Consider model logit(p)=a+bx. I would like to get a analytic formula of a and b like in linear regression. In linear regression, we can get a formula of estimates of a and b.
I tried using MLE to estimate it. But it is too complicated for me.
I am doing research about the statistical genetics. Here p is penetrance and x is genotype. In linear regression, we can use the coefficient b to represent the covariance matrix of x and trait y (both of them should be standardized). I am thinking whether we can use the b in logistic model to recover the information about the covariance matrix of x and y (binary trait) in meta-analysis (they do not provide individual level data). But some cohort will prove the odds ratio and coefficient t-statistic. I do not know how to use it to recover the covariance matrix of x and y.
But some summary statistics will be provided like estimated odds ratio, Coefficient t-statistic.
1 answer
Don't go overboard when trying to learn something new. If you're not comfortable with the model, work with it some more before jumping into the complicated stuff. I couldn't even finish reading your paragraph there, it's so full of different ideas and poorly defined words like meta-analysis.
Start at the basic model and plug in some values and see what it means. Make up scenarios.
logit(p)=a+bx, you said p is penetrance, presumably a number in (0-1) so for example try p=0.99. Then the penetrance is high and genotype should be directing phenotype. With x being a genotype, you'll have to evaluate x=(0,1,2), make a table, and fill up a few pieces of paper.
What's logit(p) anyway?
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