Ascertainment bias

Systematic differences in the identification of individuals included in a study or distortion in the collection of data in a study.


Ascertainment bias arises when data for a study or an analysis are collected (or surveyed, screened, or recorded) such that some members of the target population are less likely to be included in the final results than others. The resulting study sample becomes biased, as it is systematically different from the target population. Ascertainment bias is related to sampling bias, selection bias, detection bias, and observer bias.

Ascertainment bias can happen when there is more intense surveillance or screening for outcomes among exposed individuals than among unexposed individuals, or differential recording of outcomes.

Ascertainment bias can occur in screening, where take-up can be influenced by factors such as cultural differences. It can occur in case-control studies in the initial identification of cases and controls, which can be skewed by relevant exposures, leading to biased estimates of associations.

In a clinical trial, if allocation concealment and blinding are lacking, then outcome ascertainment can be influenced by knowledge of the allocation.


In a Cochrane systematic review “Helmets for preventing head and facial injuries in bicyclists”, the authors examined information from five case-control studies. These studies obtained information from cyclists who had crashed or fallen. Cases were cyclists suffering head, or facial injuries and controls were cyclists suffering other injuries. In this situation, ascertainment bias could occur if information on helmet wearing was obtained differently for cases and controls.

In describing how to avoid ascertainment bias, the authors write:“In order to study facial injuries, cases should be limited to serious injuries (lacerations and fractures) that would result in an emergency department visit whether or not a head injury was also present. People with minor facial injuries may be identified because bicyclists seek care for head injuries.” This example illustrates how biased screening for the outcome can lead to ascertainment bias.

A database analysis found that rates of Alzheimer’s disease were lower among higher users of statins; a letter commenting on this study suggested that some or all of this observed association may be due to ascertainment bias: Alzheimer’s diagnosis requires contact with a doctor and statins use might be associated with frequency of visiting the doctor.


We have not identified studies reporting the empirical impact of ascertainment bias.

Preventive steps

Appropriate inclusion of cases and controls within case-control studies is essential to avoid ascertainment bias. In cohort studies, knowledge of exposures of interest should be kept separate from screening, identification and recording of relevant outcomes of interest. In clinical trials, allocation concealment and blinding of all study staff are critical to maintaining a low risk of bias throughout the trial.

Leave a Reply

Your email address will not be published. Required fields are marked *