Nonparametric estimation of cause-specific cross hazard ratio with bivariate competing risks data

We propose an alternative representation of the cause-specificcross hazard ratio for bivariate competing risks data. The representationleads to a simple plug-in estimator, unlike an existing ad hocprocedure. The large sample properties of the resulting inferencesare established. Simulations and a real data example demonstratethat the proposed methodology may substantially reduce the computationalburden of the existing procedure, while maintaining similarefficiency properties.     

Nonparametric estimation of a Markov 'illness-death' process from interval-censored observations, with application to diabetes survival data

The nonparametric estimation of the cumulative transition intensityfunctions in a threestate time-nonhomogeneous Markov processwith irreversible transitions, an ‘illness-death’model, is considered when times of the intermediate transition,e.g. onset of a disease, are interval-censored. The times of‘death’ are assumed to be known exactly or to beright-censored. In addition the observed process may be left-truncated.Data of this type arise when the process is sampled periodically.For example, when the patients are monitored through periodicexaminations the observations on times of change in their diseasestatus will be interval-censored. Under the sampling schemeconsidered here the Nelson–Aalen estimator (Aalen, 1978)for a cumulative transition intensity is not applicable. Inthe proposed method the maximum likelihood estimators of someof the transition intensities are derived from the estimatorsof the corresponding subdistribution functions. The maximumlikelihood estimators are shown to have a self-consistency property.The self-consistency algorithm is developed for the computationof the estimators. This approach generalises the results fromTurnbull (1976) and Frydman (1992). The methods are illustratedwith diabetes survival data.     

A note on nonparametric quantile inference for competing risks and more complex multistate models

Nonparametric quantile inference for competing risks has recentlybeen studied by Peng & Fine (2007). Their key result establishesuniform consistency and weak convergence of the inverse of theAalen–Johansen estimator of the cumulative incidence function,using the representation of the cumulative incidence estimatoras a sum of independent and identically distributed random variables.The limit process is of a form similar to that of the standardsurvival result, but with the cause-specific hazard of interestreplacing the all-causes hazard. We show that this fact is nota coincidence, but can be derived from a general Hadamard differentiationresult. We discuss a simplified proof and extensions of theapproach to more complex multistate models. As a further consequence,we find that the bootstrap works.     

Nonparametric inference for competing risks current status data with continuous, discrete or grouped observation times

New methods and theory have recently been developed to nonparametrically estimate cumulative incidence functions for competing risks survival data subject to current status censoring. In particular, the limiting distribution of the nonparametric maximum likelihood estimator and a simplified naive estimator have been established under certain smoothness conditions. In this paper, we establish the large-sample behaviour of these estimators in two additional models, namely when the observation time distribution has discrete support and when the observation times are grouped. These asymptotic results are applied to the construction of confidence intervals in the three different models. The methods are illustrated on two datasets regarding the cumulative incidence of different types of menopause from a cross-sectional sample of women in the United States and subtype-specific HIV infection from a sero-prevalence study in injecting drug users in Thailand.     

Nonparametric Estimates of the Asymptotic Covariance Matrix Associated with BICKEL's Estimate for Multivariate Shift Parameters

Nonparametric estimates are proposed for the asymptotic covariance matrix associated with the vector of medians of averages of pairs when estimating the shift parameter in the multivariate one sample problem. These estimates are based on density estimation and are competitors to the “natural” estimate proposed by BICKEL (1965). Weak and strong consistency of the estimates as well as asymptotic normality are presented.     

Nonparametric variance estimation in the analysis of microarray data: a measurement error approach

We investigate the effects of measurement error on the estimationof nonparametric variance functions. We show that either ignoringmeasurement error or direct application of the simulation extrapolation,SIMEX, method leads to inconsistent estimators. Nevertheless,the direct SIMEX method can reduce bias relative to a naiveestimator. We further propose a permutation SIMEX method thatleads to consistent estimators in theory. The performance ofboth the SIMEX methods depends on approximations to the exactextrapolants. Simulations show that both the SIMEX methods performbetter than ignoring measurement error. The methodology is illustratedusing microarray data from colon cancer patients.