Accounting for Misclassification Bias in Binary Outcome Measures of Illness: The Case of Post-Traumatic Stress Disorder in Male Veterans

Abstract

The theoretical consequences of measurement error in outcome variables that are continuous are widely known by practitioners, at least for the classical model: purely random errors will lead to a loss of efficiency but not to bias in regression coefficients. When the outcome variable is binary, however, regression coefficients, both linear and nonlinear, will contain bias, even if the measurement error (in this setting more commonly referred to as classification error) is purely random. This paper illustrates a method of correcting for misclassification bias that relies solely on the primary survey data. It is particularly suited to analyses of surveys where external validation of survey responses is unavailable but where there is strong reason to suspect contaminated data. *Smith College 49 50 SAVOCA This situation is common in observational studies of the health of populations. The technique is applied to a model of the antecedents of post-traumatic stress disorder (PTSD) using data from a large-scale cross-sectional survey of Vietnam-era veterans.Results show that when adjusted for errors in diagnoses, the sample PTSD prevalence estimate falls significantly; that failure to correct for misclassification in PTSD dramatically understates the effects of risk factors; and that this downward bias remains even when the model incorporates differential classification errorsthat is, errors that are correlated with some of the explanatory variables in the model.

Accounting for misclassification bias of binary outcomes due to underscreening: a sensitivity analysis

“…The goal of our method was to demonstrate the use of the Bayesian selection model for missing outcome or misclassified outcome due to under-screening. Unlike other methods that rely on assumptions [ 8 , 9 ] or validation data, [ 12 ] the BSM method relates the propensity of receiving screening to the disease status through a sensitivity parameter. By varying the sensitivity parameter, the BSM method demonstrated how the prevalence and the association of risk factors change with the sensitivity parameter.…”

“…Standard approach to handle misclassification in binary outcomes relies on validation study of a subsample of initial non-respondents in the study population. When validation data are not available, Hausman et al (1998) [ 11 ] and Savoca (2011) [ 12 ] examine the misclassification bias as a function of the error rates, under balanced and unbalanced scenarios. However, these methods require assumptions on the functional form of the positive diagnosis probability and the misclassification parameters [ 12 ].…”

“…When validation data are not available, Hausman et al (1998) [ 11 ] and Savoca (2011) [ 12 ] examine the misclassification bias as a function of the error rates, under balanced and unbalanced scenarios. However, these methods require assumptions on the functional form of the positive diagnosis probability and the misclassification parameters [ 12 ]. Shebl et al (2012) develop a likelihood-based method to estimate incidence when disease status is measured imperfectly based on hidden Markov models, while assuming the known constant levels of sensitivity and specificity and constant incidence rates over time [ 4 ].…”

Depression and self-care in diabetes; adjustment for misclassification bias: application of predictive weighting method

“…Therefore, due to the high prevalence of depression in diabetic patients and since various questionnaires with different diagnostic value (sensitivity and specificity less than 100%) are used to measure depressive mood, it is possible for misclassification to occurs in examining the association between depressive mood as the exposure and self-care as the outcome; misclassifications due to un-perfect tests [ 19 ]. Measurement error as the difference between the measured value of the variable and the actual value affects the validity of the study [ 20 ]. Measurement error and misclassification in two-by-two probability tables reduce the power of statistical inference to examine the association between the exposure and the outcome [ 21 ].…”

Repeated responses in misclassification binary regression: A Bayesian approach

“…In the context of binary regression models for cross-sectional studies, ignoring misclassification errors can produce biased covariate effect estimates (Neuhaus, 1999; McInturff et al., 2004; Meyer and Mittag, 2017; Savoca, 2011; Carroll et al., 2006). McInturff et al.…”