Following the recent launch of the Catalogue of Bias on the website of the Centre for Evidence-Based Medicine, Jeff Aronson continues, in the last of three blogs, his investigation into the word “bias”, and proposes a definition.
In the first blog in this series of three, I explored the etymology and usages of “bias”. In the second I analysed definitions of “bias” that have previously been proposed in statistical, epidemiological, and sociological texts. Recurrent themes that emerged were, in order of frequency: systematicity; truth (although the concept of probability is preferred); error; deviation or distortion; the elements affected; the direction of the effect. These features should be implied or incorporated within any definition of bias in evidence-based medicine.
Figure 2 shows how biases operate in relation to the observations in clinical studies.
Figure 2. How biases operate in relation to observations in clinical studies
This analysis shows how biases can affect the interpretation of results from studies, whether the results are positive, negative, or neutral. It stresses that biases can alter the apparent association between an exposure to an intervention (e.g. a medication) and a measured outcome (e.g. a biomarker) or can alter the apparent nature of an association between two measurements (e.g. two biomarkers) and that in any study multiple biases may be present and have different effects.
Sources of bias
Finally, I return to the definitions included in the OED, as cited in my first blog in this series. The dictionary gives two definitions of “bias” in its transferred usages, which are those with which we are concerned:
1. An inclination, leaning, tendency, bent; a preponderating disposition or propensity; predisposition towards; predilection; prejudice.
2. Statistics. A systematic distortion of an expected statistical result due to a factor not allowed for in its derivation; also, a tendency to produce such distortion.
That preconceptions and prejudices could act as biases in clinical medicine, highlighted in the first of these two definitions, was perhaps realised before it was appreciated that other factors can do so as well. This is reflected in Alvin Feinstein’s definition of “bias” in his textbook Clinical Judgment (1967): “The preconception that a clinician brings to his observations when he expects each instance of a disease to behave in a ‘typical’ way.” It is also seen in a reference in Hammersley & Gomm (1997) to “a tendency on the part of researchers to collect data, and/or to interpret and present them, in such a way as to favour false results that are in line with their prejudgments and political or practical commitment.”
This reminds us that biases can arise from different causes, as in the following examples:
A proposed definition of “bias”
Based on these analyses, I propose the following definition of “bias”, relevant to the Catalogue of Bias, here couched as it would appear in a standard dictionary.
bias, n. /ˈbʌɪəs/ A systematic distortion, due to a design problem, an interfering factor, or a judgement, that can affect the conception, design, or conduct of a study, or the collection, analysis, interpretation, presentation, or discussion of outcome data, causing erroneous overestimation or underestimation of the probable size of an effect or association [ancient Greek ἐπικάρσιος, crosswise, esp. at right angles, via French biais]
This definition defines the count noun “bias”, in other words, any example of the non-count noun “bias”. The non-count noun could be defined as a tendency to produce biases. The definition includes the important features of previous definitions, enumerated in this and the previous blog, recognises that different biases can arise from different sources, and acknowledges that bias can result in overestimation or underestimation of outcomes in studies of the effects of interventions or associations between different measurements.
Defining types of bias
The catalogue of bias is in progress. It will grow as new entries are added. The current list of potential entries runs to about 250 varieties, of greater or lesser importance. Probably not all of them will end up being covered in the catalogue. However, each one that does will be defined.
In crafting the definitions we shall recognize that it is conventional to name each bias after the problem, factor, or judgement that produces it, which is not itself a bias. For example, reporting is not a bias. However, if a bias – a systematic distortion – arises because of a problem with reporting, that would be called a reporting bias. So “reporting bias” could be defined as “a systematic distortion that arises from a problem with the way in which the results of a study are reported”. The many different types of reporting bias would be defined to reflect this.
Jeffrey Aronson is a clinical pharmacologist and Fellow of the Centre for Evidence-Based Medicine in Oxford’s Nuffield Department of Primary Care Health Sciences. He is also president emeritus of the British Pharmacological Society.
Competing interests: None declared.
Other articles in this series:
A Word About Evidence: 5. Bias—previous definitions – Catalog of Bias
A Word About Evidence: 4. Bias—etymology and usage – Catalog of Bias