# 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

- 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.