Author: Arthur V. Peterson
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 134
Book Description
Nonparametric Estimation in the Competing Risks Problem
Author: Arthur V. Peterson
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 134
Book Description
Publisher:
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
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 Fleming
Publisher:
ISBN:
Category : Markov processes
Languages : en
Pages : 138
Book Description
Publisher:
ISBN:
Category : Markov processes
Languages : en
Pages : 138
Book Description
Semi-parametric Modeling of the Semi-competing Risks Problem
Nonparametric Analysis of Semi-competing Risks Data
Author: Jing Li
Publisher:
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.
Publisher:
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 Henriƫtte Maathuis
Publisher:
ISBN:
Category : Competing risks
Languages : en
Pages : 262
Book Description
Publisher:
ISBN:
Category : Competing risks
Languages : en
Pages : 262
Book Description
Nonparametric Estimation of Competing Risks Models with Covariates
Nonparametric Estimation with Right-censored and Masked Competing Risks Data
Author: Jiahui Li
Publisher:
ISBN: 9781267552495
Category : Competing risks
Languages : en
Pages : 212
Book Description
Publisher:
ISBN: 9781267552495
Category : Competing risks
Languages : en
Pages : 212
Book Description
Contributions to Semi-competing Risks Data
Nonparametric Estimation under Shape Constraints
Author: Piet Groeneboom
Publisher: Cambridge University Press
ISBN: 0521864011
Category : Business & Economics
Languages : en
Pages : 429
Book Description
This book introduces basic concepts of shape constrained inference and guides the reader to current developments in the subject.
Publisher: Cambridge University Press
ISBN: 0521864011
Category : Business & Economics
Languages : en
Pages : 429
Book Description
This book introduces basic concepts of shape constrained inference and guides the reader to current developments in the subject.