Seminar
Nathaniel Schenker
Improving on Analyses of Self-Reported Data in an Interview-Based Health Survey
by Using Information from a Smaller Examination-Based Survey
Common data sources for assessing the health of a population include large-scale surveys
based on interviews that often pose questions requiring self-reports. Answers to such questions
might not always reflect the true prevalences of health conditions, either due to the nature of
the questions asked or due to inaccurate responses. This talk describes a study, involving two
surveys conducted by the National Center for Health Statistics, Centers for Disease Control and
Prevention, of methods for addressing this "measurement error" problem. Models predicting clinical
values from self-reported values and covariates are fitted to data from the National Health and
Nutrition Examination Survey (NHANES), which asks self-report questions during an interview component
and also obtains clinical measurements during a physical examination component. The fitted models are
used to multiply impute clinical values for the National Health Interview Survey (NHIS), a larger survey
which obtains data solely via interviews. Results for examples involving hypertension, diabetes, and
obesity suggest that estimates of measures of health based on the multiply imputed clinical values
are different from those based on the NHIS self-reported data alone and have smaller estimated standard
errors than those based solely on the NHANES clinical data. The talk will discuss the relationship of
the methods used in the study to other methods, along with limitations, practical considerations, and
areas for future research.
