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SUMMARY:&quot\;Addressing missingness using two-phase sampling for nonresp
 onse: methods and benefits&quot\; - Dr Nanhua Zhang\, Cincinnati Children
 ’s Hospital Medical Center
DTSTART:20160909T133000Z
DTEND:20160909T143000Z
UID:TALK67160@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:Nonresponse is common in epidemiological surveys and clinical 
 trials. Common methods for dealing with missing data rely on untestable as
 sumptions. Nonresponse two-phase sampling (NTS)\, which re-contacts and co
 llects data from a subsample of the initial nonrespondents\, has been used
  to reduce nonresponse bias. We propose two methods for dealing with data 
 collected from NTS sampling: (1) Nonrespondent subsample multiple imputati
 on (NSMI)\, where multiple imputation was performed within the subsample o
 f nonrespondents in phase I using additional data collected in phase II\; 
 (2) A Bayesian selection model which utilizes the additional data collecte
 d in phase II of data collection. We examine the performance of the method
 s under various missing data mechanisms using simulation studies and apply
  the methods to a Quality of Life (QOL) dataset. The simulation study show
 s that the gain of using the NTS scheme can be substantial\, even if NTS s
 ampling only collects data from a small proportion of the initial nonrespo
 ndents.
LOCATION:Large  Seminar Room\, 1st Floor\, Institute of Public Health\, Un
 iversity Forvie Site\, Robinson Way\, Cambridge
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