Hi,

Does anyone have good resources on different statistical models, i.e. predictive model vs explanatory model vs descriptive model?

Thanks!

Hi,

Does anyone have good resources on different statistical models, i.e. predictive model vs explanatory model vs descriptive model?

Thanks!

The introductory chapter of RMS covers the many similarities between models for estimation vs. prediction vs. hypothesis testing. I’m sure that others can tell us about more in-depth resources.

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I remember this Bradley Efron paper from 2020 was helpful in clarifying the subtle distinctions:

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Thank you, Prof Frank Harell.

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

Should univariable pre-screening be avoided for all 3 types of models?

In particular, performing a table 1 or univariable analysis, and selecting those that are significant; but **at the same time, include those non-significant ones that may be considered clinically important**.

I also refer to other questions here. My impression is that *exclusively* selecting variables that are significant, should not be done. But, if it’s coupled with variables that are not significant but are clinically important (based on prior knowledge), then is it okay?

Since that is directly related to RMS chapter 4 please repost that to https://discourse.datamethods.org/t/rms-multivariable-modeling-strategies and I’ll remove it here. Thanks.