Consider a case/control study designed to investigate a possible assoc
iation between exposure to a putative risk factor and development of a
particular disease. Let E denote the information required to specify
a subject's exposure to the risk factor. We examine the effect that er
rors in the recorded values of E (which we denote by E) have on infer
ences of an association between disease and the risk factor. We concen
trate on situations where the errors in recorded exposure are such tha
t exposure is underestimated for controls and overestimated for cases.
This phenomenon is referred to as differential recall bias and may le
ad to spurious inferences of an association between exposure and disea
se. We describe how the standard inferential techniques used in the an
alysis of data from case/control studies may be adjusted to take accou
nt of specified mechanisms whereby E is distorted to produce E Such a
djustments may be used to determine the sensitivity of an analysis to
the phenomenon of differential recall bias and to quantify the extent
of such bias that would be required to overturn the conclusions of the
analysis. There remains the matter of judging whether a given distort
ion mechanism is reasonable in a particular context. This emphasizes t
he need for investigators to take account of differential recall bias
in validation studies of exposure assessment techniques. The methodolo
gy developed here is applied to a recent major study investigating the
possible association between lung cancer and exposure to environmenta
l tobacco smoke. The log-odds ratio of 0.23 based on recorded exposure
differs significantly from 0 (p < 0.02). However, the association is
rendered non-significant by a very modest degree of differential recal
l bias. For example, if 3.8 per cent of exposed controls report no exp
osure, 3.8 per cent of unexposed cases report exposure, and all other
subjects report exposure accurately, the log-odds ratio drops to 0.07
and the corresponding p-value increases to 0.49.