Determination of risk factor associations with questionnaire outcomes: A methods case study

Karen Bandeen-Roche*, Guan-Hua Huang, Beatriz Munoz, Gary S. Rubin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Increasingly in biomedical studies, health status is inferred through a series of questionnaire item responses. Challenges for analyzing associations between such responses and risk factors include multiplicity - many indicators must be combined to derive summary statements about health status, and measurement error persons' self-report fluctuates due to causes other than substantive health changes. In order to deal with these challenges, the authors propose a strategy which comprises three methods: 1) score the item responses, then regress the score on predictors; 2) regress each item response on predictors, accounting for within-person associations; and 3) summarize and analyze the item responses jointly, using a latent variable model. The authors develop modeling and diagnostic procedures for method 3. They then show how the three-method analytic strategy can be used to solve the problem of determining which aspects of vision are associated with self- reported functioning in activities that require seeing at a distance. They demonstrate that methods 2 and 3 illuminate basic findings from method 1 by adding specificity, describing patterns as well as severities of health impairments, and identifying isolated items that relate to risk factors differentially than others. They conclude that the three-method strategy specifies how risk factors determine questionnaire-based health outcomes substantially better than any of the methods in isolation.

Original languageEnglish
Pages (from-to)1165-1178
Number of pages14
JournalAmerican Journal of Epidemiology
Volume150
Issue number11
DOIs
StatePublished - 1 Dec 1999

Keywords

  • Aging
  • Discrete data
  • Goodness-of-fit
  • Latent class
  • Latent variable
  • Multivariate regression
  • Vision

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