Hi JMP Community,
I routinely analyze binary outcomes such as Response / Non-Response at a single time point in my research using the Nominal Logistic Platform. Now, I need to expand my analysis to incorporate the factor of Time in my model, and I realized that I am unsure about the best approach to do so.
Specifically, in the mock data file attached to this post, I need to evaluate the effect of Time, VAR1, and VAR1 * Time for each level of VAR2 and VAR3 (see plot below), considering possible interactions between VAR1 and VAR2, and VAR1 and VAR3.
I am familiar with the Repeated Measure Least Square Means model for continuous variables, but I am struggling with the implementation of a similar approach for a Categorical outcome.
JMP 16.1, Windows
All inputs are welcome.
Best,
TS
Hi JMP Community,
After doing some reading, it seems that the Generalized Linear Model with a Binomial distribution is a reasonable approach to the analysis of longitudinal binary outcomes. However, I have a few questions regarding the details of the platform report:
- In the Goodness of Fit: what is the correct interpretation when the Pearson Statistics is > 0.05 and the Deviance Statistics is < 0.05?
- For the Studentized Deviance: what are the expectations for this distribution for a Binomial distribution?
Thank you for your help.
Best,
TS
