Menu location: Analysis_Meta-Analysis_Options.
You can set some common calculation and charting options via these functions:-
'Try exact calculation': This dictates whether or not conditional maximum likelihood calculations are attempted – the computations can take a long time with large tables that contain a mixture of large and small numbers. In most cases the calculation completes within a few seconds.
Continuity correction for sparse tables: This determines the number that is added to each count in a table that contains zero in any cell where a zero would prevent the calculation of the main effect or its variance - for example the odds ratio of a fourfold table (a, b, c, d) is the ratio of cross-products, (a*d)/(b*c), therefore a zero b or c would make it impossible to calculate the odds ratio. The traditional work-around has been to add 0.5 to each count in the table – the so-called 'Haldane correction'. Agresti (1996) recommends adding smaller constants - you may enter your choice. Very small constants bring the Mantel-Haenszel estimate of the pooled odds ratio very close to the conditional maximum likelihood estimate. The Peto method performs well with sparse datasets where the event rate is low, provided there is not a large imbalance between the sizes of the treatment vs. control groups (Bradburn et al, 2006). Alternative empirical continutity corrections (Sweeting et al., 2004) have been introduced to improve calculations where there is a substantial difference between the sizes of the groups being compared. The so called 'reatment arm (sums to 1)' continuity correction performs well with the Mantel-Haenszel pooled estimate. If R is the ratio of treatment to control group sizes then the treatment arm continuity correction for the treatment group is 1/(R+1), and for the control group it is R/(R+1).
'Delay continuity correction': This option works only with the odds ratio fixed-effects Mantel-Haenszel calculation, where it delays the application of any continutity correction that is necessary until the random effects stage.
These options determine the type of bias detection plot used. The vertical axis options are: standard error; precision; 1/sample size; sample size; 1/log(sample size); log(sample size); 1/Mantel-Haenszel weight. If you are considering an incidence study then you should make your selection by reading these options as 'person-time' instead of 'sample size'.
The 'include CI if relevant' check box determines whether or not confidence interval lines are drawn on the bias detection plot – these form an inverted triangle on a traditional 'funnel plot'.
See bias detection for more information about these plots.