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Differential Misclassification (differential + misclassification)

Distribution by Scientific Domains
Mathematics and Statistics40%

Selected Abstracts

Misclassification and the use of register-based indicators for depression

ACTA PSYCHIATRICA SCANDINAVICA, Issue 4 2009
K. Thielen
Objective:, To study the degree to which depression indicators based on register data on hospital and antidepressant treatment suffer from differential misclassification with respect to gender, age and social group. Method:, Data on 7378 persons were obtained by linking a cross-sectional survey of Danish adults aged 40 and 50 years with population-based registers. Misclassification was analysed by comparing survey data to register data on major depression using the method proposed by Rothman and Greenland. Results:, Differential misclassification was found. Adjustment for misclassification reduced women's odds ratios from 2.18 to 1.00 for hospital treatment and from 1.70 to 1.10 for antidepressants. For the lower social group, the corresponding odds ratios increased from 1.18 to 3.52, and from 1.35 to 2.32 respectively, whereas odds ratios with respect to age remained almost unchanged. Conclusion:, Differential misclassification should be considered when register-based information about hospital and antidepressant treatment are used as depression indicators. [source]

Causal Inference with Differential Measurement Error: Nonparametric Identification and Sensitivity Analysis

AMERICAN JOURNAL OF POLITICAL SCIENCE, Issue 2 2010
Kosuke Imai
Political scientists have long been concerned about the validity of survey measurements. Although many have studied classical measurement error in linear regression models where the error is assumed to arise completely at random, in a number of situations the error may be correlated with the outcome. We analyze the impact of differential measurement error on causal estimation. The proposed nonparametric identification analysis avoids arbitrary modeling decisions and formally characterizes the roles of different assumptions. We show the serious consequences of differential misclassification and offer a new sensitivity analysis that allows researchers to evaluate the robustness of their conclusions. Our methods are motivated by a field experiment on democratic deliberations, in which one set of estimates potentially suffers from differential misclassification. We show that an analysis ignoring differential measurement error may considerably overestimate the causal effects. This finding contrasts with the case of classical measurement error, which always yields attenuation bias. [source]

Misclassification in Logistic Regression with Discrete Covariates

BIOMETRICAL JOURNAL, Issue 5 2003
Ori Davidov
Abstract We study the effect of misclassification of a binary covariate on the parameters of a logistic regression model. In particular we consider 2 × 2 × 2 tables. We assume that a binary covariate is subject to misclassification that may depend on the observed outcome. This type of misclassification is known as (outcome dependent) differential misclassification. We examine the resulting asymptotic bias on the parameters of the model and derive formulas for the biases and their approximations as a function of the odds and misclassification probabilities. Conditions for unbiased estimation are also discussed. The implications are illustrated numerically using a case control study. For completeness we briefly examine the effect of covariate dependent misclassification of exposures and of outcomes. [source]

A Note on Estimating Crude Odds Ratios in Case,Control Studies with Differentially Misclassified Exposure

BIOMETRICS, Issue 4 2002
Robert H. Lyles
Summary. Morrissey and Spiegelman (1999, Biometrics55, 338,344) provided a comparative study of adjustment methods for exposure misclassification in case-control studies equipped with an internal validation sample. In addition to the maximum likelihood (ML) approach, they considered two intuitive procedures based on proposals in the literature. Despite appealing ease of computation associated with the latter two methods, efficiency calculations suggested that ML was often to be recommended for the analyst with access to a numerical routine to facilitate it. Here, a reparameterization of the likelihood reveals that one of the intuitive approaches, the inverse matrix method, is in fact ML under differential misclassification. This correction is intended to alert readers to the existence of a simple closed-form ML estimator for the odds ratio in this setting so that they may avoid assuming that a commercially inaccessible optimization routine must be sought to implement ML. [source]