A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. Meta-analyses can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error.
How to write a meta analysis?
To minimize errors a good meta-analysis should follow simple steps like:
- Use two or more independent reviewers or have consensus meetings to decide about any conflicts
- Try to educate reviewers by making them practice analysis of the research by reading various articles so that every reviewer standardizes to a common goal
- Always indulge frequently in comparison of abstracts and texts to unearth discrepancies in the studies
What best describes Meta analysis?
A meta-analysis is the process of summarizing research from completed studies. The analysis is completed by taking correlations from multiple studies and determining if the effect holds true or not. To answer the next question, consider this finding from an assessment: extroverts tend to end up in leadership positions.
What does meta analysis mean?
What is a meta-analysis? Meta-analysis is a statistical technique for combining data from multiple studies on a particular topic. Meta-analyses play a fundamental role in evidence-based healthcare.
What does meta analysis mean in research?
Meta-analysis would be used for the following purposes:
- To establish statistical significance with studies that have conflicting results
- To develop a more correct estimate of effect magnitude
- To provide a more complex analysis of harms, safety data, and benefits
- To examine subgroups with individual numbers that are not statistically significant
What is an example of a meta-analysis?
An example of a meta-analysis study would be a team of researchers collecting and statistically combining the results of 20 different randomized clinical trials on the effectiveness of a certain medication for alleviating symptoms of Parkinson's disease.
What is a meta-analysis in simple terms?
What does meta-analysis mean? Meta-analysis is a statistical process that combines the data of multiple studies to find common results and to identify overall trends.
How do you perform a meta-analysis in statistics?
Eight steps in conducting a meta-analysisStep 1: defining the research question. ... Step 2: literature search. ... Step 3: choice of the effect size measure. ... Step 4: choice of the analytical method used. ... Step 5: choice of software. ... Step 6: coding of effect sizes. ... Step 7: analysis. ... Step 8: reporting results.
What is the purpose of a meta-analysis?
Meta-analysis would be used for the following purposes: To establish statistical significance with studies that have conflicting results. To develop a more correct estimate of effect magnitude. To provide a more complex analysis of harms, safety data, and benefits.
What is another word for meta-analysis?
This is reflected in the variety of terms and definitions for synonym circumstances, e.g. "meta-analysis", "systematic review", "narrative review", "meta-syntheses".
What are the types of meta-analysis?
There are four widely used methods of meta-analysis for dichotomous outcomes, three fixed-effect methods (Mantel-Haenszel, Peto and inverse variance) and one random-effects method (DerSimonian and Laird inverse variance). All of these methods are available as analysis options in RevMan.
What are the advantages of a meta-analysis?
Benefits of meta-analysis Through meta-analysis, researchers can combine smaller studies, essentially making them into one big study, which may help show an effect. Additionally, a meta-analysis can help increase the accuracy of the results. This is also because it is, in effect, increasing the size of the study.
What makes a good meta-analysis?
The key to designing a high quality meta-analysis is to identify an area where the effect of the treatment or exposure is uncertain and where a relatively homogenous body of literature exists.
What's the difference between systematic review and meta-analysis?
A systematic review attempts to gather all available empirical research by using clearly defined, systematic methods to obtain answers to a specific question. A meta-analysis is the statistical process of analyzing and combining results from several similar studies.
What is a meta-analysis model?
A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. Meta-analyses can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error.
How many studies is a meta-analysis?
Two studiesTwo studies is a sufficient number to perform a meta-analysis, provided that those two studies can be meaningfully pooled and provided their results are sufficiently 'similar'.
How accurate are meta-analysis?
A study by Paul Glasziou and colleagues in 2010 found that even when there were several trials, the most precise one carried on average half the weight of the results – and around 80% of the time the conclusion of the meta-analysis was pretty much the same as that single study.
When can you do a meta-analysis?
Meta-analysis should be conducted when a group of studies is sufficiently homogeneous in terms of subjects involved, interventions, and outcomes to provide a meaningful summary. However, it is often appropriate to take a broader perspective in a meta-analysis than in a single clinical trial.
