It would be hard to say that the college love this, but it has certainly showed up in the exams of late: Question 26 from the first paper of 2014 and Question 5 from the second paper of 2013 asked the candidates to define bias and discuss strategies to minimise it.

Bias in medical research

There is a good article on bias in research from the journal Radiology.

  • Bias is a systematic error which distors study findings
  • It is caused by flaws in study design, data collection or analysis
  • It is not altered by sample size (increasing sample size only decreases random variations and the influence of chance)
  • It can creep in at any stage in research, from the literature search to publishing of the results.

Though many types of bias have been described, there are some commonly observed forms which one might want to be familiar with. The Cochrane Handbook is the best source for this.

 

Selection bias:

The selection of specific patients which results in a sample group which is not random, and which is not representative of a population. This can be avoided by randomising selection.

 

Detection bias:

The observations in the treatment group are pursued more diligently than in the control group. This can be avoided by blinding.

 

Observer bias:

The observer makes subjective decisions about the outcome. This can be avoided by blinding the observer, and making the outcome measures objective (eg. measuring mortality, rather than than measuring the warm fluffy sensation of internal wellbeing).

 

Recall bias:

The patients know whether they were allocated to the treatment group or the control group, and this discolours their reporting of their symptoms. This can be avoided by blinding the patients.

 

Response bias:

The patients enrol themselves in the trial, which results in a non-representative sample. This can be avoided by randomly sampling the population.

 

Publication bias:

Publication bias was the topic of Question 5 from the second paper of 2013.

  • Publication bias is the influence of study results on the likelihood of their publication. Nobody likes to publish negative data, even though it is as valuable as positive data. This in turn influences the meta-analysis of all data (which cannot be accurate if the only published data is positive).
  • A funnel plot can be used to identify publication bias.
  • A meta-analysis can be invalidated if publication bias has influenced the included studies.
  • Publication bias leads to the selection of mostly positive (or mostly negative) studies, which in turn leads to positive meta-analysis results. Studies with the opposite effect may not have been selected for publication, and may not be available to the meta-analysis authors.
  • Meta-analysis authors may develop an inherent publication bias by only using English-language studies, only free-access articles, or only focusing their search within a narrow field of research.
  • Publication bias can be overcome by contacting relevant authors and requesting unpublished trial data, by searching for publications in all languages, and by searching broadly in multiple cross-specialty databases.

Regression to mean

When random chance influences cause extreme variations in an initial measurement, the next measurement (unaffected by this random influence) will be closer to the mean, thus giving the apparance of a treatment effect. This is avoided by using control groups.

 

Hawthorne effect

The process of follow-up and careful scrutiny influences the patient outcome. Patients who receive more attention may do better than patients who are ignored. The way to avoid this is to mask the intention of the study from the patients and observers.

 

Treatment selection bias

The effects of a treatment are determined by confounders (such as differences in the patients or other co-interventions) rather than than the treatment itself.

 

References

Higgins, Julian PT, and Sally Green, eds. Cochrane handbook for systematic reviews of interventions. Vol. 5. Chichester: Wiley-Blackwell, 2008.