Journal ArticleDOI
Comparison between two partial likelihood approaches for the competing risks model with missing cause of failure.
Kaifeng Lu,Anastasios A. Tsiatis +1 more
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TLDR
It is shown that the estimator for the regression coefficients based on the Dewanji partial likelihood is not only consistent and asymptotically normal, but also semiparametric efficient.Abstract:
In many clinical studies where time to failure is of primary interest, patients may fail or die from one of many causes where failure time can be right censored. In some circumstances, it might also be the case that patients are known to die but the cause of death information is not available for some patients. Under the assumption that cause of death is missing at random, we compare the Goetghebeur and Ryan (1995, Biometrika, 82, 821–833) partial likelihood approach with the Dewanji (1992, Biometrika, 79, 855–857)partial likelihood approach. We show that the estimator for the regression coefficients based on the Dewanji partial likelihood is not only consistent and asymptotically normal, but also semiparametric efficient. While the Goetghebeur and Ryan estimator is more robust than the Dewanji partial likelihood estimator against misspecification of proportional baseline hazards, the Dewanji partial likelihood estimator allows the probability of missing cause of failure to depend on covariate information without the need to model the missingness mechanism. Tests for proportional baseline hazards are also suggested and a robust variance estimator is derived.read more
Citations
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The Poisson-exponential lifetime distribution
TL;DR: The properties of the proposed distribution are discussed, including a formal proof of its probability density function and explicit algebraic formulae for its reliability and failure rate functions, quantiles and moments, including the mean and variance.
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The complementary exponential geometric distribution: Model, properties, and a comparison with its counterpart
TL;DR: A new two-parameter lifetime distribution with increasing failure rate, the complementary exponential geometric distribution, which is complementary to the exponential geometric model proposed by Adamidis and Loukas (1998).
Journal ArticleDOI
The complementary exponential power series distribution
TL;DR: In this article, the complementary exponential power series distributions, with failure rate either increasing or decreasing, were introduced, where the lifetime associated with a particular risk is not observable, rather we observe only the maximum lifetime value among all risks.
Journal ArticleDOI
Survival Analysis with Multiple Causes of Death: Extending the Competing Risks Model.
TL;DR: A model for multiple cause of death data grounded on an empirical approach that assigns weights to each cause on the death certificate is proposed, which can serve to study the burden and etiology of mortality related to each disease, particularly using Cox regression methodology.
Journal ArticleDOI
The Poisson–exponential distribution: a Bayesian approach
TL;DR: In this article, a two-parameter lifetime distribution with increasing failure rate was proposed, which arises on a latent complementary risk scenario and the properties of the proposed distribution are discussed, including a formal proof of its density function and an explicit algebraic formulae for its quantiles and survival and hazard functions.
References
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Journal ArticleDOI
The Statistical Analysis of Failure Time Data.
Journal ArticleDOI
Inference and missing data
TL;DR: In this article, it was shown that ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are missing at random and the observed data are observed at random, and then such inferences are generally conditional on the observed pattern of missing data.
Book
Analysis of Survival Data
David Cox,D. Oakes +1 more
TL;DR: In this article, the authors give a concise account of the analysis of survival data, focusing on new theory on the relationship between survival factors and identified explanatory variables and conclude with bibliographic notes and further results that can be used for student exercises.
Journal ArticleDOI
Proportional hazards tests and diagnostics based on weighted residuals
TL;DR: In this article, Chen et al. showed that a treatment effect that decreases with time can be directly visualized by smoothing an appropriate residual plot, which can be expressed as a weighted least-squares line fitted to the residual plot.
Journal ArticleDOI
Cox's Regression Model for Counting Processes: A Large Sample Study
TL;DR: In this article, the Cox regression model for censored survival data is extended to a model where covariate processes have a proportional effect on the intensity process of a multivariate counting process, allowing for complicated censoring patterns and time dependent covariates.
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