We should emphasize that this book is about data analysis and that it demonstrates how stata can be used for regression analysis, as opposed to a book that. The p value of each regression coefficient will indicate whether this difference is statistically significant. A basic tutorial arindam basu university of canterbury may 12, 2017 concepts of metaanalyses meta analysis refers to a process of integration of the results of many studies to arrive at evidence syn. In stata, a comprehensive set of userwritten commands is available for metaanalysis. When running a regression we are making two assumptions, 1 there is a linear relationship between two variables i. A meta regression can be done in stata 16 with the meta regress command. Network meta analysis is a popular way to combine results from several studies usually randomized trials comparing several treatments or interventions.
Although it is commonly ignored in practice, the constant, or intercept, in a meta regression equation can be extremely valuable because using it permits one to estimate confidence bands and intervals. We present a revised version of the metareg command, which performs meta analysis regression meta regression on studylevel summary data. Regression with stata chapter 1 simple and multiple regression. We even can use meta regression models to analyze association between treatment effect and study characteristics. We now briefly describe the four major meta regression approaches presented in the literature.
I am working on a meta regression of a random effects meta analysis with 53 studies. Meta regression introduction fixedeffect model fixed or random effects for unexplained heterogeneity randomeffects model introduction in primary studies we use regression, or multiple regression, to assess the relationship between one or more covariates moderators and a dependent variable. I am working on a metaregression of a random effects metaanalysis with 53 studies. Linear regression analysis in stata procedure, output and. For example, studies in which allocation sequence concealment was adequate may yield different results from those in which it was inadequate. However, the output for one of the variables sample size the regression coefficient and. Metaregression is a tool used in metaanalysis to examine the impact of moderator variables on study effect size using regressionbased techniques.
Depicting estimates using the intercept in metaregression. Reprinted in stata technical bulletin reprints, vol. I have undertaken a meta regression as part of a meta analysis. As we have mentioned before, multiple metaregression, while very useful when applied properly, comes with certain caveats we have to know and consider when fitting a model. Throughout, bold type will refer to stata commands, while le names, variables names, etc. The results of the analyses were applied to a recent study calculating the excess lengthofstay associated with icuacquired ventilatorassociated pneumonia, a major complication of mechanical ventilation. Network metaanalysis is a popular way to combine results from several studies usually randomized trials comparing several treatments or interventions. The major revisions involve improvements to the estimation methods and the addition of an option to use a permutation test to estimate pvalues, including an adjustment for multiple testing we have also made additions to the output, added an.
We present a revised version of the metareg command, which performs metaanalysis regression metaregression on studylevel summary data. Metaanalyses have become an essential tool in synthesizing evidence on clinical and epidemiological questions derived from a multitude of similar studies assessing the particular issue. The regression coefficients will estimate how the intervention effect in each subgroup differs from a nominated reference subgroup. Official metaanalysis commands are available in stata 16 stata 16 contains a suite of commands for performing metaanalysis.
Difference between metaregression and linear regression. We now briefly describe the four major metaregression approaches presented in the literature. I am conducting a meta analysis in psychotherapy research and want to use meta regression to identify moderators. If studies are divided into subgroups see section 9. Note, results are not backtransformed in printouts of metaanalyses using summary measures with transformations, e. Mixedeffects metaregression was performed to explore for sources of heterogeneity. However, lm computes the pvalues based on the tdistribution, while rma uses by default the standard normal distribution. Metaregression may be performed using the metareg macro available for the stata statistical package. This is the second of two stata tutorials, both of which are based on the 12th version of stata, although most commands discussed can be used in. Like ordinary least squares regression models, metaregression models include a single intercept and a slope for each moderator variable. In subsequent papers in this series, we will discuss how you can conduct meta analysis of diagnostic and screening studies, and principles of network meta analyses, where you can conduct a meta analysis with more than one intervention or exposure variable. This will include looking at the accumulation of evidence in cumulative meta analysis, using graphical and statistical techniques to look for evidence of bias, and using meta regression to investigate possible sources of heterogeneity.
Impact of mechanical ventilation on the daily costs of icu. Linear regression and the mixedeffects metaregression model. Basics of stata this handout is intended as an introduction to stata. It has usually been performed in a bayesian setting, but recently it has become possible in a frequentist setting using multivariate metaanalysis and metaregression, implemented in stata. It is used to study the relationship between study effect sizes and covariates. To do the metaregression i have taken all the aspects of the included studies that could have contributed to the heterogeneity followup duration, study size, study population and period of the. Appropriate and accessible statistical software is needed to produce the summary statistic of interest. The overall isquare in the meta analysis is 79% with p0. Meta analysis of studies with binary relative risk, odds ratio, risk difference or continuous outcomes mean differences can be performed. Metaanalysis of proportions introduction this module performs a metaanalysis of a set of twogroup, binaryevent studies. Performing standard metaregression analysis with spss. Categorical models and metaregression ryan williams former managing editor, methods coordinating group assistant professor, counseling, educational psychology, and research, university of memphis, usa campbell collaboration colloquium may 2012. Remarks and examples remarks are presented under the following headings.
I2 residual in metaregression statalist the stata forum. We even can use metaregression models to analyze association between treatment effect and study characteristics. However, when there is substantial unaccounted heterogeneity in. Article information, pdf download for metaregression in stata, open epub for. How do i interpret an e in a metaregression coefficient. It also provides explanations of various plots that are presented as well as introducing metaregression. Linear regression and the mixedeffects metaregression. The major revisions involve improvements to the estimation methods and the addition of an option to use a permutation test to estimate pvalues, including an adjustment for multiple testing.
