Estimating disease prevalence from two-phase surveys with non-response at the second phase

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19 Citations (Scopus)

Abstract

In this paper we compare several methods for estimating population disease prevalence from data collected by two-phase sampling when there is non-response at the second phase. The traditional weighting type estimator requires the missing completely at random assumption and may yield biased estimates if the assumption does not hold. We review two approaches and propose one new approach to adjust for non-response assuming that the non-response depends on a set of covariates collected at the first phase: An adjusted weighting type estimator using estimated response probability from a response model; a modelling type estimator using predicted disease probability from a disease model; and a regression type estimator combining the adjusted weighting type estimator and the modelling type estimator. These estimators are illustrated using data from an Alzheimer's disease study in two populations. Simulation results are presented to investigate the performances of the proposed estimators under various situations. Copyright (C) 2000 John Wiley and Sons, Ltd.

Original languageEnglish
Pages (from-to)2101-2114
Number of pages14
JournalStatistics in Medicine
Volume19
Issue number16
DOIs
StatePublished - Aug 30 2000

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Non-response
Estimator
Weighting
Population
Alzheimer Disease
Two-phase Sampling
Missing Completely at Random
Alzheimer's Disease
Surveys and Questionnaires
Modeling
Biased
Covariates
Regression

ASJC Scopus subject areas

  • Epidemiology

Cite this

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abstract = "In this paper we compare several methods for estimating population disease prevalence from data collected by two-phase sampling when there is non-response at the second phase. The traditional weighting type estimator requires the missing completely at random assumption and may yield biased estimates if the assumption does not hold. We review two approaches and propose one new approach to adjust for non-response assuming that the non-response depends on a set of covariates collected at the first phase: An adjusted weighting type estimator using estimated response probability from a response model; a modelling type estimator using predicted disease probability from a disease model; and a regression type estimator combining the adjusted weighting type estimator and the modelling type estimator. These estimators are illustrated using data from an Alzheimer's disease study in two populations. Simulation results are presented to investigate the performances of the proposed estimators under various situations. Copyright (C) 2000 John Wiley and Sons, Ltd.",
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