Comments (2)
Hi Gauti,
It is unclear to me whether you are comparing ordLORgee with the results from ordgee or geeglm functions, so I consider both cases:
- Comparison with ordgee: It seems that there is a bug in ordgee so I would not use this function. See Introduction Section
- Comparison with geeglm: It depends on the coding of the response categories for the categorical and of the scores in the linear variable (higher score implies anxiety?). The key is to focus on the interpretation and not the sign. For example, consider the following code, using the dataset arthritis from
multgee
:
library("multgee")
data("arthritis")
fitmod <- ordLORgee(formula = y ~ factor(trt) , data = arthritis, id = id, repeated = time, LORstr = "independence")
coef(fitmod)
beta10 beta20 beta30 beta40 factor(trt)2
-3.021 -1.038 0.698 2.656 -0.521
library("geepack")
fitmod_gee <- geeglm(formula = y ~ factor(trt), data = arthritis, id = id, corstr = "independence")
coef(fitmod_gee)
(Intercept) factor(trt)2
3.079 0.296
In this example, the signs for the estimated regression coefficient for the treatment are different, but the interpretation is similar. In the gee multinomial model, the estimated coefficient is negative which means that the cumulative odds for a fixed response category or below are lower for the treatment (hence the treatment is better than the placebo).
In the linear model (second model), it seems that the treatment increases the score of the response variable (again treatment is better than placebo).
I hope this helps. Just keep in mind, that the above is not a mathematical proof and I cannot prove that this is true for all datasets.
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Hi Anthestis
Thank you very much for you answer.
I was comparing ordLORgee to both geeglm and ordgee, but will not use ordgee because of known bugs (as you mention) but also because it does not work with glht, which I use to pull out results for interaction terms.
I still don't understand why the sign of the estimate is not the same but still interpretation the same. I am even tempted to reverse my ordered outcome so I get the estimates with same sign as I am used to.
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Related Issues (8)
- ordLORgee got killed due to memory issue HOT 3
- "Please insert initial values" using ordLORgee HOT 3
- Non-cluster weights HOT 2
- About computation time using ordLORgee from multgee package HOT 1
- [Proposal] Adding a (score) test for (non-)proportional odds
- Making the solvers compatible with MICE multiple imputation
- Making the solvers compatible with emmeans package
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