The random partition masking model for interval-censored and masked competing risks data

We study the nonparametric maximum likelihood estimate (NPMLE) of the cdf or sub-distribution functions of the failure time for the failure causes in a series system. The study is motivated by a cancer research data (from the Memorial Sloan-Kettering Cancer Center) with interval-censored time and ma...

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Bibliographic Details
Published in:Journal of statistical computation and simulation 2012-07, Vol.82 (7), p.981-1002
Main Authors: Wang, Jiaping, Yu, Qiqing, Wong, George Y. C.
Format: Article
Language:English
Subjects:
Algorithms
Cancer
Computation
Computer simulation
Failure
Failure times
interval-censored
masked competing risks data
Masking
Mathematical models
maximal intersections
Maximum likelihood method
Memorials
nonparametric MLE
Partitions
Probability distribution
RPM model
self-consistent algorithm
Simulation
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Summary:We study the nonparametric maximum likelihood estimate (NPMLE) of the cdf or sub-distribution functions of the failure time for the failure causes in a series system. The study is motivated by a cancer research data (from the Memorial Sloan-Kettering Cancer Center) with interval-censored time and masked failure cause. The NPMLE based on this data set suggests that the existing masking models are not appropriate. We propose a new model called the random partition masking model, which does not rely on the commonly used symmetry assumption (namely, given the failure cause, the probability of observing the masked failure causes is independent of the failure time; see Flehinger et al. Inference about defects in the presence of masking, Technometrics 38 (1996), pp. 247-255). The RPM model is easier to implement in simulation studies than the existing models. We discuss the algorithms for computing the NPMLE and study its asymptotic properties. Our simulation and data analysis indicate that the NPMLE is feasible for a moderate sample size.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2011.566567