Bias and Confounding

Describe bias, types of error, confounding factors and sample size calculations, and the factors that influence them

Bias

Bias is a systematic deviation from truth, and causes a study to lack internal validity.

In a research study, an observed difference between groups may be due to:

  • A true difference between groups
  • An error
    Error can be due to:
    • Normal random variation, i.e. chance
    • A systematic difference, i.e. bias
      Unlike error due to chance, the effect of bias cannot be reduced by increasing the sample size.

Types of Bias

Type of bias Description Prevention
Selection Where subject allocation results in treatment groups that are systematically different, apart from in the intervention being studied Randomisation
Detection Where measurements are taken differently between treatment groups Blinding
Observer Where the data collector is able to be subjective about the outcome Blinding, Hard outcomes
Publication When negative studies are less likely to be submitted or published than positive ones Clinical trial registries
Recall Altered reporting of symptoms by patients depending on which group they have been allocated to Blinding
Response When patients who enroll for a trial differ from the population, limiting generalisability Random sampling
Hawthorne effect When the process of actually doing the study improves the outcome Control group, masking study intent from patients and observers

Confounder

A confounder is "a variable that, if removed, results in a change in the outcome variable by a clinically significant amount." It is a type of bias which will result in a distortion of the measured effect.

A confounding factor must be:

  • Associated with the exposure but not a consequence of it
    • A confounding factor cannot be on the causal pathway between exposure and disease
    • It must be present unevenly between groups to cause distortion of the measured effect
  • An independent predictor of outcome
    The confounding factor must also be a risk factor for the disease, but independently from exposure.

Controlling for confounding

By Design

  • Randomisation
    All confounders (known and unknown) are distributed evenly between groups.
  • Restriction
    Restricts participants to remove confounders.
    • Results in reduced generalisablility and does not control all factors
  • Matching
    Pairing of similar subjects between groups.
    • May introduce additional confounding, and matching by multiple characteristics is difficult

By Analysis

  • Standardisation
    Adjust for differences by transforming data.
  • Stratification
    Analyse the data in subgroups for each potential confounding factor.

References

  1. Sackett, D. L. (1979). Bias in analytic research. Journal of Chronic Diseases 32 (1–2): 51–63.
  2. PS Myles, T Gin. Statistical methods for anaesthesia and intensive care. 1st ed. Oxford: Butterworth-Heinemann, 2001.
  3. Stats notes from my MPh (University of Sydney). Probably a Timothy Schlub lecture, circa 2014.
Last updated 2017-09-07

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