What are the problems with meta-analysis?
Several problems arise in meta-analysis: regressions are often non-linear; effects are often multivariate rather than univariate; coverage can be restricted; bad studies may be included; the data summarised may not be homogeneous; grouping different causal factors may lead to meaningless estimates of effects; and the ...
Why does meta-analysis provide a powerful integrative tool?
Why does meta analysis provide a powerful integrative tool? It provides methods for combining and differentiating between the conclusions of a number of data analyses. It provides statistical methods for combining a number of variables.
Does a meta-analysis have a hypothesis?
Meta-analysis is not a hypothesis-testing activity, and cannot legitimately be used to establish the reality of a putative hazard or therapy. The proper use of meta-analysis is to increase the precision of quantitative estimates of health states in populations.
What is the purpose of a meta-analysis?
The main task of the statistical model is to establish the properties of the effect-size population from which the individual effect-size estimates have been selected. To accomplish the first purpose in a meta-analysis, that is, to calculate an average effect size, two statistical models can be assumed: the fixed- and the random-effects models.
Why is meta analysis important?
Meta-analysis combines findings from many different – yet related – studies to foster empirical knowledge about causal associations that are more trustworthy than those possible from any single study. This benefit arises for two main reasons. First, combining findings from parallel studies promises to increase statistical power and precision for estimating the magnitude of a causal association. More importantly, however, is the potential of meta-analysis to strengthen external validity by identifying the realm of application of a causal association – that is, meta-analyses are most useful when they allow us to examine whether a causal association (1) holds with specific populations of persons, settings, times, and ways of varying the cause or measuring the effect; (2) holds across different populations of people, settings, times, and ways of operationalizing a cause and effect; and (3) can even be extrapolated to other populations of people, settings, times, causes, and effects than those that have been studied to date – that is, meta-analyses offer opportunities to probe external validity questions 1, 2, and 3.
What software is used to conduct meta analysis?
The statistics of meta-analysis could be conducted with software such as Stata or Review manager (RevMan).
What are the two types of statistical models used in meta-analysis?
To accomplish the first purpose in a meta-analysis, that is, to calculate an average effect size, two statistical models can be assumed: the fixed- and the random-effects models.
What is the RR of a meta-analysis of 10 randomized control trials involving 1194 participants?
A meta-analysis of 10 randomized control trials involving 1194 participants showed no differences in the risk of preeclampsia [RR, 0.98; 95% CI, 0.56–1.74], a primary indicator of maternal outcome [13M ]. The meta-analysis included women with GDM who were not controlled with lifestyle modifications and thus required drug treatment. The treatment schedule in the control was insulin and the interventional group was glyburide [ 13M ]. This result was consistent with the findings of a previous meta-analysis of 11 studies that involved 1754 GDM patients [ 7M ].
What is meta analysis?
In general, meta-analysis involves the systematic identification, evaluation, statistical synthesis, and interpretation of results from multiple studies. It is useful particularly when studies on the same or a similar subject or problem present contradictory findings, thereby challenging interpretation of the collective results.
Why is meta analysis important?
Meta-analysis is especially common in the fields of medicine and epidemiology, where it often is used to combine results from observational studies, to guide policy decisions, and to help determine the effectiveness of medical interventions.
What is Bayesian meta analysis?
Bayesian meta-analysis, which allows both the data and the model itself to be random parameters , can also be used. Bayesian methods further allow the inclusion of relevant information external to the meta-analysis and allow for the consideration of the utility of different clinical outcomes. Owing to the latter, in the case of epidemiologic studies, Bayesian methods can facilitate the extension of meta-analysis to decision-making processes. Cumulative meta-analysis is the process of performing a new (or updated) meta-analysis as results become available.
What is a fixed effect meta analysis?
The fixed-effects model applies to a situation that assumes each study result estimates a common (but unknown) pooled effect. The random-effects model assumes that each study result estimates its own (unknown) effect, which has a population distribution (having a mean value and some measure of variability). Thus, the random-effects model allows for between- and within-study variability. Nonetheless, even when using a random-effects model, summary estimates from heterogeneous studies must be interpreted with caution.
