Lifetime Data Anal - Estimation in a competing risks proportional hazards model under length-biased sampling with censoring.

Tópicos

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Resumo

What population does the sample represent? The answer to this question is of crucial importance when estimating a survivor function in duration studies. As is well-known, in a stationary population, survival data obtained from a cross-sectional sample taken from the population at time t(0) represents not the target density f (t) but its length-biased version proportional to t f (t), for t > 0. The problem of estimating survivor function from such length-biased samples becomes more complex, and interesting, in presence of competing risks and censoring. This paper lays out a sampling scheme related to a mixed Poisson process and develops nonparametric estimators of the survivor function of the target population assuming that the two independent competing risks have proportional hazards. Two cases are considered: with and without independent censoring before length biased sampling. In each case, the weak convergence of the process generated by the proposed estimator is proved. A well-known study of the duration in power for political leaders is used to illustrate our results. Finally, a simulation study is carried out in order to assess the finite sample behaviour of our estimators.

Resumo Limpo

popul sampl repres answer question crucial import estim survivor function durat studi wellknown stationari popul surviv data obtain crosssect sampl taken popul time t repres target densiti f t lengthbias version proport t f t t problem estim survivor function lengthbias sampl becom complex interest presenc compet risk censor paper lay sampl scheme relat mix poisson process develop nonparametr estim survivor function target popul assum two independ compet risk proport hazard two case consid without independ censor length bias sampl case weak converg process generat propos estim prove wellknown studi durat power polit leader use illustr result final simul studi carri order assess finit sampl behaviour estim

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