Statistics How To

Neyman Bias (Prevalence-Incidence)

Types of Bias

What is Neyman Bias?

Neyman Bias is a selection bias where the very sick or very well (or both) are erroneously excluded from a study. The bias (“error”) in your results can be skewed in two directions:

  • Excluding patients who have died will make conditions look less severe.
  • Excluding patients who have recovered will make conditions look more severe.

You may not know which groups (improved/died) you are excluding, making it impossible to adjust for any bias in your results.

This type of bias often happens when a significant amount of time has passed between exposure and investigation; patients who have died or recovered will be erroneously excluded from any analysis, skewing the results towards individuals who are more “average.” For example, a study of patients hospitalized for the flu will miss those patients who have died, and those who have been discharged after recovery. Neyman bias is less of a problem with acute, short-lived cases than with long-term diseases like HIV or tuberculosis.

This type of bias is also called prevalence-incidence bias from the fact that it’s preferable to use incident cases instead of prevalent cases. Incident cases are newer cases — like first time admissions. Prevalent cases are pre-existing cases, which are usually sicker with more progressed disease than incident cases. Combining prevalent and incident cases can actually make prevalent-incidence bias worse, obscuring the true relationship between your study variables (i.e. the variables in your experiment or study) (Magnus, 2008).

Avoiding Neyman Bias

Careful selection of study type can help to lessen the effects from this bias, because some studies are more susceptible to prevalence-incidence bias than others. For example, this bias usually happens in case-control and cross-sectional research — although it sometimes occurs in experimental or cohort studies. On the other hand, a carefully designed follow-up study can help to lessen the effects from this bias.

Streiner and Norma (2009) offer the following example: the long term outlook for schizophrenia patients is poor, but is mostly based on a natural history study of patients hospitalized for the disease. This misses discharged patients living productive lives. Follow-up studies of patients admitted for the first time reveal a somewhat optimistic picture– that between 60% and 80% of patients are actually living productive lives in the community.

References:
Magnus, M. “Essentials of Infectious Disease Epidemiology.” 2008, Jones & Bartlett LEarning.
Streiner, D. & Norman, G. “PDQ Epidemiology.” 2009, Medical.

------------------------------------------------------------------------------

Confused and have questions? Head over to Chegg and use code “CS5OFFBTS18” (exp. 11/30/2018) to get $5 off your first month of Chegg Study, so you can understand any concept by asking a subject expert and getting an in-depth explanation online 24/7.

Comments? Need to post a correction? Please post a comment on our Facebook page.

Check out our updated Privacy policy and Cookie Policy