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Meta-analysis: continuous measure

Next selectMeta-analysis
Next selectContinuous measure


For a short overview of meta-analysis in MedCalc, see Meta-analysis: introduction.

For meta-analysis of studies with a continuous measure (comparison of means between treated cases and controls), MedCalc uses the Hedges g statistic as a formulation for the standardized mean difference under the fixed effects model. Next the heterogeneity statistic is incorporated to calculate the summary standardized mean difference under the random effects model (DerSimonian & Laird, 1986).

The standardized mean difference Hedges g is the difference between the two means divided by the pooled standard deviation, with a correction for small sample bias:

Formula for Hedges G

where Γ is the Gamma function.

How to enter data

The data of different studies can be entered as follows in the spreadsheet:

How to enter data for meta-analysis: continuous measure

In this example, in a first study 40 cases were treated and the mean of the parameter of interest was 23.52 with a standard deviation of 1.38. In 40 control cases the mean was 20.12 with standard deviation of 3.36. On the next rows of the spreadsheet follow the data of 4 other studies.

Required input

The dialog box for "Meta-analysis: continuous measure" can then be completed as follows:

Meta-analysis: continuous measure - dialog box

Studies: a variable containing an identification of the different studies.

Intervention groups & Control groups:

Number of cases, Mean, Standard deviation: variables containing the number of cases, mean and standard deviation observed in the different studies, in the intervention groups and control groups respectively.

Filter: a filter to include only a selected subgroup of cases in the graph.



Meta-analysis: continuous measure - results

The program lists the results of the individual studies: number of positive cases, total number of cases, the standardized mean difference (SMD) with 95% CI.

The total Standardized Mean Difference with 95% CI is given both for the Fixed effects model and the Random effects model.

If the value 0 is not within the 95% CI, then the SMD is statistically significant at the 5% level (P<0.05).

Cohen's rule of thumb for interpretation of the SMD statistic is: a value of 0.2 indicates a small effect, a value of 0.5 indicates a medium effect and a value of 0.8 or larger indicates a large effect.

The random effects model will tend to give a more conservative estimate (i.e. with wider confidence interval), but the results from the two models usually agree where there is no heterogeneity. See Meta-analysis: introduction for interpretation of the heterogeneity statistics Cochran's Q and I2. When heterogeneity is present the random effects model should be the preferred model.

See Meta-analysis: introduction for interpretation of the different publication bias tests.

Forest plot

The results of the different studies, with 95% CI, and the overall standardized mean difference with 95% CI is shown in the following forest plot:

Meta-analysis: continuous measure - forest plot


See also

Recommended book

Book cover

Introduction to Meta-Analysis
Michael Borenstein, Larry V. Hedges, Julian P. T. Higgins, Hannah R. Rothstein

Buy from Amazon

This book provides a clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies. Meta-analysis has become a critically important tool in fields as diverse as medicine, pharmacology, epidemiology, education, psychology, business, and ecology.