Lifetime Data Anal - A consistent NPMLE of the joint distribution function with competing risks data under the dependent masking and right-censoring model.

Tópicos

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Resumo

Dinse (Biometrics, 38:417-431, 1982) provides a special type of right-censored and masked competing risks data and proposes a non-parametric maximum likelihood estimator (NPMLE) and a pseudo MLE of the joint distribution function [Formula: see text] with such data. However, their asymptotic properties have not been studied so far. Under the extention of either the conditional masking probability (CMP) model or the random partition masking (RPM) model (Yu and Li, J Nonparametr Stat 24:753-764, 2012), we show that (1) Dinse's estimators are consistent if [Formula: see text] takes on finitely many values and each point in the support set of [Formula: see text] can be observed; (2) if the failure time is continuous, the NPMLE is not uniquely determined, and the standard approach (which puts weights only on one element in each observed set) leads to an inconsistent NPMLE; (3) in general, Dinse's estimators are not consistent even under the discrete assumption; (4) we construct a consistent NPMLE. The consistency is given under a new model called dependent masking and right-censoring model. The CMP model and the RPM model are indeed special cases of the new model. We compare our estimator to Dinse's estimators through simulation and real data. Simulation study indicates that the consistent NPMLE is a good approximation to the underlying distribution for moderate sample sizes.

Resumo Limpo

dins biometr provid special type rightcensor mask compet risk data propos nonparametr maximum likelihood estim npmle pseudo mle joint distribut function formula see text data howev asymptot properti studi far extent either condit mask probabl cmp model random partit mask rpm model yu li j nonparametr stat show dins estim consist formula see text take finit mani valu point support set formula see text can observ failur time continu npmle uniqu determin standard approach put weight one element observ set lead inconsist npmle general dins estim consist even discret assumpt construct consist npmle consist given new model call depend mask rightcensor model cmp model rpm model inde special case new model compar estim dins estim simul real data simul studi indic consist npmle good approxim under distribut moder sampl size

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