Step 1 : Note down p values from summary table for each baseline variable or calculate p values from summary statistics by using ANOVA method here( can be done for two groups as well, as t-test is special case of ANOVA)

Step 2 : Enter p values (in descending lines like a column,dont use comma in between) from independent trials here-can be pasted from excel column

Interpretation :

Notes : Cons of Using combined p values in Carlisle method : Carlisle method is suspicious if combined p values too high or too low ; However often this is the case in multi centre or stratified RCTs (which arent truly randomised ) leading to high p values , baseline group variables-covariates are often correlated( can lead to low p values) Carlisle notes these flaws in his analysis as well.Hence it makes sense to avoid Carlisle's method for multi centre or stratified trials and avoid correlated baseline group variables.

References : Stouffer test , Carlisle 2015 , Carlisle 2017 , Nick Brown's blog post with critical analysis of Carlisle method , Simonsohn 2013 ,.

Do we really Need to check for baseline imbalance ?: Senn 1994 , de Boer 2015 , Altman .