Meta-Analysis Regression

The Meta-Analysis Regression procedure performs meta-regression analysis.

Several research studies were conducted in history to investigate a faddish but debatable medicine to help treat type II diabetes. The oral medicine was claimed to be able to reduce the blood glucose level after meals. Data were collected from different research sites from 1979 to 1986.
A principal investigator would like to draw statistical inference about the effect of the oral medicine. Due to the fact that the data were generated from different studies, she proposed the idea of synthesizing the results across the studies to reach an overall understanding of the effect and to identify those underlying sources of variation in the outcomes.
Confidence level, iterative method, step-halving, convergence tolerance, sample means, sample variance, standard deviation, estimated effect size, estimation method, regression-based test, random-effects model, fixed-effects model, dispersion parameter, restricted maximum likelihood estimator, empirical Bayes estimator, Hedges estimator, Hunter-Schmidt estimator, DerSimonian-Laird estimator, Sidik-Jonkman estimator, Knapp-Hartung standard-error adjustment, truncated Knapp-Hartung standard-error adjustment, coefficients, model coefficient test, exponentiated statistics.

Obtaining a Meta-Analysis Regression analysis

  1. From the menus choose:

    Analyze > Meta Analysis > Meta Regression

  2. Select a single dependent Effect Size variable that denotes the effect size. The selected variable must be numeric (string variables are not supported).
  3. Select one of the following settings and then select a corresponding single numeric variable:
    Standard error
    Select a variable that specifies the standard error that is converted to the weight. This is the default setting.
    Select a variable that specifies the variance that is converted to the weight.
    Select a variable that specifies the weight.
  4. Optionally, add factor variables to the Factor(s) list. For each selected factor variable, an optional value can be specified for each variable to designate custom Last Categories. You can click Reset to restore the Last Categories values to their default settings.
    Note: When there are no cases that match the specified Last Categories values, the last occurring values are treated as the last categories.
  5. Optionally, select numeric covariate variables.
  6. Optionally, select a Model setting.
    The default setting builds the random-effects model.
    Builds the fixed-effects model. You can optionally select the Include dispersion parameter setting.
  7. Optionally, you can:
    • Click Criteria... to specify the general criteria.
    • Click Inference to specify the estimation methods.
    • Click Print to control the table outputs.
    • Click Save to predict and save the estimated statistics to the active dataset.
    • Click Plot to specify which plots to include in the output.
  8. Click OK.

This procedure pastes META REGRESSION command syntax.