期刊
STATISTICAL METHODS IN MEDICAL RESEARCH
卷 27, 期 10, 页码 3062-3076出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0962280217690939
关键词
Bias adjustment; binary-outcome proxy pattern-mixture model; binary variable; illicit drug use; Iranian Mental Health Survey; latent class analysis; latent class log-linear model; measurement error; nonresponse bias; survey data
类别
资金
- Tehran University of Medical Sciences
PurposeThe prevalence estimates of binary variables in sample surveys are often subject to two systematic errors: measurement error and nonresponse bias. A multiple-bias analysis is essential to adjust for both biases. MethodsIn this paper, we linked the latent class log-linear and proxy pattern-mixture models to adjust jointly for measurement errors and nonresponse bias with missing not at random mechanism. These methods were employed to estimate the prevalence of any illicit drug use based on Iranian Mental Health Survey data. ResultsAfter jointly adjusting for measurement errors and nonresponse bias in this data, the prevalence (95% confidence interval) estimate of any illicit drug use changed from 3.41 (3.00, 3.81)% to 27.03 (9.02, 38.76)%, 27.42 (9.04, 38.91)%, and 27.18 (9.03, 38.82)% under missing at random, missing not at random, and an intermediate mode, respectively. ConclusionsUnder certain assumptions, a combination of the latent class log-linear and binary-outcome proxy pattern-mixture models can be used to jointly adjust for both measurement errors and nonresponse bias in the prevalence estimation of binary variables in surveys.
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