CLMM/CLMM2 (R) 中优势比的置信区间

Question

I am trying to find the best way to estimate the confidence intervals for odds ratios as a part of CLMM output. I am working in R, and my model looks something like this:

model <- clmm(Rating ~ Problem+Condition+(1|Subject), data = data, Hess=TRUE, nAGQ=10)

> summary(model)
Cumulative Link Mixed Model fitted with the adaptive Gauss-Hermite 
quadrature approximation with 10 quadrature points 

formula: Rating ~ Problem + Condition + (1 | Subject)
data:    data

 link  threshold nobs logLik   AIC     niter     max.grad cond.H 
 logit flexible  1106 -1114.39 2244.79 545(1638) 1.57e-03 3.1e+01

Random effects:
 Groups  Name        Variance Std.Dev.
 Subject (Intercept) 0.3296   0.5741  
Number of groups:  Subject 96 

Coefficients:
           Estimate Std. Error z value Pr(>|z|)    
Problem1    -0.9696     0.1814  -5.345 9.03e-08 ***
Problem2     0.7001     0.1715   4.083 4.45e-05 ***
Problem3    -0.1745     0.1711  -1.020   0.3078    
Condition1   0.3057     0.1440   2.124   0.0037 **  
Condition2   0.1103     0.1427   0.773   0.4396    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

I understand that the odds ratio for the parameter estimates are just exp(β). Is there a way to calculate confidence intervals for the odds ratio? Open to other ways to express the magnitude of these effects? Thanks!

Answer 1

Just had to do something similar, here is my solution:

Obtain the 95% CI for a fitted model (in the example below: "models.OrdinalMixed_1") with clmm with

CI_tables.OrdinalMixed_1 <- confint(models.OrdinalMixed_1, level = 0.95)

and then you can exponentiate the 95% CIs just like your coefficients":

exp(CI_tables.OrdinalMixed_1)

You can confirm by calculating the CI's manually (exemplified by your coefficient for "Problem2"):

exponentiate your coefficient for your OR:

exp(0.7001)

[1] 2.013954

calculate the upper CI by exponentiating your coefficient + 1.96 * the standard error of the term (this is an approximation!)

exp(0.7001 + (1.96*0.1715))

[1] 2.818599

should be very similar to your result in the OR CI table from above. Hope this is helpful.