Journal ArticleDOI
On errors-in-variables for binary regression models
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In this paper, the authors consider binary regression models when some of the predictors are measured with error and show that if the measurement error is large, the usual estimate of the probability of the event in question can be substantially in error, especially for high risk groups.Abstract:
SUMMARY We consider binary regression models when some of the predictors are measured with error. For normal measurement errors, structural maximum likelihood estimates are considered. We show that if the measurement error is large, the usual estimate of the probability of the event in question can be substantially in error, especially for high risk groups. In the situation of large measurement error, we investigate a conditional maximum likelihood estimator and its properties.read more
Citations
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Models for longitudinal data: a generalized estimating equation approach.
TL;DR: This article discusses extensions of generalized linear models for the analysis of longitudinal data in which heterogeneity in regression parameters is explicitly modelled and uses a generalized estimating equation approach to fit both classes of models for discrete and continuous outcomes.
Journal ArticleDOI
Exposure measurement error in time-series studies of air pollution: concepts and consequences.
Scott L. Zeger,Duncan C. Thomas,Francesca Dominici,Jonathan M. Samet,Joel Schwartz,Douglas W. Dockery,Aaron Cohen +6 more
TL;DR: This paper developed a systematic conceptual formulation of the problem of measurement error in epidemiologic studies of air pollution and then considered the consequences within this formulation, and presented new simple analyses of data on exposures of particulate matter < 10 microm in aerodynamic diameter from the Particle Total Exposure Assessment Methodology Study.
ReportDOI
Estimation and Comparison of Changes in the Presence of Information Right Censoring by Modeling the Censoring Process.
Margaret Wu,Raymond J. Carroll +1 more
TL;DR: In this paper, a linear random effect model with a probit model for the right censoring process is used to estimate the expected rates of change and the parameters of the right process.
Journal ArticleDOI
Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error
TL;DR: Two methods are provided to correct relative risk estimates obtained from logistic regression models for measurement errors in continuous exposures within cohort studies that may be due to either random (unbiased) within-person variation or to systematic errors for individual subjects.
Journal ArticleDOI
Statistical validation of intermediate endpoints for chronic diseases.
TL;DR: A criterion due to Prentice for the statistical validation of intermediate endpoints for chronic disease, which involves examining in a cohort or intervention study whether an exposure or intervention effect, adjusted for the intermediate endpoint, is reduced to zero is discussed.
References
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Bootstrap Methods: Another Look at the Jackknife
TL;DR: In this article, the authors discuss the problem of estimating the sampling distribution of a pre-specified random variable R(X, F) on the basis of the observed data x.
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The advanced theory of statistics
TL;DR: The Advanced Theory of Statistics by Maurice G. Kendall as discussed by the authors is a very handsomely produced volume which is one which it will be a pleasure to any mathematical statistician to possess.
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Distributions in Statistics: Continuous Multivariate Distributions
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Censored Data and the Bootstrap
TL;DR: The bootstrap, which is an elaboration of the jackknife, provides a general method for answering questions about the parameters of an unknown distribution when the data is subject to right censoring.