Hi Prof Harell,

I had not attended your course (really wish I could attend one in the future), and I hope it is okay for me to post a question here.

I refer to the section 4.12 of RMS book, the different models:

- Developing Predictive Models
- Developing Models for Effect Estimation
- Developing Models for Hypothesis Testing

I noted from various posts in datamethods [1][2] that univariable analysis is not useful and using it to pre-screen variables for inclusion in the multivariable analysis is not appropriate.

My questions are:

- Should this practice be avoided for all 3 types of models? Or can it be applied for some?
- In the question posted here [1], I understand that the variables are solely chosen due to significant p-values (in which the cut-off of 0.30 is arbitrary). If say, we generate table 1 or univariable analysis, to give some idea of what should be included or excluded, and
**in the multivariable, we include all statistically significant ones as well as others that are considered clinically important** (either through previous studies, or clinical practice) - would this be okay?

Additionally, how are the different models defined in the book? I read *Shmueli, G. (2010). To explain or to predict? Statistical Science, 25, 289–310*, but I wonder if there’s any distinction in the definition of these. In summary:

- Explanatory modelling:

*Used for testing causal hypotheses – implicates cause and effect

*Here, it is important to identify the roles of each variable on the specific causal pathway for the study question (confounder, collider, mediator or effect modifiers).
- Predictive modelling:

*Used for the purpose of predicting new or future observations. The goal is to predict

the output value (Y) for new observations given their input values (X).
- Descriptive modelling:

*Used for summarizing or representing the data structure in a compact manner (or parsimoniously).

*In other words, it is used for capturing the association between the dependent and independent variables rather than for causal inference or for prediction.

I hope to learn more from you. Thanks!

Regards,

Hanis