7. Survival Analysis

  1. Stephane Heritier1,
  2. Eva Cantoni2,
  3. Samuel Copt3 and
  4. Maria-Pia Victoria-Feser4

Published Online: 1 DEC 2010

DOI: 10.1002/9780470740538.ch7

Robust Methods in Biostatistics

Robust Methods in Biostatistics

How to Cite

Heritier, S., Cantoni, E., Copt, S. and Victoria-Feser, M.-P. (2009) Survival Analysis, in Robust Methods in Biostatistics, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470740538.ch7

Author Information

  1. 1

    The George Institute for International Health, University of Sydney, Australia

  2. 2

    Department of Econometrics, University of Geneva, Switzerland

  3. 3

    Merck Serono International, Geneva, Switzerland

  4. 4

    HEC Section, University of Geneva, Switzerland

Publication History

  1. Published Online: 1 DEC 2010
  2. Published Print: 17 APR 2009

ISBN Information

Print ISBN: 9780470027264

Online ISBN: 9780470740538



  • survival analysis;
  • survival analysis, central to biostatistics and modeling data - part of work carried out by statisticians with clinicians and medical researchers;
  • Cox model and regression quantiles, an innovative technique;
  • Cox model - the partial likelihood approach;
  • Sandwich formula for asymptotic variance;
  • robust estimation and inference in Cox model;
  • ARE works in practice and myeloma data;
  • robust inference and its current limitations – performance of robust Wald test;
  • complete analysis based on a benchmark in survival analysis - veteran's administration lung cancer data;
  • structural misspecifications - robust techniques dealing with distributional robustness


This chapter contains sections titled:

  • Introduction

  • The Cox Model

  • Robust Estimation and Inference in the Cox Model

  • The Veteran's Administration Lung Cancer Data

  • Structural Misspecifications

  • Censored Regression Quantiles