I was referring to no workaround for a contrast of survival probabilities at a fixed time.
For lrm I did not implement ‘no intercept’ models.
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Hi Prof Harrell,
I want to confirm my understanding about RMS 4.12.1 Developing Predictive Models.
In this section, you said that we should use a a single p value to test the entire/non-linear/interaction.
For example, if a single predictor has a > 0.05 P value, we should NOT delete it, we need to check the global P value. For an interaction term, we should observe the global interaction P value, if the global P < 0.05, we should NOT delete any interaction term, even there is an interaction term with a >0.05 P value.
（Prerequisites: variables and interactions are based on pre-made assumptions, that is, if there is a physiological mechanism for the interaction between two variables, an interaction should be included）
I have marked my understanding in bold at the end of your notes. In summary, we should not exclude any interaction or predictor just because its p>0.05, we should focus on the global p-value
8 Can do highly structured testing to simplify “initial” model
Test entire group of predictors with a single -value (⇒ TOTAL P value in ANOVA test)
Make each continuous predictor have same number of knots, and select the number that optimizes AIC
Test the combined effects of all nonlinear terms with a single -value. (⇒ TOTAL NONLINEAR P value in ANOVA test)
- Check additivity assumptions by testing pre-specified interaction terms. Use a global test and either keep all or delete all interactions (⇒ TOTAL INTERACTION P value in ANOVA test)
Is my understanding correct? Thank you very much.
This topic is for rms software discussions. Please use the navigation instructions at the top. For your topic post at datamethods.org/rms4 and I’ll remove this.