Prediction Models
13 important questions on Prediction Models
What is the problem with both selection methods?
When are variables kept or deleted in fw or bw selection?
-->if Pentry DIFFP removal then FW and BW selections lead to DIFF models
Events per Variable Problem
How to solve that?
10/15 people *each prediction variable(dof)
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Quality of Prediction model, how to assess that?
•Explained variance(R2, values 0-100%, the higher the better)
•Calibration(=agreement between predicted probabilities of outcome and observed outcome)
•Discrimination(= how well the model can distinguish between the people who have the outcome and those who don't)
What types of calibration are there?
Calibration curve - predicted probabilities against observed probabilities. Ideally in a linear line. scale is 0,1 in both x and y
Hosmer and Lemeshow- agreement predicted and expected numbers
What is the linear predictor?
Calibration slope and what can you use it for?
It is used to correct overfitting(optimism) of the regression coefficient.
What is the shrinkage factor?
Why do you need to adjust the regression coefficient?
Internal validation of Prediction model
- Internal validation is important to obtain a honest estimate of performance for patients that are similar to those in the development sample
-Methods to internally validate: bootstrapping-estimation optimism-adjustment slope accordingly.
External Validation of Prediction model
External validity depends on quality of internal validity and different distribution of predictors/outcome values between development and validation samples
3 steps for external validation of Prediction models?
2.Calculation new Linear predictor (LP)= multiply regression coeff with values predictors in external dataset
3. Quality model evaluation (discrimination, calibration)
What is the wald value?
The question on the page originate from the summary of the following study material:
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