Odds Analyze Review

Posted on 4/15/2022by admin

Command:

Statistics
Meta-analysis
Odds ratio

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Description

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

MedCalc uses the Mantel-Haenszel method (Mantel & Haenszel, 1959) for calculating the weighted pooled 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 & Laird, 1986).

How to enter data

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

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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:

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

Intervention groups

Total number of cases: a variable containing the total number of cases in the intervention groups of the different studies
Number with positive outcome: a variable containing the number of cases with positive outcome in the intervention groups of the different studies

Control groups

Total number of cases: a variable containing the total number of cases in the control groups of the different studies
Number with positive outcome: a variable containing the number of cases with positive outcome in the control groups of the different studies

Filter

A filter to include only a selected subgroup of studies in the meta-analysis.

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

Options

Forest plot: creates a forest plot.
- Marker size relative to study weight: option to have the size of the markers that represent the effects of the studies vary in size according to the weights assigned to the different studies. You can choose the fixed effect model weights or random effect model weights.
- Plot pooled effect - fixed effects model: option to include the pooled effect under the fixed effects model in the forest plot.
- Plot pooled effect - random effect model: option to include the pooled effect under the random effects model in the forest plot.
- Diamonds for pooled effects: option to represent the pooled effects using a diamond (the location of the diamond represents the estimated effect size and the width of the diamond reflects the precision of the estimate).
Funnel plot: creates a funnel plot to check for the existence of publication bias. See Meta-analysis: introduction.

Results

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

The pooled 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. See Meta-analysis: introduction for interpretation of the heterogeneity statistics Cohran'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.

Note that when a study reports no events (or all events) in both intervention and control groups the study provides no information about relative probability of the event and is automatically omitted from the meta-analysis (Higgins & Green, 2011).

Forest plot

The results of the different studies, with 95% CI, and the overall effect with 95% CI are shown in a forest plot:

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