We consider a failure hazard function, conditional on a time-independent covariate Z, given by ηγ0(t)fβ0(Z). The baseline hazard function ηγ0 and the relative risk fβ0 both belong to parametric families with θ = (β, γ0)> ∈ R. The covariate Z has an unknown density and is measured with an error through an additive error model U = Z + ε where ε is a random variable, independent from Z, with known...

Key WOrds and Phrases: errors-in-variab7es; functiona7 mode7s; genera7ized 7inear mode7; /II-estimation; measurement error; structura7 mode7s. ABSTRACT This paper discussed a general strategy for reducing measurement-error-induced bias in statistical models. It is assu.ed that the measure.ent ·error is unbiased with a known variance although no other distributional assulIPtions on the measur..e...

Statistical methodology is presented for the analysis of multiple events with random eeects and measurement error. We model multiple events in a general space using a random measure, and deene point process regression models with residual random eeects. Our approach to parameter estimation and signiicance testing is to start with a simple naive model of Poisson process regression, and then to a...

When covariates are measured with error, inference based on conventional generalized linear models can yield biased estimatesof regressionparameters. This problem can potentiallybe rectiŽed byusing generalizedlinear latent and mixedmodels (GLLAMM), including a measurementmodel for the relationship between observed and true covariates. However, the models are typically estimated under the assump...

Prediction precision is arguably the most relevant criterion of a model in practice and is often a sought after property. A common difficulty with covariates measured with errors is the impossibility of performing prediction evaluation on the data even if a model is completely given without any unknown parameters. We bypass this inherent difficulty by using special properties on moment relation...

This paper studies Cox‘s proportional hazards model under covariate measurement error. Nakamura‘s (1990) methodology of corrected log-likelihood will be applied to the so called Breslow likelihood, which is, in the absence of measurement error, equivalent to partial likelihood. For a general error model with possibly heteroscedastic and non-normal additive measurement error, corrected estimator...

In many longitudinal studies, it is of interest to characterize the relationship between a time-to-event (e.g. survival) and several time-dependent and time-independent covariates. Time-dependent covariates are generally observed intermittently and with error. For a single time-dependent covariate, a popular approach is to assume a joint longitudinal data-survival model, where the time-dependen...