Thank you very much for bringing up this rare topic. My question what's the substantial difference between logistf and brglm package? Also, can I get the bootstrapped values of the summaries, respectively for parameter estimates, standard error, OR, 95% CI for OR and p-value for OR?
Hi there, thanks for your video. As a premise, I'm not an expert in statistics. Could you please explain me why, in the plainest way you can, if I run this test and I get a p-value < α (let's say < 0.05), if I try to manually calculate the 95% confidence interval based on the values of coefficient and std error found (= e^(B ± 1.96*se)), it contains the value 1? It does not match with the one obtained by the use of "profile penalized log likelihood" (i'm using SPSS with R extension to use the logistf function), does it use a different way to calculate it? In that case, which one is the most reliable? Thank you!
Thanks for this interesting short tutorial! i see that here you go directly to insert all dependent variables! How do i do if i want to have the univariate analysis using firth LR. I tried using the independent variable and one dependent variable, but R shows errors !
Hi Mike, thank you for your clear explanation. I followed all the steps but the output is different from yours. I would be grateful if I could advise me. I would like to report OR, CI, and P values
if still needed, after computing the normal firth LR command, you will have to type the command exp(coef(fit..)) to have the crude /adjusted OR . While the p value and CI is already present in the previous output .
Thank you very much for bringing up this rare topic. My question what's the substantial difference between logistf and brglm package? Also, can I get the bootstrapped values of the summaries, respectively for parameter estimates, standard error, OR, 95% CI for OR and p-value for OR?
Thank you for your efforts. However, compare both methods there is no significant change in the terms of the p-value. It doesn't improve the results.
Thanks for this! It is very helpful!
How does Firth respond to models with quasi-complete separation? Do those models actually converge and provide salient estimates for coefficients?
Hi there, thanks for your video.
As a premise, I'm not an expert in statistics.
Could you please explain me why, in the plainest way you can, if I run this test and I get a p-value < α (let's say < 0.05), if I try to manually calculate the 95% confidence interval based on the values of coefficient and std error found (= e^(B ± 1.96*se)), it contains the value 1? It does not match with the one obtained by the use of "profile penalized log likelihood" (i'm using SPSS with R extension to use the logistf function), does it use a different way to calculate it? In that case, which one is the most reliable?
Thank you!
Thanks for this interesting short tutorial! i see that here you go directly to insert all dependent variables! How do i do if i want to have the univariate analysis using firth LR. I tried using the independent variable and one dependent variable, but R shows errors !
thanks for such a nice way to describe rare topic in Statistics. I need some help in univarite level of Firth ... any suggestion
It seems the outcome variable is not a factor. Moreover, does this logistic regression is giving odd ratios for 0 versus 1 or 1 versus 0 of AchC
Hi Mike, thank you for your clear explanation. I followed all the steps but the output is different from yours. I would be grateful if I could advise me. I would like to report OR, CI, and P values
if still needed, after computing the normal firth LR command, you will have to type the command exp(coef(fit..)) to have the crude /adjusted OR . While the p value and CI is already present in the previous output .
What are the difference between logistf package and Firth package?
Is there a way to convert the CI interval to the more conventionally displayed form? I have never seen a negative lower limit of confidence interval..
right? me neither, i guess it exist when using data with very few entries! i had it myself.