What are the criteria for a search for relevant data?
A search for relevant data requires explicit, scientifically valid inclusion and exclusion criteria. Commonly used criteria include time (e.g, time period covered in a review), variables of interest and their operational definitions, study quality, and publication language.
Is meta analysis more widely used?
In addition, as meta-analysis has become more widely used , new methods have emerged. For example, meta-analysis is often complicated by a lack of information on standard deviation of estimates in reports; the validity of various methods of imputing this information from other sources has been studied.
Does inclusion or exclude bias affect meta-analysis?
Despite a researcher’s best attempts to provide an objective measure of quality, decisions to include or exclude studies introduce bias into the meta-analysis. Still other researchers note that the quality of a study might not have an effect on the study’s outcome.
Why do we use meta analysis?
The idea behind conducting Meta analysis is to help the researcher by providing certain methodological literature that the researcher wants to obtain from the experimental research. Measures of effect size are gathered from existing, previously conducted studies and examined to obtain an overall effect in the subject of study.
What databases are used for meta analysis?
They are as follows: Mental Health abstracts. Sociological abstracts. PsycLit / PsycInfo.
What is the first step in meta analysis?
There are certain steps that can help in understanding the procedure of conducting Meta analysis: The first step involves the definition of the theoretical relationship of interest by the researcher.
What is quantitative literature review?
There is a quantitative literature review that consists of articles that contain the effects that are investigated by the researcher under different cases. This furthers the overall strength described by the researcher by using Meta analysis.
What is meta analysis?
v. t. e. A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. Meta-analysis can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error.
Why is meta analysis important?
For instance, a meta-analysis may be conducted on several clinical trials of a medical treatment, in an effort to obtain a better understanding of how well the treatment works.
How does the quality effects meta-analysis work?
They introduced a new approach to adjustment for inter-study variability by incorporating the contribution of variance due to a relevant component (quality) in addition to the contribution of variance due to random error that is used in any fixed effects meta-analysis model to generate weights for each study. The strength of the quality effects 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 statistics in clinical research. To do this a synthetic bias variance is computed based on quality information to adjust inverse variance weights and the quality adjusted weight of the i th study is introduced. These adjusted weights are then used in meta-analysis. In other words, if study i is of good quality and other studies are of poor quality, a proportion of their quality adjusted weights is mathematically redistributed to study i giving it more weight towards the overall effect size. As studies become increasingly similar in terms of quality, re-distribution becomes progressively less and ceases when all studies are of equal quality (in the case of equal quality, the quality effects model defaults to the IVhet model – see previous section). A recent evaluation of the quality effects model (with some updates) demonstrates that despite the subjectivity of quality assessment, the performance (MSE and true variance under simulation) is superior to that achievable with the random effects model. This model thus replaces the untenable interpretations that abound in the literature and a software is available to explore this method further.
How does meta analysis affect results?
However, in performing a meta-analysis, an investigator must make choices which can affect the results, including deciding how to search for studies, selecting studies based on a set of objective criteria, dealing with incomplete data, analyzing the data, and accounting for or choosing not to account for publication bias. Judgment calls made in completing a meta-analysis may affect the results. For example, Wanous and colleagues examined four pairs of meta-analyses on the four topics of (a) job performance and satisfaction relationship, (b) realistic job previews, (c) correlates of role conflict and ambiguity, and (d) the job satisfaction and absenteeism relationship, and illustrated how various judgement calls made by the researchers produced different results.
Why do studies not report the effects?
Studies often do not report the effects when they do not reach statistical significance. For example, they may simply say that the groups did not show statistically significant differences, without reporting any other information (e.g. a statistic or p-value). Exclusion of these studies would lead to a situation similar to publication bias, but their inclusion (assuming null effects) would also bias the meta-analysis. MetaNSUE, a method created by Joaquim Radua, has shown to allow researchers to include unbiasedly these studies. Its steps are as follows:
What are the two types of evidence in meta-analysis?
