Thursday, April 2, 2015 - 3:00pm
209 W. Eighteenth Ave. (EA), Room 170
Sex, Lies and Self-Reported Counts: General Birth-Death Processes to Model Self-Reported Count of Sexual Behavior
Marc Suchard, University of California, Los Angeles
Surveys often ask respondents to report non-negative counts, but respondents may misremember or round to a nearby multiple of five or 10. The error inherent in this heaping can bias estimation. To avoid bias, we propose a novel reporting distribution arising from a general birth-death process whose underlying parameters are readily interpretable as rates of misremembering and rounding. The process accommodates a variety of heaping grids and allows for quasi-heaping to values nearly but not equal to heaping multiples. Inference using this stochastic process requires novel, efficient techniques to compute finite-time transition probabilities for arbitrary birth-death processes that we provide through Laplace transforms and a continued fraction representation. We present a Bayesian hierarchical model for longitudinal samples with covariates to infer both the unobserved true distribution of counts and the parameters that control the heaping process. Finally, we apply our methods to longitudinal self-reported counts of sex partners in a study of high-risk behavior in HIV-positive youth.
Marc Suchard is Professor in the Departments of Biomathematics, Biostatistics and Human Genetics at David Geffen School of Medicine at UCLA and UCLA School of Public Health. He is a recent recipient of the Committee of Presidents of Statistical Societies (COPSS) Presidents’ Award for outstanding contributions to the statistics profession by a person aged 40 or under.