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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
        

Selection and Stratification

Predictions from regression models may help to select patients for randomized clinical trials through exclusion of patients at low or at high risk of an adverse outcome.

QUESTION 2.2

Suppose that a drug with known substantial side effects is studied in a placebo-controlled trial. Which patients might be excluded?

Selection AThose at low risk of an unfavorable outcome without treatment
Selection BThose at intermediate risk of an unfavorable outcome without treatment


Keep in mind that balance is desired in prognosis when treatment groups are being compared. This can be ensured by stratification. Stratification may be based on single factors, e.g. some important risk factors. Statistical models may also play a role here by providing predictions that are used for stratification. For example, predictions may be classified into several categories, with a balance of randomization within each category ("stratified randomization").

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