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Statistical Models for Prognostication
Author Bio
Introduction
Currently selected section: Predictions: Statistical Models
Insight: Statistical Models
Ingredients: Statistical Models
Theoretical Aspects
Central Concepts
Regression Models
Problems: Regression
Practical Advice
Example 1
Example 2




Chapter 8: Statistical Models for Prognostication: Predictions from Statistical Models
        

You Answered:

Selection AAn unadjusted analysis of the treatment effect

INCORRECT

The correct answer is (b).

An adjusted analysis provides a higher power for detection of any treatment effects compared to an unadjusted (or "crude" analysis). With linear models, predictor variables explain variability in the outcome, which leads to smaller variability in the adjusted estimate of the treatment effect. With nonlinear models, such as logistic regression or Cox regression, the variability in the adjusted estimate of the treatment effect is not smaller. However, the adjusted estimate will be more extreme (further away from the neutral value), which causes a net increase in power to detect a treatment effect (Robinson and Jewell, 1991). So, in either analysis, the adjusted model will increase the likelihood that the researcher can detect a true difference between groups.


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