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SUMMARY:Modeling longitudinal observations with excess zeros and measureme
 nt error\, with application to nutritional epidemiology - Victor Kipnis\, 
 National Cancer Institute\, US
DTSTART:20101021T133000Z
DTEND:20101021T143000Z
UID:TALK26187@talks.cam.ac.uk
CONTACT:Michael Sweeting
DESCRIPTION:The field of dietary assessment provides numerous statistical 
 challenges. The great majority of large prospective studies of diet and di
 sease have been using a food frequency questionnaire (FFQ) to assess usual
  (i.e.\, long-term average) dietary intake.  It is now well appreciated th
 at such questionnaires involve substantial measurement error\, random and 
 systematic\, leading to distorted effects of diet and incorrect statistica
 l tests of diet-disease relationships.  To correct for this error\, many c
 ohorts include calibration sub-studies in which more precise short-term di
 etary instruments\, such as biomarkers\, multiple 24-hour dietary recalls 
 (24HRs)\, or food records\, are administered as reference instruments. Thu
 s\, usual dietary intake is assessed by those instruments with considerabl
 e measurement error. A popular method for correcting for measurement error
  of FFQ-reported intakes\, regression calibration\, has been developed und
 er the assumption that although reference observations may contain within-
 person random error\, they provide unbiased measurements. Application of t
 his method to foods that are not consumed every day by everyone (episodica
 lly consumed foods) is problematic\, since short-term reference instrument
 s usually include a substantial proportion of subjects with zero intakes\,
  leading to observations with excess zeros. In addition\, it is often pref
 erable to analyze the amounts of dietary components relative to the amount
  of total energy intake to take into account dietary composition. We prese
 nt the recently developed bivariate model for short-term reference observa
 tions on food and total energy intakes. The new model allows a rigorous ap
 plication of the regression calibration to the analysis of energy-adjusted
  associations between foods/nutrients and disease using repeat unbiased sh
 ort-term reference measurements in a calibration sub-study. We exemplify t
 he method by applying it to data from the US NIH-AARP Diet and Health Stud
 y. We also use simulations to compare the newly developed methodology with
  more conventional applications of regression calibration that do not take
  excess zeros in reference measurements into account. 
LOCATION:Large Seminar Room\, 1st Floor\, Institute of Public Health\, Uni
 versity Forvie Site\, Robinson Way\, Cambridge
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