Prevalence-incidence (Neyman) bias

Exclusion of individuals with severe or mild disease resulting in a systematic error in the estimated association or effect of an exposure on an outcome.


Prevalence-incidence bias or Neyman’s bias occurs due to the timing of when cases are included in a research study. David Sackett wrote in 1979: “A late look at those exposed (or affected) early will miss fatal and other short episodes, plus mild or ‘silent’ cases and cases in which evidence of exposure disappears with disease onset.”

Excluding patients who have died will make the disease appear less severe. Excluding patients who have recovered will make the disease seem more severe. The Greater the time between exposure and investigation means more likelihood of individuals dying or recovering from the disease and therefore being excluded from the analysis, and this bias is more likely to impact long-lasting diseases than short-acting conditions.

Case-control studies are most susceptible to this bias, but it can also occur in cross-sectional studies and experimental or cohort studies.


A case-control study investigating pneumonia that only enrols cases and controls admitted to a hospital. Those with pneumonia who died prior to admission will not be included the sample. The selected sample will, therefore, include moderately severe cases, but not fatal cases.


We have not found formal investigations of the impact of prevalence-incidence bias.

Preventive steps

Careful selection of study samples is crucial for developing a good understanding of a disease and its causes. Using incident cases rather than prevalent cases can avoid prevalence-incidence (Neyman) bias.

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