This document is an introduction to using stata 12 for data analysis. In this model, the data are y i, s i,andx i, and we aim to estimate the. One of these macros is called metareg which can perform fixedeffect or mixedeffects meta regression. The overall isquare in the metaanalysis is 79% with p0. The estimated model coefficients, corresponding standard errors, and the test statistics are exactly the same. It seeks to summarize and explain the disparate empirical findings routinely reported in nearly all areas of economics. To do the meta regression i have taken all the aspects of the included studies that could have contributed to the heterogeneity followup duration, study size, study population and period of the. Indeed, some argue that multiple metaregression is often improperly used and interpreted in practice, leading to a low validity of many metaregression models. These studies have a treatment group arm and a control group. We have also made additions to the output, added an option.
Technically, linear regression estimates how much y changes when x changes one unit. I am conducting a metaanalysis in psychotherapy research and want to use metaregression to identify moderators. This is a revised version of the program originally written by stephen sharp stb42, sbe23. Pdf we present a revised version of the metareg command, which performs metaanalysis regression metaregression on studylevel summary data. Metaprop is a statistical program implemented to perform meta analyses of proportions in stata. Introduction metaregression approaches ncbi bookshelf. I want to see what this variable does to the mortality rate, expressed as rd or or. We should emphasize that this book is about data analysis and that it demonstrates how stata can be used for regression analysis, as opposed to a book that covers the. The first approach is a fixed effects approach which utilizes logistic regression. Stata is available on the pcs in the computer lab as well as on the unix system. This r function is a wrapper function for r function rma. By the way, user wolfgang is the author of an r package called metafor. An extension of mvmeta, my program for multivariate randome. If you are using the official metaanalysis commands in stata 16, the collection of stata journal articles is still valuable because the collection contains information about metaanalysis, and not just information on the communitycontributed metaanalysis commands.
Meta analyses have become an essential tool in synthesizing evidence on clinical and epidemiological questions derived from a multitude of similar studies assessing the particular issue. Metaregression is a technique for performing a regression analysis to assess the relationship between the treatment effects and the study characteristics of interest e. Difference between metaregression and linear regression in. Mra is the statistical analysis of previously reported regression results stanley and jarrell, 1989. Linear regression using stata princeton university. Stata is a software package popular in the social sciences for manipulating and summarizing data and conducting statistical analyses. Note, results are not backtransformed in printouts of meta analyses using summary measures with transformations, e. This is an excellent piece of software to conduct meta regression. Data analysis with stata 12 tutorial university of texas. How to conduct metaregression with continuous and categorial. Stata module to perform metaanalysis regression metareg performs randomeffects metaregression on studylevel summary data. Metaregression is analogous to standard regression used when individual data are available, but in metaregression, the observations. Although there is not heterogeneity in these data to be explained by a meta regression, an example of the command and its output is given below. This will include looking at the accumulation of evidence in cumulative metaanalysis, using graphical and statistical techniques to look for evidence of bias, and using metaregression to investigate possible sources of heterogeneity.
The results of each study may be summarized as counts in a 2by2 table. I have often found it useful to write directly to package authors with queries about their addon packages as a package author, i myself entertain such questions frequently. Metaregression introduction fixedeffect model fixed or random effects for unexplained heterogeneity randomeffects model introduction in primary studies we use regression, or multiple regression, to assess the relationship between one or more covariates moderators and a dependent variable. Meta regression is a tool used in meta analysis to examine the impact of moderator variables on study effect size using regression based techniques. In stata, a comprehensive set of userwritten commands is available for meta analysis. The relative increase in the daily costs was estimated using random effects meta regression. The command meta summarize, subgroup can be used to do a subgroup analysis. This will generate the output stata output of linear regression analysis in stata. Meta regression is a statistical method that can be implemented following a traditional meta analysis and can be regarded as an extension to it. Meta regression is more effective at this task than are standard meta analytic techniques. Regression with stata chapter 1 simple and multiple.
Metaregression columbia university mailman school of. Although it is commonly ignored in practice, the constant, or intercept, in a metaregression equation can be extremely valuable because using it permits one to estimate confidence bands and intervals. Introduction to metaregression analysis hendrix college. Openingsaving a stata datafile quick way of finding variables subsetting using conditional if stata color coding system from spsssas to stata example of a dataset in excel from excel to stata copyandpaste. If you are using the official meta analysis commands in stata 16, the collection of stata journal articles is still valuable because the collection contains information about meta analysis, and not just information on the communitycontributed meta analysis commands. This book is composed of four chapters covering a variety of topics about using stata for regression. Metaregression is more effective at this task than are standard metaanalytic techniques. One of these macros is called metareg which can perform fixedeffect or mixedeffects metaregression. Welcome tessa, this seems to be a stataspecific and, actually, a metaregspecific question, so i have voted to shift it to stackoverflow where stataspecific questions are usually handled. Brief overview of metaanalysis metaanalysis models commoneffect.
It has usually been performed in a bayesian setting, but recently it has become possible in a frequentist setting using multivariate meta analysis and meta regression, implemented in stata. We present a revised version of the metareg command, which performs metaanalysis regression. This is an excellent piece of software to conduct metaregression. For how to perform metaanalysis in stata, seemeta meta. Metaprop is a statistical program implemented to perform metaanalyses of proportions in stata. Like ordinary least squares regression models, meta regression models include a single intercept and a slope for each moderator variable. Official meta analysis commands are available in stata 16 stata 16 contains a suite of commands for performing meta analysis. This paper is a basic introduction to the process of metaanalysis.
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