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Overview
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Predictor variables in statistical analyses also are called independent variables, prognostic factors, regressors, and covariates. Prognostic factor analysis (PFA) is an analysis that attempts to assess the relative importance of several predictor variables simultaneously. Typically, a PFA uses one or more predictor variables that were not controlled by the investigator.

One reason for studying prognostic factors is to learn the relative importance of several variables that might affect, or be associated with, disease outcome. A second reason for studying prognostic factors is to improve the design of clinical trials. For example, if a prognostic factor is identified as strongly predictive of disease outcome, then investigators of future clinical trials with respect to that disease should consider using it as a stratifying variable.

Knowledge of prognostic factors can improve the ability to analyze randomized clinical trials.

## Objectives

- Differentiate between means and adjusted means.
- State how ANCOVA can reduce difficulties resulting from imbalance in prognostic factors.
- Recognize situations in which using a prognostic factor as a covariate is recommended. Recognize the difficulty presented with time-dependent covariates.
- Recognize interaction effects and differentiate between qualitative and quantitative interactions.
- Select the appropriate analysis of the data among ANCOVA, logistic regression and proportional hazards.
- Modify SAS programs to perform ANCOVA and logistic regression. Interpret the relevant portions of SAS output for these analyses.
- Identify 4 approaches to model building.
- Recognize the effects of incomplete data on model-building.
- Propose two methods of validating a model from a nonrandomized study.

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References:
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Piantadosi Steven. (2005) Prognostic Factor Analyses. In: Piantadosi Steven. *Clinical Trials: A Methodologic Perspective.* 2nd ed. Hobaken, NJ: John Wiley and Sons, Inc.