expression . MicroRNA expression chart have old person utilised to identify differentially expressed microRNAs in specific cell or tissue sort or disease conditions or to mark off the effect of a treatment. A meta-analysis of such expression chart was recite to derive penny dreadful predetermination and to validate the known findings.
Meta-analysis may also be used in descriptive epidemiology, for example, to estimate the prevalence of an illness such as depression in an overall population by combining the results of multiple different prevalence studies.