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Bias and confounding


At the end of the lecture students should be able to:

  1. Explain the concept of bias and identify some potential sources of bias
  2. Define the concept of confounding and identify potential confounders
  3. Describe some potential solutions to confounding


Altman, D.G., 1991. Practical statistics for medical research, pp. 74-106. Chapman & Hall, London.

This chapter, on designing research, includes discussion of various types of bias and techniques for avoiding it.

Bland M., 1995. An Introduction to Medical Statistics, 2nd ed., pp. 5-25. Oxford Medical Publications, Oxford.

This chapter discusses many of the issues involving bias and confounding.

Campbell M.J. & D. Machin, 1993. Medical Statistics, A Commonsense Approach, 2nd ed., pp. 100-103. John Wiley & Sons, Chichester.

A brief outline of the use of multiple regression to deal with confounding variables.



In a study 20 'normal' people take the standard treatment for bad breath (drug A). 20 garlic eaters take drug B. The results of the study indicate that the people taking drug A have better breath. Is drug A better than drug B?


Not necessarily, our study is biased.

Bias can be defined as the distortion of the estimated effects caused by a systematic difference between the groups being compared.

Potential sources of bias include

Solutions to the problem of bias


If you don't know whether an effect is caused by the variable you are interested in (e.g. a drug or smoking) or by another variable (e.g. age or sex) then the other variable is called a confounder and it is said to cause confounding.

To be a confounder a variable must be associated both with the exposure you are interested in and the outcome you are analysing.

If we consider the example above which was biased because a confounder was not adjusted for:

Garlic eating was a confounder, which caused the results to be biased.

Controlling for potential confounders

Multivariate methods