We’re fitting a model that tests the efficacy of a treatment (e.g., a pharmaceutical product). The DV is Recovered, a binary variable (0/1), defined as numeric nominal. The research question is whether the treatment affects the expression of a precondition and thus improves recovery. We expect that people with certain level of the precondition will react differently to the treatment, i.e., an interaction effect.
We fit a generalized regression model with binomial distribution. The predictors are 1) whether the person received treatment (Treatment; binary), 2) the variable whose expression should be affected by the treatment (Precondition, continuous), 34) two continuous control variables (Period and Q), as well as an 5) interaction term between Treatment and Precondition.
The output is below. Thanks for your help!
Generalized Regression for Recovered = 0
Model Comparison
Show  Response Distribution  Estimation Method  Validation Method  Nonzero Parameters  AICc  BIC  Generalized RSquare  
[x]  Binomial  Logistic Regression  None  5  3677.7423  3708.3526  0.1934902 
Model Launch
Binomial
Lasso [ ] Adaptive
AICc

 

 
[ ] Early Stopping


Logistic Regression
Model Summary
Response  Recovered  
Distribution  Binomial  
Estimation Method  Logistic Regression  
Validation Method  None  
Probability Model Link  Logit 
Measure 
 
Number of rows  6128  
Sum of Frequencies  3380  
LogLikelihood  1833.8622  
Number of Parameters  5  
BIC  3708.3526  
AICc  3677.7423  
Generalized RSquare  0.1934902 
Parameter Estimates for Original Predictors
Term  Estimate  Std Error  Wald ChiSquare  Prob > ChiSquare  Lower 95%  Upper 95%  
Intercept  0.8755145  0.1101971  63.122825  <.0001*  0.6595322  1.0914968  
Treatment[01]  0.0643045  0.0696953  0.8512868  0.3562  0.072296  0.2009047  
Period  0.1015936  0.0130748  60.375781  <.0001*  0.0759675  0.1272197  
Q  0.0012641  0.0004935  6.5606499  0.0104*  0.0002968  0.0022314  
Precondition  0.000095  0.0001314  0.522411  0.4698  0.000352  0.0001626  
(Precondition226.24)*Treatment[01]  0.0009875  0.0003172  9.6914587  0.0019*  0.0003658  0.0016093 
(All the variable names are aliases due to confidentiality requirements).
In order to have the model for Treatment = 1 instead of 0, turn on the Value Ordering property for the Treatment column. Move the 1 level up, so it is on the top of the list and rerun your model.
You can use the same approach to predict the probability of a 1 for Recovered when using a General Linear Model.
Thanks for the response. We did a broad search before posting the question, including in this forum, but we may have missed something. Could you point us to 1) how to change the predicted DV level and how to interpret results 2) & 3)? The numbers refer to the original questions.
Thank you! We're making progress here.
In order to have the model for Treatment = 1 instead of 0, turn on the Value Ordering property for the Treatment column. Move the 1 level up, so it is on the top of the list and rerun your model.
You can use the same approach to predict the probability of a 1 for Recovered when using a General Linear Model.
@G_Mand @Dan_Obermiller: Through your answers, we managed to complete the interpretation, and proceed with the research. Thank you!