Parametric Methods for Regression Model Fitting and Identification of Prognostic Factors

  1. Elisa T. Lee and
  2. John Wenyu Wang

Published Online: 30 JUN 2003

DOI: 10.1002/0471458546.ch11

Statistical Methods for Survival Data Analysis, Third Edition

Statistical Methods for Survival Data Analysis, Third Edition

How to Cite

Lee, E. T. and Wang, J. W. (2003) Parametric Methods for Regression Model Fitting and Identification of Prognostic Factors, in Statistical Methods for Survival Data Analysis, Third Edition, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/0471458546.ch11

Author Information

  1. Department of Biostatistics and Epidemiology and Center for American Indian Health Research, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA

Publication History

  1. Published Online: 30 JUN 2003
  2. Published Print: 4 APR 2003

ISBN Information

Print ISBN: 9780471369974

Online ISBN: 9780471458548

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Keywords:

  • regression model fitting;
  • parametric methods;
  • prognostic factor identification;
  • preliminary examination;
  • data;
  • parametric regression models;
  • general structure;
  • asymptotic likelihood inference;
  • exponential regression model;
  • Weibull regression model;
  • lognormal regression model;
  • extended generalized gamma regression model;
  • log-logistic regression model;
  • model selection methods;
  • other parametrical regression models

Summary

In this chapter we focus on parametric regression models. We begin with a brief discussion of the possible types of response and prognostic variables and things that can be done in a preliminary screening before a formal regression analysis. Next, we introduce the general structure of a commonly used parametric regression model, the accelerated failure time (AFT) model. Then, we cover several special cases of AFT models. The SAS and BMDP code that can be used to fit the models are given at the end of the examples section. Readers may find these codes helpful. We introduce two other models and then discuss the model selection methods and goodness of fit tests. The chapter concludes with a problem solving section.