A Semi‐parametric Transformation Frailty Model for Semi‐competing Risks Survival Data
نویسندگان
چکیده
منابع مشابه
Semiparametric transformation models for semicompeting survival data.
Semicompeting risk outcome data (e.g., time to disease progression and time to death) are commonly collected in clinical trials. However, analysis of these data is often hampered by a scarcity of available statistical tools. As such, we propose a novel semiparametric transformation model that improves the existing models in the following two ways. First, it estimates regression coefficients and...
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We propose a class of transformation models for survival data with a cure fraction. The class of transformation models is motivated by biological considerations, and it includes both the proportional hazards and the proportional odds cure models as two special cases. An efficient recursive algorithm is proposed to calculate the maximum likelihood estimators. Furthermore, the maximum likelihood ...
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We propose a new class of semiparametric frailty models for spatially correlated survival data. Specifically, we extend the ordinary frailty models by allowing random effects accommodating spatial correlations to enter into the baseline hazard function multiplicatively. We prove identifiability of the models and give sufficient regularity conditions. We propose drawing inference based on a marg...
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Semicompeting risks data are often encountered in clinical trials with intermediate endpoints subject to dependent censoring from informative dropout. Unlike with competing risks data, dropout may not be dependently censored by the intermediate event. There has recently been increased attention to these data, in particular inferences about the marginal distribution of the intermediate event wit...
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Semicompeting risks data are commonly seen in biomedical applications in which a terminal event censors a non-terminal event. Possible dependent censoring complicates statistical analysis. We consider regression analysis based on a non-terminal event, say disease progression, which is subject to censoring by death.The methodology proposed is developed for discrete covariates under two types of ...
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ژورنال
عنوان ژورنال: Scandinavian Journal of Statistics
سال: 2016
ISSN: 0303-6898,1467-9469
DOI: 10.1111/sjos.12244