What is Referral Bias?Referral bias (also called admission rate bias) is a specific type of selection bias. It happens when the likelihood of unusual outcomes increases as a result of referrals to a study. The sequence of referrals from primary care physicians, relatives and friends to specialized clinics and hospitals means that those patients are more likely to suffer from outcomes that are different from the general population. These outcomes are usually adverse, like a higher rate of febrile seizures among patients admitted to tertiary care clinics. However, some specific conditions, like anemia, may result in more favorable outcomes.
The major problem with this type of bias is that associations between risk factors and disease can’t accurately be calculated in an unrepresentative group taken from a larger population. Risk factors are likely to be overestimated and the results from any study analysis are not generalizable. Overestimating or underestimating disease effects, diagnostic performance or other factors can adversely affect patient care.
Adjusting for referral bias isn’t routinely done in studies, but it is possible. For example, Bayesian methods can be used for the adjustment. (see here for an example).
Test Referral bias
Test referral bias (also called verification bias) is when a “gold standard” procedure is used to verify the results of a test. Some patients are given the gold standard test, while others are not. The reason for this is that many gold standard tests are expensive, invasive or risky.
Ladapo. J. et.al. “Clinical Implications of Referral Bias in the Diagnostic Performance of Exercise Testing for Coronary Artery Disease”. J Am Heart Assoc. 2013 Dec; 2(6): e000505.
Salive, M. “R.B. in Tertiary Care: The Utility of Clinical Epidemiology.” Mayo Clinic Proceedings. August 1994, Volume 69, Issue 8, Pages 808–809.
If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.Comments? Need to post a correction? Please post on our Facebook page.