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RISS 인기검색어
- 검색키워드
- 저자 : Pao-sheng Shen (검색결과 7 건)
- 무료
- 기관 내 무료
- 유료
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A self-consistent estimator of survival function with interval-censored and left-truncated data
Pao-sheng Shen 한국통계학회 2015 Journal of the Korean Statistical Society Vol.44 No.2
Interval censoring refers to a situation in which, T ∗ i , the time to occurrence of an event of interest is only known to lie in an interval [L∗i , R∗i ]. In some cases, the variable T ∗ i also suffers left-truncation. The nonparametric maximum likelihood estimator (NPMLE) of the survival function of T ∗ i can be obtained by using an EM algorithm of Turnbull (1976). One disadvantage of the NPMLE is that it is not uniquely defined in the innermost intervals. In this article, we propose a self-consistent estimator (SCE), which does not require interpolation. Furthermore, we show that the NPMLE is also an SCE. We establish the consistency of the SCE under certain conditions, which implies that the NPMLE is also a consistent estimator. A simulation study is conducted to compare the performance between the SCE and the NPMLE.
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Median regression model with left truncated and interval-censored data
Pao-sheng Shen 한국통계학회 2013 Journal of the Korean Statistical Society Vol.42 No.4
We consider the problem of fitting a heteroscedastic median regression model fromleft-truncated and interval-censored data. It is demonstrated that the adapted Efron’sself-consistency equation of McKeague, Subramanian, and Sun (2001) can be extendedto analyze left-truncated and interval-censored data. The asymptotic property of theproposed estimator is established. We evaluate the finite sample performance of theproposed estimators through simulation studies.
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Estimation of Kendall’s tau for bivariate doubly truncated data
Pao-sheng Shen 한국통계학회 2016 Journal of the Korean Statistical Society Vol.45 No.1
In this article, we consider the estimation of Kendall’s tau for bivariate doubly truncated data, where two correlated event times are potentially observed only if both fall within subject specific intervals of times. Using the inverse-probability-weighted (IPW) approach, we propose two nonparametric estimators of Kendall’s tau for bivariate doubly truncated data. The first estimator is based on V-statistics and the second estimator is based on weighted comparable pairs. The asymptotic properties of the proposed estimators are established. Simulation studies are conducted to investigate their finite sample performance.
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Pseudo MLE for semiparametric transformation model with doubly truncated data
Pao-sheng Shen,Yi Liu 한국통계학회 2019 Journal of the Korean Statistical Society Vol.48 No.3
In this article, we consider the efficient estimation of the semiparametric transformation model with doubly truncated data. We propose a two-step approach for obtaining the pseudo maximum likelihood estimators (PMLE) of regression parameters. In the first step, the truncation time distribution is estimated by the nonparametric maximum likelihood estimator (Shen, 2010a) when the distribution function K of the truncation time is unspecified or by the conditional maximum likelihood estimator (Bilker and Wang, 1996) when K is parameterized. In the second step, using the pseudo complete-data likelihood function with the estimated distribution of truncation time, we propose expectation– maximization algorithms for obtaining the PMLE.Weestablish the consistency of the PMLE. The simulation study indicates that the PMLE performs well in finite samples. The proposed method is illustrated using an AIDS data set.
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Regression analysis of doubly truncated data based on pseudo-observations
Shen Pao-sheng 한국통계학회 2021 Journal of the Korean Statistical Society Vol.50 No.4
Doubly truncated data arise when an individual is potentially observed only if its failure-time lies within a certain interval, unique to that individual. In this paper, we consider the pseudo-observations approach for estimating regression coefficients when data is subject to double truncation. The pseudo-observations generated from the nonparametric maximum likelihood estimates (NPMLE) of the survival function are used as response variables in a generalized estimating equation to estimate the parameters of the model. We look at two estimators for regression parameters of survival probabilities based on different ways of defining pseudo-observations, namely, the simple pseudo-observations (SPO) and stopped pseudo-observations (STPO). We establish asymptotic properties of the two estimators under some conditions. Simulations results show that the proportion of failed estimation based on STPO are smaller than that based on SPO. The estimator based on STPO performs adequately for finite samples while the estimator based on SPO can be very unstable when sample size is not large enough.
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Shen Pao-sheng 한국통계학회 2023 Journal of the Korean Statistical Society Vol.52 No.1
Establishing equivalence of two treatments has received a lot attention in the pharmaceutical industry. For assessing equivalence of two survival curves, an elegant test is proposed by Wellek (Biometrics 49:877–881, 1993) under the Cox proportional hazards (PH) model. An alternative test based on the proportional odds (PO) model was proposed by Martinez et al. (Stat Methods Med Res 26:75–87, 2017). Recently, Shen (J Biopharm Stat 31:79–90, 2021) proposed a test for equivalence based on a semiparametric log transformation model, which can be used if neither the PH nor the PO assumptions hold. In this article, under the class of Box–Cox transformation models (BCTM), we propose an equivalence test for the difference of two survival functions. Under the class of BCTM, we show that the hypothesis of equivalence of two survival functions can be formulated as a two-sided test which involves only the treatment effect parameter. Simulation results show that the proposed test has satisfactory size and adequate power for finite sample.
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A multiple imputation approach for the Cox–Aalen cure model with interval-censored data
Shen Pao-sheng 한국통계학회 2023 Journal of the Korean Statistical Society Vol.52 No.4
Interval censored survival data, where the exact event time is only known to lie in an interval, is commonly encountered in practice. Furthermore, medical advancements have made it possible for a fraction of patients to be cured. In this article, we analyze interval-censored data using the Cox–Aalen model with a cure fraction, where the probability of being uncured is determined by a logistic regression model and the failure times of the uncured subjects are modelled by the Cox–Aalen model with fixed covariates. We propose a multiple imputation (MI) scheme for obtaining parameter and variance estimates for both the cure probability and survival distribution of the uncured subjects. One major advantage of the proposed MI scheme is its simplicity since it avoids computational complexity resulting from interval censoring and presence of a cure fraction. The presented approach can be implemented by using the existing software for the Cox–Aalen model with right censored data. Simulation studies indicate that the approach performs well for practical situation. We apply the proposed method to the analysis of the data from hypobaric decompression sickness (HDS) study.
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