In general, two types of evidence can be distinguished when performing a meta-analysis: individual participant data (IPD), and aggregate data (AD). The aggregate data can be direct or indirect.
Why are funnel plots controversial?
These are controversial because they typically have low power for detection of bias, but also may make false positives under some circumstances. For instance small study effects (biased smaller studies), wherein methodological differences between smaller and larger studies exist, may cause asymmetry in effect sizes that resembles publication bias. However, small study effects may be just as problematic for the interpretation of meta-analyses, and the imperative is on meta-analytic authors to investigate potential sources of bias.
Why is meta analysis important?
Meta-analysis helps aggregate the information, often overwhelming, from many studies in a principled way into one unified final conclusion or provides the reason why such a conclusion cannot be reached.
What is meta data in Stata?
Declaring the meta-analysis data is the first step of your meta-analysis in Stata. During this step, you specify the main information needed for meta-analysis such as the study-specific effect sizes and their standard errors. You declare this information once by using either meta set or meta esize, and it is then used by all meta commands. The declaration step helps minimize potential mistakes and typing; see [META] meta data for details.
What is meta regression in Stata?
Meta-regression is often used to explore heterogeneity induced by the relationship between moderators and study effect sizes. Moderators may include a mixture of continuous and categorical variables. In Stata, you perform meta-regression by using meta regress.
Why is the funnel plot asymmetric?
Recall, however, that in our earlier heterogeneity analysis, we established the presence of between-study variability. Thus, this may be one of the reasons for the asymmetry of the funnel plot.
How many studies are missing in meta trimfill?
meta trimfill estimated the number of studies missing presumably due to publication bias to be 3, imputed the omitted studies, and reported additional results using both the observed and imputed studies. With the imputed studies, the overall effect-size estimate is reduced from 0.084 to 0.028 with a wider 95% CI.
Why are there small study effects?
Two common reasons for the presence of small-study effects are between-study heterogeneity and publication bias. Publication bias arises when the decision of whether to publish a study's results depends on the significance of the obtained results.
When does heterogeneity occur in meta-analysis?
In meta-analysis, heterogeneity occurs when variation between the study effect sizes cannot be explained by sampling variability alone. meta summarize and meta forestplot report basic heterogeneity measures and the homogeneity test to assess the presence of heterogeneity.
What is meta analysis?
A meta-analysis, sometimes referred to as a meta-analysis study, can be defined as a type of research which uses a statistical approach to combine the findings of numerous empirical studies into a summary study of available data on the given topic. Meta-analytical studies are often used when a large amount of scientific research has already been conducted on a certain topic, or research question. The researcher performing the meta-analysis is able to combine the existing data on the topic to produce a summative study that is more likely to have statistically significant results, or results that are able to reliably support that the difference between the control and test groups in a study was not simply caused by chance. In other words, meta-analyses are useful for averaging the results of many studies on a topic to show that there really is a cause-and-effect relationship between the factors being studied.
What is the overall analysis of meta-analysis?
After each study has been analyzed, the researcher will conduct an overall analysis to account for the different sample sizes of the individual studies in the meta-analysis . This overall approach, or combination of data from several studies into one, is referred to as aggregate data. Most commonly, a weighted-average analysis is used so that each study has the same influence on the overall results of the meta-study. To demonstrate, if a researcher used 30 studies in the meta-analysis and one of them used a sample size of 500 participants but the rest used less than 100 participants, the larger study would factor into the overall results more than the smaller studies. The data in a meta-analysis are often depicted with a type of graphic known as a forest plot.
Why is meta analysis important?
Meta-analysis is a powerful tool for increasing the amount of participant data available to answer a research question, increasing the reliability of the results , and providing summative answers to much-debated research questions.
What are the three types of bias in meta-analysis?
