Deconfounding confounding part 1: traditional explanations

John R Attia, Michael P Jones and Alexis Hure
Med J Aust 2017; 206 (6): . || doi: 10.5694/mja16.00491
Published online: 3 April 2017

The first article of this series1 presented a framework to assist in judging the presence of bias:

  • 1 Centre for Clinical Epidemiology and Biostatistics, University of Newcastle, Newcastle, NSW
  • 2 John Hunter Hospital, Newcastle, NSW
  • 3 Macquarie University, Sydney, NSW


Competing interests:

No relevant disclosures.

  • 1. Attia JR, Jones MP, Suthers B. Aiming for the truth: understanding the difference between validity and precision. Med J Aust 2016; 205: 392-394. <MJA full text>
  • 2. Lipsitch M, Tchetgen Tchetgen E, Cohen T. Negative controls: a tool for detecting confounding and bias in observational studies. Epidemiology 2010; 21: 383-388.
  • 3. Rossouw JE, Anderson GL, Prentice RL, et al. Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the Women’s Health Initiative randomized controlled trial. JAMA 2002; 288: 321-333.
  • 4. Attia J, Page J. A graphic framework for teaching critical appraisal of randomised controlled trials. Evid Based Med 2001; 6: 68-69.


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