Can I calculate odds ratio for Firth's Bias-Reduced Logistic Regression?
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I have data that I apply a test called Firth's Bias-Reduced Logistic Regression.
I am using R with package called brglm. I got coefficient and I will try to find a way to calculate a confidence interval
Is it valid to take exponentiation to calculate and report odds-ratio in this type of test?
regression logistic logit
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Omar113
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up vote
2
down vote
favorite
I have data that I apply a test called Firth's Bias-Reduced Logistic Regression.
I am using R with package called brglm. I got coefficient and I will try to find a way to calculate a confidence interval
Is it valid to take exponentiation to calculate and report odds-ratio in this type of test?
regression logistic logit
asked yesterday
Omar113
133
add a comment |Â
up vote
2
down vote
favorite
up vote
2
down vote
favorite
I have data that I apply a test called Firth's Bias-Reduced Logistic Regression.
I am using R with package called brglm. I got coefficient and I will try to find a way to calculate a confidence interval
Is it valid to take exponentiation to calculate and report odds-ratio in this type of test?
regression logistic logit
asked yesterday
Omar113
133
I have data that I apply a test called Firth's Bias-Reduced Logistic Regression.
I am using R with package called brglm. I got coefficient and I will try to find a way to calculate a confidence interval
Is it valid to take exponentiation to calculate and report odds-ratio in this type of test?
regression logistic logit
regression logistic logit
asked yesterday
Omar113
133
asked yesterday
Omar113
133
edited yesterday
asked yesterday
Omar113
133
asked yesterday
Omar113
133
asked yesterday
Omar113
133
133
add a comment |Â
add a comment |Â
1 Answer
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up vote
2
down vote
accepted
In short, yes.
If you have coefficients on the log-odds scale, which is what Firth's penalized likelihood (or bias-reduced) logistic regression reports, using exp(coefficient) gets you an odds ratio. In fact, that is more than anything a core motivation beyond the particular method: getting estimates of coefficients/odds ratios that remove the first order bias term. It so happens that this has some nice side effects such as dealing with complete separation in a way that may be considered reasonable (but not reasonable if you think that some things might be completely causal to yes or no).
Confidence intervals work the same way: get the confidence intervals (or if you have only standard errors use $hatbeta pm textfactor such as 1.96 times textSE$) and exponentiate the limits.
answered yesterday
Björn
8,2291833
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
2
down vote
accepted
In short, yes.
If you have coefficients on the log-odds scale, which is what Firth's penalized likelihood (or bias-reduced) logistic regression reports, using exp(coefficient) gets you an odds ratio. In fact, that is more than anything a core motivation beyond the particular method: getting estimates of coefficients/odds ratios that remove the first order bias term. It so happens that this has some nice side effects such as dealing with complete separation in a way that may be considered reasonable (but not reasonable if you think that some things might be completely causal to yes or no).
Confidence intervals work the same way: get the confidence intervals (or if you have only standard errors use $hatbeta pm textfactor such as 1.96 times textSE$) and exponentiate the limits.
answered yesterday
Björn
8,2291833
add a comment |Â
up vote
2
down vote
accepted
In short, yes.
If you have coefficients on the log-odds scale, which is what Firth's penalized likelihood (or bias-reduced) logistic regression reports, using exp(coefficient) gets you an odds ratio. In fact, that is more than anything a core motivation beyond the particular method: getting estimates of coefficients/odds ratios that remove the first order bias term. It so happens that this has some nice side effects such as dealing with complete separation in a way that may be considered reasonable (but not reasonable if you think that some things might be completely causal to yes or no).
Confidence intervals work the same way: get the confidence intervals (or if you have only standard errors use $hatbeta pm textfactor such as 1.96 times textSE$) and exponentiate the limits.
answered yesterday
Björn
8,2291833
add a comment |Â
up vote
2
down vote
accepted
up vote
2
down vote
accepted
In short, yes.
If you have coefficients on the log-odds scale, which is what Firth's penalized likelihood (or bias-reduced) logistic regression reports, using exp(coefficient) gets you an odds ratio. In fact, that is more than anything a core motivation beyond the particular method: getting estimates of coefficients/odds ratios that remove the first order bias term. It so happens that this has some nice side effects such as dealing with complete separation in a way that may be considered reasonable (but not reasonable if you think that some things might be completely causal to yes or no).
Confidence intervals work the same way: get the confidence intervals (or if you have only standard errors use $hatbeta pm textfactor such as 1.96 times textSE$) and exponentiate the limits.
answered yesterday
Björn
8,2291833
In short, yes.
If you have coefficients on the log-odds scale, which is what Firth's penalized likelihood (or bias-reduced) logistic regression reports, using exp(coefficient) gets you an odds ratio. In fact, that is more than anything a core motivation beyond the particular method: getting estimates of coefficients/odds ratios that remove the first order bias term. It so happens that this has some nice side effects such as dealing with complete separation in a way that may be considered reasonable (but not reasonable if you think that some things might be completely causal to yes or no).
Confidence intervals work the same way: get the confidence intervals (or if you have only standard errors use $hatbeta pm textfactor such as 1.96 times textSE$) and exponentiate the limits.
answered yesterday
Björn
8,2291833
answered yesterday
Björn
8,2291833
answered yesterday
Björn
8,2291833
answered yesterday
Björn
8,2291833
8,2291833
add a comment |Â
add a comment |Â
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