When interpreting the results of, or conducting, a meta-analysis study, there are three types of bias of which people should be aware: selection bias, reporting bias , and publication bias. Selection bias is particularly relevant in meta-analysis research because the researcher is responsible for selecting the studies which will be included in the meta-analysis study. Reporting bias occurs when a particular finding, or result, is omitted from the data set because it does not support the outcome that the researcher desires. The third type of bias, publication bias , occurs when the researcher, or research team, decides not to publish the results of a study because they do not support a certain theory or outcome.
What does meta mean in research?
The prefix "meta", which is Greek for transcending or beyond, is often used to describe the process of thinking about the bigger picture. In the case of meta-analysis, the researcher is using the big picture of studies available to think about a topic. The term meta-analysis means doing a study about studies.
How to validate meta-analysis?
Validating the results of a meta-analysis is normally achieved by testing the results for homogeneity. It is important to determine the degree to which the results of the studies being combined in a meta-analysis are similar, or homogenous. Homogeneity of results in a meta-analysis is desirable so that the data can be aggregated, or combined, without being adapted to meet the needs of the study. To determine homogeneity, researchers test for its opposite, heterogeneity. Cochran's-Q and I-Square, also called I-2 Index are two common statistical methods for determining heterogeneity of research findings.
What is the first step in meta-analysis?
The first step in meta-analysis involves carefully thinking about the topic in order to develop a precise research question.
What is the purpose of meta analysis?
The purpose of meta-analysis is that it seeks to determine whether an effect is present in a study and also determine whether the present effect is a positive one or a negative one. Meta-analysis examines the strengths of the results of a study. It checks whether there is substantial evidence to back up the findings of a study.
Why Meta-Analysis?
Meta-analysis is designed to review the information and put it into simpler terms. Meta-analysis however follows some principles which are:
Why is meta analysis important?
Meta-analysis is helpful if your studies are based on finding the similarity in the Trent between existing research and the new one. However, be mindful of the studies you combine so that your research will not be at risk of biases which can lead to erroneous conclusions.
Why is meta analysis so time consuming?
Meta-analysis is time-consuming. This is because it reviews outcomes from diverse studies.
What is the ability to be totally objective in analyzing and evaluating research outcomes?
Meta-Analysis has the ability to be totally objective in analyzing and evaluating research outcomes.
Why is meta-analysis best avoided?
If there are no similarities in the subjects of study meta-analysis is best avoided because the study may lose its meaning.
When to use meta analysis review?
So as a researcher or investigator, you can be sure to use a meta-analysis review if your study has similar topics or subjects, similar treatments, similar interventions, and similar results.
What is meta analysis?from study.com
meta analysis. A method that uses statistical techniques to combine results from different studies and obtain a quantitative estimate of the overall effect of a particular intervention or variable on a defined outcome —i.e., it is a statistical process for pooling data from many clinical trials to glean a clear answer.
What is a meta-analysis estimate?from en.wikipedia.org
The meta-analysis estimate represents a weighted average across studies and when there is heterogeneity this may result in the summary estimate not being representative of individual studies. Qualitative appraisal of the primary studies using established tools can uncover potential biases, but does not quantify the aggregate effect of these biases on the summary estimate. Although the meta-analysis result could be compared with an independent prospective primary study, such external validation is often impractical. This has led to the development of methods that exploit a form of leave-one-out cross validation, sometimes referred to as internal-external cross validation (IOCV). Here each of the k included studies in turn is omitted and compared with the summary estimate derived from aggregating the remaining k- 1 studies. A general validation statistic, Vn based on IOCV has been developed to measure the statistical validity of meta-analysis results. For test accuracy and prediction, particularly when there are multivariate effects, other approaches which seek to estimate the prediction error have also been proposed.
