Dear all,
I have the following table for a breast cancer study and I was trying to
calculate the odds ratio of different subtypes based on AR (AR+ as the base
case).
AR- AR+Luminal A 1 19Luminal B 1 15Her2
0 4Basal-like 20 1Normal Like 2 2
I did Firth logistic regression using the model:
fit <- logistf(AR ~
Age+LumA+LumB+HER2+Basal+Normal-1,data=df).summary(fit)exp(coef(fit))exp(confint(fit))
coef se(coef) lower 0.95 upper 0.95 Chisq pAge
0.02675758 0.03138425 -0.03462915 0.09404934 0.7210455 0.39580118LumA
-4.04312870 2.08208148 -8.91195072 -0.32580912 4.6216454 0.03157094LumB
-3.74769116 1.96613514 -8.19454796 -0.16290619 4.2262682 0.03980286HER2
-3.71796180 2.45080140 -9.50654675 0.54467296 2.8798910 0.08969209Basal
1.12126338 1.83884710 -2.45335167 5.00553060 0.3763601 0.53955773Normal
-1.51899257 2.05597939 -5.97976670 2.40100862 0.5806537 0.44605621
My question is: how come the p-value of Basal is so large? I was expecting a
smaller p-value than LumA or LumB.
Some extra information:
> exp(coef(fit)) Age LumA LumB HER2 Basal
> Normal 1.02711878 0.01754250 0.02357211 0.02428341 3.06872873 0.21893233
> exp(confint(fit)) Lower 95% Upper 95%Age 9.659636e-01
> 1.0986139LumA 1.347687e-04 0.7219430LumB 2.761551e-04 0.8496709HER2
> 7.436339e-05 1.7240445Basal 8.600484e-02 149.2362476Normal 2.529416e-03
> 11.0343002
If I calculate odds ratio using an online calculator based on: AR-
AR+Basal 20 1Non-basal 4 40
I got:
OR:200 95% CI: 20.9510 to 1909.2138 p < 0.0001
Can someone please help me explain?
Thank you very much!
Peter
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