• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.401-409
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• 2006

We consider a generalized partly-parametric additive risk model which generalizes the partly parametric additive risk model suggested by McKeague and Sasieni (1994). As an estimation method of this model, we propose to use the weighted least square estimation, suggested by Huffer and McKeague (1991), for Aalen's additive risk model by a piecewise constant risk. We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least squares method.

• Journal of the Korean Statistical Society
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• 제25권3호
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• pp.433-444
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• 1996

In contrast to the multiplicative risk model, the additive risk model specifies that the hazard function with covariates is the sum of, rather than product of, the baseline hazard function and the regression function of covariates. We, in this paper, propose a method for checking the adequacy of the additive risk model based on partial-sum of matingale residuals. Under the assumed model, the asymptotic properties of the proposed test statistic and approximation method to find the critical values of the limiting distribution are studied. Several real examples are illustrated.

• Journal of the Korean Data and Information Science Society
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• 제18권1호
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• pp.97-105
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• 2007

We consider a nonparametric additive risk model that is based on splines. This model consists of both purely and smoothly nonparametric components. As an estimation method of this model, we use the weighted least square estimation by Huller and Mckeague (1991). We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least square method.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2006년도 추계 학술발표회 논문집
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• pp.49-50
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• 2006

We consider a nonparametric additive risk model that are based on splines. This model consists of both purely and smoothly nonparametric components. As an estimation method of this model, we use the weighted least square estimation by Huffer and McKeague (1991). We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least square method.

• Journal of the Korean Data and Information Science Society
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• 제19권2호
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• pp.421-429
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• 2008

We consider a general semiparametric additive risk model that consists of three components. They are parametric, purely and smoothly nonparametric components. In parametric component, time dependent term is known up to proportional constant. In purely nonparametric component, time dependent term is an unknown function, and time dependent term in smoothly nonparametric component is an unknown but smoothly function. As an estimation method of this model, we use the weighted least square estimation by Huffer and McKeague (1991). We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least square method.

• Journal of the Korean Statistical Society
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• 제26권4호
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• pp.429-443
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• 1997

We consider the problem of obtaining several types of simultaneous confidence bands for the survival curve under the additive risk model. The derivation uses the weak convergence of normalized cumulative hazard estimator to a mean zero Gaussian process whose distribution can be easily approxomated through simulation. The bands are illustrated by applying them from two well-known clinicla studies. Finally, simulation studies are carried outo to compare the performance of the proposed bands for the survival function under the additive risk model.

• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.537-549
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• 1995

In this article, we propose a class of weighted estimators for the excess risk in additive risk model with a binary covariate. The proposed estimator is consistent and asymptotically normal. When the assumed model is inappropriate, however, the estimators with different weights converge to nonidentical constants. This fact enables us to develop a goodness-of-fit test for the excess assumption by comparing estimators with diffrent weights. It is shown that the proposed test converges in distribution to normal with mean zero and is consistent under the model misspecifications. Furthermore, the finite-sample properties of the proposed test procedure are investigated and two examples using real data are presented.

• 응용통계연구
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• 제25권3호
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• pp.403-413
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• 2012

The modifications suggested in Uhm et al. (2011) are studied using a partly parametric version of Aalen's additive risk model. A follow-up time period is partitioned into intervals, and hazard functions are estimated as a piecewise constant in each interval. A maximum likelihood estimator by iteratively reweighted least squares and variance estimates are suggested based on the model as well as evaluated by simulations using mean square error and a coverage probability, respectively. In conclusion the modifications are needed when there are a small number of uncensored deaths in an interval to estimate the piecewise constant hazard function.

• 응용통계연구
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• 제9권1호
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• pp.75-89
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• 1996

본 논문에서는 하나의 이진공변량(binary covariate)을 갖는 가산위험모형(additive risk model)의 적합도를 검정하기 위한 검정법을 제안하였다. 제안된 검정법은 마팅게일잔차(martingale residual)에 기초한 Wei(1984)의 비례위험모형(proportional hazards model)의 적합도검정법을 가산위험모형으로 확장한 형태이다. 제안된 검정통계량의 점근성질을 유도하였고 실제자료에 적용하여 보았다.

• Journal of the Korean Statistical Society
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• 제27권3호
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• pp.359-368
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• 1998

Let ξ$_{p}$(z$_{0}$) be the pth quantile of the distribution of the survival time of an individual with time-invariant covariate vector z$_{0}$ in the additive risk model. We propose an estimator of (ξ$_{p}$(z$_{0}$) and derive its asymptotic distribution, and then construct an approximate confidence interval of ξ$_{p}$(z$_{0}$) . Simulation studies are carried out to investigate performance of the proposed estimator far practical sample sizes in terms of empirical coverage probabilities. Also, the estimator is illustrated on small cell lung cancer data taken from Ying, Jung, and Wei (1995) .d Wei (1995) .