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Partial questionnaire designs, questionnaire non-response, and attributable fraction: applications to adult onset asthma.
Stat Med 2006 May; 25(9):1499-1519
The attributable fraction (AF) is often used to explore the policy implications of an association between a disease and an exposure. To date, there have been no proposed estimators of AF in the context of partial questionnaire designs (PQD). The PQD, first proposed in a public health context by Wacholder is often used to enhance response rates in questionnaires. It involves eliciting responses from each subject on preassigned subsets of questions, thereby reducing the burden of response. We propose a computationally efficient method of estimating logistic (or more generally, binary) regression parameters from a PQD model where there is non-response to the questionnaire and the rates of non-response differ between sub-populations. Assuming a log-linear model for the distribution of missing covariates, we employ the methods of Wacholder to motivate consistent estimating equations, and weight each subject's contribution to the estimating function by the inverse probability of responding to the questionnaire. We also propose techniques for goodness-of-fit to assist in model selection. We then use the PQD regression estimates to derive an estimate of AF similar to that proposed by Bruzzi. Finally, we demonstrate our methods using data obtained from a study on adult occupational asthma, conducted within a Massachusetts HMO. Although we concentrate on a particular type of missing data mechanism, other missing data techniques can be incorporated into AF estimation in a similar manner.
Respiratory-system-disorders; Bronchial-asthma; Questionnaires; Health-surveys; Diseases; Statistical-analysis; Biostatistics; Mathematical-models; Computer-models; Case-studies; Simulation-methods; Biological-effects; Exposure-assessment; Data-processing; Author Keywords: attributable risk; aetiologic fraction; goodness-of-fit; missing covariates; multiple matrix sampling; occupational asthma; prediction error; weighted estimating equations
E. Andres Houseman, Department of Biostatistics, Harvard School of Public Health, 655 Huntington Ave., Boston, MA 02115, USA
Issue of Publication
Statistics in Medicine
Harvard School of Public Health, Boston, Massachusetts
Page last reviewed: September 2, 2020
Content source: National Institute for Occupational Safety and Health Education and Information Division