Dear all,
We have a rare binary outcome variable (0/1) with 85 observations are 1's (14%) and n=600. Different procedures of estimation are used (firthlogit, penlogit, relogit, exactlogit). How to deal with an explanatory variable, let's say x1, binary, which is also scant?
x1 may present potentially problematic statistical issues that must we address because we have only 3% of x1 are one's (20 observations). The tetrachoric correlation between y and x1 gives (rho = 0.4 ; p-value = 0.006).
Thanks
We have a rare binary outcome variable (0/1) with 85 observations are 1's (14%) and n=600. Different procedures of estimation are used (firthlogit, penlogit, relogit, exactlogit). How to deal with an explanatory variable, let's say x1, binary, which is also scant?
x1 may present potentially problematic statistical issues that must we address because we have only 3% of x1 are one's (20 observations). The tetrachoric correlation between y and x1 gives (rho = 0.4 ; p-value = 0.006).
PHP Code:
Number of obs = 605
Wald chi2(1) = 9.22
Penalized log likelihood = -235.42229 Prob > chi2 = 0.0024
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y | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | 1.472613 .4850946 3.04 0.002 .5218447 2.423381
cons | -1.900057 .1225844 -15.50 0.000 -2.140318 -1.659796
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