cancel
Showing results for 
Show  only  | Search instead for 
Did you mean: 
JMP is taking Discovery online, April 16 and 18. Register today and join us for interactive sessions featuring popular presentation topics, networking, and discussions with the experts.
Choose Language Hide Translation Bar
Sandeep123
Level III

how do I adjust for covariates

I have a data set with two cohorts . They have outcome differences ( Length of stay, continious variables) on univariate analysis. They are however not similiar in baseline characterstics ( have different severity of illness ( continious), age ( continious) and diagnosis ( categorial variables ).

 

How do I adjust the outcome variable based on age, disease severity and diagnosis ?

14 REPLIES 14
Sandeep123
Level III

Re: how do I adjust for covariates

tharesidual plot.PNG

Sandeep123
Level III

Re: how do I adjust for covariates

Thank you. I agree that it is best to have discussions in the public forum for other researchers who may benefit. Still felt uncomfortable sharing real data on the Internet.

It is interesting and sort of eye-opening for me to realise how much difference in results can there be by just using a different model to compare. Even with generalised linear model, it makes a difference if I choose normal distribution versus Poission distribution. This leads to element of model dependency and researcher discretion.

Even though the difference is significant, the parameters estimate are very low. So cases have a LOS 0.05 days more than the controls..that is not clinically a relevant difference

What bothered me the most is that with this model even sex had a significant impact on length of stay with a P value of< 0.001 and a parameter estimate of 0.05. Just on bivariate comparison of sex with length of stay,
There is no difference (and there is no clinical reason why there should be a difference)

I would need to include a statement on justification of this model compared to a standard least square regression. And for poisson distribution.

Regarding the lack of fit, I would think that is expected because there would be a lot more variables that may confound the two groups which we have not measured or cannot be measured.

Another concern is that the variables in the model are not entirely independent. Severity of illness scores already include diagnostic categories in them.

Re: how do I adjust for covariates

A proper model makes all the difference! The choice is at the discretion of the researcher but it should be guided by prior knowledge, theoretical foundations, objective comparison with alternative models, and evaluation of the model assumptions.

 

The parameter estimates are used in a linear combination to determine the distribution parameters. They can be difficult to interpret on their own. You might click the red triangle at the top and select Profilers > Profiler. You can change factor levels and see the change in the predicted response.

 

I have another idea about the lack of fit. I do not see the test for over-dispersion, which is a common occurance. The Poisson distribution has a single parameter. It is the mean and the variance of the distribution. Real distributions of counts often exhibit a variance that is greater than the mean. You should find check boxes when you select Generalized Linear Models for the over-dispersion tests and intervals and for the Firth bias-adjusted estimates. I recommend selecting both of these options.

 

Otherwise, you might consider adding terms for potential interaction and non-linear effects.

Sandeep123
Level III

Re: how do I adjust for covariates

I think that explains it. I am satisfied with the results
One last question hopefully
How does this strategy compared to doing a propensity matching of cases and controls.
I was reading on other discussion posts that propensity matching is not possible in JMP and I have to do it on SPSS or R... Is that correct?

Re: how do I adjust for covariates

I don't know about propensity matching in JMP or other software. Perhaps another community member can help.