= Combining p-values by Fisher's method =

The basic idea is that if $$p_i (i=1 \ldots n)$$ are the one-sided $$p$$-values for $$n$$ independent statistics then $$-2 \sum\log(p_i)$$ has is a $$\chi^2(2n)$$ statistic which reflects whether the combined $$p$$-values are smaller than would be expected if they were Uniform(0,1) variates.

The following MATLAB code evaluates this statistic and its p-value.

{{{
function p = pfast(p)
% Fisher's method for combination of independent p-values
    product=prod(p);
    n=length(p);
    if n<=0
        error('pfast was passed an empty array of p-values')
    elseif n==1
        p = product;
        return
    elseif product == 0
        p = 0;
        return
    else
        x = -log(product);
        t=product;
        p=product;
        for i = 1:n-1
            t = t * x / i;
            p = p + t;
        end
    end  
}}}

Let's try it out:

{{{ 
>> pvals=[0.1 0.01 0.01 0.7 0.3 0.1];
>> pfast(pvals)

ans =

    0.0021
}}}

I.e. the combined p-value is 0.0021 for this array of 6 $$p$$-values.