title: Evaluating assumptions of weighting class methods for partial response using a selection model.
abstract: In survey sampling, information about the prevalence of a health outcome Y for a defined target population is frequently obtained using a two-stage data collection process. In the first stage, households that have members of the target population are identified and socio-demographic data that are believed to be associated with Y are collected. At the end of the first stage of data collection, permission is requested to contact the member's health providers so that accurate information about Y can be obtained. When permission is obtained, a second phase of data collection is conducted in which those health providers are contacted and Y is obtained. A 'complete response' results when data are obtained from both the first and the second phases of the survey. A 'partial response' results when data are collected from the first phase, but Y is not obtained in the second phase. To adjust for selection bias in estimating the prevalence of Y caused by partial responders' missing Y values, potential differences between complete and partial responders are typically taken into account by using weighting class methods. These methods assume that missing Y values are missing at random (MAR). This paper describes statistical tests for evaluating whether missing data are missing completely at random or MAR.
authors.affiliation: Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases, 1600 Clifton Road, NE, Mail Stop E-32, Atlanta, GA 30333, USA. pzs6@cdc.hhs.gov
authors.forename: Philip J
authors.initials: PJ
authors.lastname: Smith
authors.forename: Lawrence C
authors.initials: LC
authors.lastname: Marsh
mesh_terms: D016009:Chi-Square Distribution; D002675:Child, Preschool; D003625:Data Collection / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D007223:Infant; D016014:Linear Models; D008499:Medical Records; D015233:Models, Statistical; D012494:Sampling Studies; D015983:Selection Bias; D014611:Vaccination / Q000706:statistics & numerical data