Author: Arthur V. PetersonPublisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 134
Book Description
Nonparametric Estimation in the Competing Risks Problem
Author: Arthur V. PetersonPublisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 134
Book Description
Nonparametric Estimation in the Competing Risks Problem
Author: Arthur V. Peterson (Jr.)Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 290
Book Description
Nonparametric Estimation for Non-time-homogeneous Markov Processes in the Problem of Competing Risks
Author: Thomas Richard FlemingPublisher:
ISBN:
Category : Markov processes
Languages : en
Pages : 138
Book Description
Semi-parametric Modeling of the Semi-competing Risks Problem
Author: Hongyu JiangNonparametric Analysis of Semi-competing Risks Data
Author: Jing LiPublisher:
ISBN:
Category :
Languages : en
Pages : 278
Book Description
It is generally of interest to explore if the risk of death would be modified by medical conditions (e.g., illness) that have occurred prior. This situation gives rise to semicompeting risks data, which are a mixture of competing risks and progressive state data. This type of data occurs when a non-terminal event can be censored by a well-defined terminal event, but not vice versa. In the first part of this dissertation, the shared gamma-frailty conditional Markov model (GFCMM) is adopted because it bridges the copula models and illness-death models. Maximum likelihood estimation methodology has been proposed in the literature. However, we found through numerical experiments that the unrestricted model sometimes yields nonparametric biased estimation. Hence a practical guideline is provided for using the GFCMM that includes (i) a score test to assess whether the restricted model, which does not exhibit estimation problems, is reasonable under a proportional hazards assumption, and (ii) a graphical illustration to evaluate whether the unrestricted model yields nonparametric estimation with substantial bias for cases where the test provides a statistical significant result against the restricted model. However, the scientific question of interest that whether the status of non-terminal event alters the risk to terminal event can only be partially addressed based on the aforementioned approach. Therefore in the second part of this dissertation, we adopt a Markov illness-death model, whose transition intensities are essentially equivalent to the marginal hazards defined in GFCMM, but with different interpretations; we develop three nonparametric tests, including a linear test, a Kolmogorov-Smirnov-type test, and a L2-distance-type test, to directly compare the two transition intensities under consideration. The asymptotic properties of the proposed test statistics are established using empirical process theory. The performance of these tests in nite samples is numerically evaluated through extensive simulation studies. All three tests provide similar power levels with non-crossing curves of cumulative transition intensities, while the linear test is suboptimal when the curves cross. Eventually, the proposed tests successfully address the scientific question of interest. This research is applied to Indianapolis-Ibadan Dementia Project (IIDP) to explore whether dementia occurrence changes mortality risk.
Nonparametric Estimation for Current Status Data with Competing Risks
Author: Marloes Henriëtte MaathuisPublisher:
ISBN:
Category : Competing risks
Languages : en
Pages : 262
Book Description
Nonparametric Estimation of Cumulative Incidence Functions for Competing Risks Data with Missing Cause of Failure
Author: Georgios EffraimidisNonparametric Estimation of Competing Risks Models with Covariates
Author: Jean-David FermanianNonparametric Estimation with Right-censored and Masked Competing Risks Data
Author: Jiahui LiPublisher:
ISBN: 9781267552495
Category : Competing risks
Languages : en
Pages : 212
Book Description
Author: Limin Peng