Can I run Mixed-effect-logistic regression with only one single outcome variable which is time varying covariate?there is no confounder for the study.
or should i have more than one independent variable?
Is there any article related to Mixed-effect-logistic regression with time varying covariate to send me?
I want to write the stat and I need some.
Thanks
I assume this question is a modification or elaboration of the question posted here:
- https://www.researchgate.net/post/Can_I_run_logistic_regression_with_only_one_single_outcome_variable_which_is_time_varying_covariate
If so, please delete the first question.
Second, a time-dependent covariate is an explanatory variable that can change over time, while an outcome variable is a dependent variable. So it is not possible for your outcome variable to be a time-dependent covariate. It would help to clarify things if you posted a small dataset that shows what your data look like. CSV format would be good, as it would be accessible to everyone regardless of what statistical software they use. What software do you use, by the way?
Thank you so much for your answer, sorry it is a typo error,
I have two dependent outcome variables
one outcome is: binary
another outcome is : the repeated time measure
I have only one independent variable which is the time-varying covariate
I am looking for an article for both
1- logistic regression with time-varying covariate, here the outcome is binary
2- Mixed-effect logistic regression with only one outcome varying covariate, here the outcome is a repeated measure
i don't have any together independent variable
While there is some lack of clarity I can say use proc glimmix in SAS and put time in your model as a predictor variable to do a longitudinal analysis that models the trajectory of your outcome variable. Standard reference is laird and Ware. Bruce can probably elaborate.
Searching for info on "time-dependent covariates" will likely turn up a lot of resources on Cox regression. You might try searching for info on how to interpret level 1 vs level 2 coefficients in multilevel models. Your time-dependent covariate is a level 1 variable in that way of thinking about things.
I am still confused about the setup. I think you have two (potentially related) outcome variables which are measured on multiple occasions (repeated measures over time). One of these is continuous , the other is binary. If so you can use a mixed model and I know mlwin software has this capacity. It does not matter about only having one explanatory predictor for each response, if that is what you have.
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