Understanding bias in clinical studies can help identify some of the reasons why we reach the wrong conclusions about the effects of interventions. Some of the biases we will be looking out for in 2010 include:

1. Publication bias: Positive findings are more likely to be published in medical journals than negative findings (Tamiflu). Media coverage of health issues often tends to be biased towards publication of stories which will grab headlines (swine flu).
2. Citation bias: This one is even more scary than publication bias: published articles tend to cite other articles that support their views rather than those articles which refute their views, and so have a negative impact on the scientific truth.
3. Selection bias: If the population studied is not representative of the population we want to draw conclusions about, then the study has a selection bias. The Framingham Heart study, for example, studied coronary heart disease in volunteers from a largely white, middle class population, and so we cannot necessarily draw conclusions about heart disease in blacks or other ethnic groups, or in populations in other countries.
4. Spectrum bias: A spectrum bias occurs when we overestimate how good a test is at picking up or excluding disease, because the test was evaluated in a biased sample of patients. The monofilament is a special tool, used to test whether diabetics have lost sensation in their feet. If it was only tested in people with mild sensory loss, then the monofilament may not be as good at picking up or excluding sensory loss, when it is used on people with severe sensory loss.
5. Information bias: If the way in which we measure an outcome or an exposure within a study is flawed, then we have an information bias. Data regarding carbon emissions rely on the integrity of countries and companies in their reporting. However, there is some evidence to suggest that such self-reporting (surprise surprise) is leading to gross under-reporting of emissions.
6. Recall bias: A special case of information bias is “recall bias”, where the ability of subjects to recall an exposure affects the results of a study. The difference between the amount of alcohol that people think that they drink and their actual alcohol consumption is a very good example.
7. Measurement bias: It is important to know whether the outcome measurement of interest is inaccurate. This can be due to inaccuracy in the measurement instrument or bias in the study participants expectations or responses. Often the way round the latter of these is to ensure adequate blinding.
8. Funding bias: an evaluation of solutions to sponsorship bias of more than 40 primary studies, and three recent systematic reviews and meta-analyses, have shown a clear association between pharmaceutical industry funding of clinical trials and pro-industry results. In 2010 elimination of such sponsorship bias should be a priority.

The final two biases are personal, in that when recognized, it may be possible to do something about them.

• Cognitive bias: is the tendency to make errors in judgment based on the way we think. In terms of diagnosis expertise is not a matter of acquiring an all-inclusive reasoning strategy, as several strategies may lead to the same diagnosis. These diagnoses are often correct; however, Clinicians tend to under-appreciate the likelihood their diagnoses are wrong and this tendency to overconfidence is related to both intrinsic and systemically reinforced factors.
• Reader Bias: Systematic errors of interpretation made during assumption by the user or reader of clinical information. These biases are due to the factors we put down to expertise: clinical experience, tradition, credentials, prejudice and human nature.

The last of these references by Richard Owen on reader bias is well worth a read, as it includes a whole host of further biases including: rivalry bias; personal habit bias, moral bias, clinical practice bias, do something bias. (The converse, do nothing bias, is common among academics), favoured design bias, prestigious journal bias, prominent author bias, famous institution bias (The converse: unrecognized institution bias), flashy title bias, friendship bias and my favourite “I am an epidemiologist" bias - Alternatively called bias bias - and is defined as repudiating a study containing any flaw in its design, analysis.

By Ami Banerjee and Carl Heneghan