Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Estimation For The Cox Proportional Hazards Cure Model   While analysing time-to-event data, it is possible that a certain fraction of
subjects will never experience the event of interest and they are said to be
cured. When this feature of survival models is taken into account, the models
are commonly referred to as cure models. In the presence of covariates, the
conditional survival function of the population can be modelled by using cure
model which depends on the probability of being uncured (incidence) and the
conditional survival function of the uncured subjects (latency), and a
combination of logistic regression and Cox proportional hazards (PH) regression
is used to model the incidence and latency respectively. In this paper, we have
shown the asymptotic normality of the profile likelihood estimator via
asymptotic expansion of the profile likelihood and obtain the explicit form of
the variance estimator with an implicit function in the profile likelihood. We
have also shown the efficient score function based on projection theory and the
profile likelihood score function are equal. Our contribution in this paper is
that we have expressed the efficient information matrix as the variance of the
profile likelihood score function. A simulation study suggests that the
estimated standard errors from bootstrap samples (SMCURE package) and the
profile likelihood score function (our approach) are providing similar and
comparable results. The numerical result of our proposed method is also shown
by using the melanoma data from SMCURE R-package (Cai et al., 2012) and we
compare the results with the output obtained from SMCURE package.

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The categories of this paper are: gr-qc
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
1
The categories of this paper are: cs
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