Select the Regression tab and click the Manage button in the Predictors group. In STATISTICA, open the Regression.sta data set containing study-level predictors. If no predictors have been entered yet, you can add them now. In my (non-clinical) research I am happy with p < 0.10.Meta-regression is used to create a model describing the linear relationship between (both continuous and categorical) study-level covariates and the effect size ( Hartung et al., 2008 Borenstein et al., 2009 Higgins and Green, 2011). That really depends on your research question. Q9: What is the preferable level of significance? Is p<0.05 still acceptable for clinical research in such an analysis? Q8: How can I weight my effect size for sample size?Īs far as I know, all meta-regression approaches expect the weights to be the inverse study variance, i.e. However, if you have a categorical (nominal) predictor variable, you will have to deal with dummy variables (see Multiple Regression with Categorical Variables). What is a "scale variable"? Do you mean a continuous/metric variable? You predictor variables can have any level of measurement. Q7: Does the independent variable have to be a scale variable? The independent variable must be also scale, or could be ordinal or nominal? Q6: How does the number of the studies affect the number of independent variables that I should enter simultaneously? Please consult a textbook for the problem of multicollinearity. Q5: How does the correlation between the independent variables affects this choice?Ī high correlation between your predictor variables will have a (negative) impact on your results. I would start with three simple bivariate models then build more complex models (be aware of multicollinearity, see below). Q4: Can I enter all three regressors at once and report the global model, or do I have to enter one regressor at a time and report 3 models each one separately? You wouldn't run a regression with 5 data points, would you? In your comment, you state that you have 20 studies which is enough to run a meta-regression. Q3: Why would 10 studies with 200 patients be enough, but 5 studies with 400 patients not be enough? Or the number of individuals? No, it is not (or less) important. What is total sample size? The number of studies? Yes, it is important. Q2: Is the total sample size an important consideration?
The answer can be found in Borenstein et al (2009: 188): Q1: What is the minimum number of studies necessary for a meta-regression? Some people suggest at least 10 studies are required.
#Revman 5 meta regression software#
This is an excellent piece of software to conduct meta-regression.Īs a general (non-technical) intro to meta-regression, I can recommend Thompson/Higgins (2002) "How should meta-regression analyses be undertaken and interpreted?". By the way, user Wolfgang is the author of an R package called metafor. One of these macros is called MetaReg which can perform fixed-effect or mixed-effects meta-regression. Have a look at David Wilson's SPSS "macros for performing meta-analytic analyses".
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