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




Chapter 8: Statistical Models for Prognostication: Ingredients of Statistical Models
        

Predictions

Statistical models provide predictions that represent the expected value of the outcome. For continuous variables, the expected value is the mean; for dichotomous or categorical variables the expected values are probabilities of each outcome value. For example, predictions for males and females of a pain score are simply the mean according to each group; predictions of mortality for these groups are probabilities.

When continuous or multiple predictors are considered, the predictions can no longer be directly compared to empirical estimates. The number of observations will be too low in cells formed by patients with similar values of a continuous variable or by the combination of multiple predictors. Residuals may then be studied, i.e. the difference between observed and predicted values.

For survival data, predictions can be made in two ways.

  • First, the probability of survival may be considered at a certain point in time, for example 2-year survival according to gender.
  • Second, the predicted survival time may be considered, for example median survival according to gender.

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