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