Meta-analysis: odds ratio

Command:

Statistics Meta-analysis Odds ratio

Description

A meta-analysis integrates the quantitative findings from separate but similar studies and provides a numerical estimate of the overall effect of interest (Petrie et al., 2003).

Under the fixed effects model, it is assumed that all studies come from a common population, and that the effect size (odds ratio) is not significantly different among the different trials. This assumption is tested by the "Heterogeneity test". If this test yields a low P-value (P<0.05), then the fixed effects model may be invalid. In this case, the random effects model may be more appropriate, in which both the random variation within the studies and the variation between the different studies is incorporated.

MedCalc uses the Mantel-Haenszel method for calculating the weighted summary Odds ratio under the fixed effects model. Next the heterogeneity statistic is incorporated to calculate the summary Odds ratio under the random effects model (DerSimonian and Laird).

How to enter data

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

In this example, in a first study 73 cases were treated with an active substance and of these, 15 had a positive outcome. 23 cases received a placebo and 3 of these had a positive outcome. On the next rows of the spreadsheet follow the data of 4 other studies.

Required input

The dialog box for "Meta-analysis: odds ratio" can then be completed as follows:

Results

The program lists the results of the individual studies: number of positive cases, total number of cases, the odds ratio with 95% CI.

The Total Odds ratio with 95% CI is given both for the Fixed effects model and the Random effects model. If the value 1 is not within the 95% CI, then the Odds ratio is statistically significant at the 5% level (P<0.05).

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. If the test of heterogeneity is statistically significant (P<0.05) then more emphasis should be placed on the random effects model.

Graph

The results of the different studies, with 95% CI, and the overall effect with 95% CI is shown in the following graph (called a forest plot):

Note that the Odds ratios with 95% CI are drawn on a logarithmic scale.

Literature

• DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Controlled Clinical Trials, 7, 177-188. [Abstract]
• Petrie A, Bulman JS, Osborn JF (2003) Further statistics in dentistry. Part 8: systematic reviews and meta-analyses. British Dental Journal, 194:73-78. [Abstract]

See also

• Continuous measure