How does the quality effects meta-analysis work?from en.wikipedia.org
They introduced a new approach to adjustment for inter-study variability by incorporating the contribution of variance due to a relevant component (quality) in addition to the contribution of variance due to random error that is used in any fixed effects meta-analysis model to generate weights for each study. The strength of the quality effects 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 statistics in clinical research. To do this a synthetic bias variance is computed based on quality information to adjust inverse variance weights and the quality adjusted weight of the i th study is introduced. These adjusted weights are then used in meta-analysis. In other words, if study i is of good quality and other studies are of poor quality, a proportion of their quality adjusted weights is mathematically redistributed to study i giving it more weight towards the overall effect size. As studies become increasingly similar in terms of quality, re-distribution becomes progressively less and ceases when all studies are of equal quality (in the case of equal quality, the quality effects model defaults to the IVhet model – see previous section). A recent evaluation of the quality effects model (with some updates) demonstrates that despite the subjectivity of quality assessment, the performance (MSE and true variance under simulation) is superior to that achievable with the random effects model. This model thus replaces the untenable interpretations that abound in the literature and a software is available to explore this method further.
What are some examples of meta-analysis?from study.com
Examples of a meta-analysis include statistically combining the results of many different clinical trials on the cardiovascular benefits of taking daily aspirin for people at risk of heart disease and performing a statistical analysis of the findings from a large number of studies regarding the academic performance of elementary students enrolled in virtual school compared with those who attend in-person. A meta-analysis is a powerful tool for increasing the amount of participant data available to answer a research question, increasing the reliability of the results, and providing summative answers to much-debated research questions.
What is the RR of a meta-analysis of 10 randomized control trials involving 1194 participants?from sciencedirect.com
A meta-analysis of 10 randomized control trials involving 1194 participants showed no differences in the risk of preeclampsia [RR, 0.98; 95% CI, 0.56–1.74], a primary indicator of maternal outcome [13M ]. The meta-analysis included women with GDM who were not controlled with lifestyle modifications and thus required drug treatment. The treatment schedule in the control was insulin and the interventional group was glyburide [ 13M ]. This result was consistent with the findings of a previous meta-analysis of 11 studies that involved 1754 GDM patients [ 7M ].
How to validate meta-analysis?from study.com
Validating the results of a meta-analysis is normally achieved by testing the results for homogeneity. It is important to determine the degree to which the results of the studies being combined in a meta-analysis are similar, or homogenous. Homogeneity of results in a meta-analysis is desirable so that the data can be aggregated, or combined, without being adapted to meet the needs of the study. To determine homogeneity, researchers test for its opposite, heterogeneity. Cochran's-Q and I-Square, also called I-2 Index are two common statistical methods for determining heterogeneity of research findings.
How does meta analysis affect results?from en.wikipedia.org
However, in performing a meta-analysis, an investigator must make choices which can affect the results, including deciding how to search for studies, selecting studies based on a set of objective criteria, dealing with incomplete data, analyzing the data, and accounting for or choosing not to account for publication bias. Judgment calls made in completing a meta-analysis may affect the results. For example, Wanous and colleagues examined four pairs of meta-analyses on the four topics of (a) job performance and satisfaction relationship, (b) realistic job previews, (c) correlates of role conflict and ambiguity, and (d) the job satisfaction and absenteeism relationship, and illustrated how various judgement calls made by the researchers produced different results.
What is meta analysis?from researchgate.net
Meta-analysis is analyzing previous studies in a way that tries to combine their results in a statistical way, rather than informally assessing how much they agree. Meta-analysis is popular in biostatistics, particularly in clinical trials and in genome-wide association studies. Part of the idea is that individual studies might be underpowered to detect results, so if we combined data from multiple studies, we could in e ect increase the sample size and gain better power for testing hypotheses and more precise estimates for con dence intervals.
What are the properties of statistical procedures?from sciencedirect.com
The properties of statistical procedures depend on the availability of unrestricted samples of effect size estimates. If the effect size estimates available to the investigator are systematically biased, special statistical procedures are needed to take account of the biasing mechanism. The chapter discusses the problem created by the censoring of effect size estimates corresponding to statistically nonsignificant results. It presents some evidence on the existence of the sampling bias. The chapter also presents a statistical model for studying the effects of the sampling bias and discusses the consequences of such bias on the estimation of effect size. It highlights the maximum likelihood estimates of effect size under the model of sampling bias. The chapter discusses a combination of estimates from several experiments under the model of sampling bias in addition to the ways in which the results of this chapter can be used to draw conclusions when sampling bias may exist.
What are the disadvantages of multivariate models?from sciencedirect.com
A disadvantage of these multivariate techniques is that they usually require knowledge of the correlations between variables—information that is not always available. In a few instances, such correlations may actually be available. For example, test-norming studies may provide very good estimates of correlations between subscales of psychological tests. Such estimates can be treated as known values to provide the correlations necessary to use the methods given in this chapter. The chapter presents the multivariate distribution of a vector of effect sizes derived from correlated observations. It also discusses the estimation of a common effect size from a vector of correlated estimates. The chapter explores methods for estimating effect sizes from a series of studies in which a few studies provide several correlated estimates and other studies provide a single estimate.
What package do you use for a computational analysis?from sciencedirect.com
Many computations can be completed on a hand calculator, whereas some require the use of a standard statistical package such as SAS, SPSS, or BMD. Readers with experience using a statistical package or who conduct analyses such as multiple regression or analysis of variance should be able to carry out the analyses described with the aid of a statistical package.
Why are omnibus methods called nonparametric?from sciencedirect.com
The procedures are called omnibus or nonparametric because they do not depend on the form of the underlying data but only on the exact significance levels commonly called p -values.
What are the data sets used in this chapter?from sciencedirect.com
This chapter presents several prototypical data sets that are used to illustrate the various statistical methods. The data sets were chosen to represent a range of areas in education and psychology. Each data set has been previously analyzed and published, and a few data sets have been reanalyzed by different investigators. The data sets contain typical information that is available to the research reviewer. The outcome data consist of a single summary statistic used as an index of effect magnitude, usually a standardized mean difference or a correlation coefficient. In addition to the index of effect magnitude, the chapter presents the sample size and characteristics of the experimental conditions in the studies. The selection of these characteristics in the studies is not entirely haphazard. As the object of research synthesis is to determine how broadly a result may generalize, reviewers usually use several characteristics that they believe to be related to the experimental outcome as independent variables.
Is ihas an exponential distribution?from math.unm.edu
ihas an exponential distribution, then the distribution of log(p
Overview
A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. Meta-analyses can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error. The aim then is to use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived. Meta-analytic result…
History
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. The first meta-analysis of all conceptually identical experiments concerning a particular research issue, and conducted …
Steps in a meta-analysis
A meta-analysis is usually preceded by a systematic review, as this allows identification and critical appraisal of all the relevant evidence (thereby limiting the risk of bias in summary estimates). The general steps are then as follows:
1. Formulation of the research question, e.g. using the PICO model (Population, Intervention, Comparison, Outcome).
Methods and assumptions
In general, two types of evidence can be distinguished when performing a meta-analysis: individual participant data (IPD), and aggregate data (AD). The aggregate data can be direct or indirect.
AD is more commonly available (e.g. from the literature) and typically represents summary estimates such as odds ratios or relative risks. This can be directly s…
Challenges
A meta-analysis of several small studies does not always predict the results of a single large study. Some have argued that a weakness of the method is that sources of bias are not controlled by the method: a good meta-analysis cannot correct for poor design or bias in the original studies. This would mean that only methodologically sound studies should be included in a meta-analysis, a practi…
Applications in modern science
Modern statistical meta-analysis does more than just combine the 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 instrument used, population sampled, or aspects of the studies' design can be coded and used to reduce variance of the estimator (see s…
See also
• Estimation statistics
• Metascience
• Newcastle–Ottawa scale
• Reporting bias
• Review journal
Further reading
• Cornell JE, Mulrow CD (1999). "Meta-analysis". In Mellenbergh GJ (ed.). Research methodology in the life, behavioural, and social sciences. London: SAGE. pp. 285–323. ISBN 978-0-7619-5883-3.
• Ellis PD (2010). The Essential Guide to Effect Sizes: An Introduction to Statistical Power, Meta-Analysis and the Interpretation of Research Results. Cambridge: Cambridge University Press. ISBN 978-0-521-14246-5.