scholarly journals Nonparametric Survival Analysis with Time-Dependent Covariate Effects: A Penalized Partial Likelihood Approach

1990 ◽  
Vol 18 (1) ◽  
pp. 329-353 ◽  
Author(s):  
David M. Zucker ◽  
Alan F. Karr
Biometrika ◽  
2020 ◽  
Author(s):  
T Sit ◽  
Z Ying ◽  
Y Yu

Summary Statistical analysis on networks has received growing attention due to demand from various emerging applications. In dynamic networks, one of the key interests is to model the event history of time-stamped interactions among nodes. We model dynamic directed networks via multivariate counting processes. A pseudo partial likelihood approach is exploited to capture the network dependence structure. Asymptotic results are established. Numerical experiments are performed to demonstrate the effectiveness of our proposal.


2020 ◽  
Vol 29 (11) ◽  
pp. 3235-3248
Author(s):  
Chun Yin Lee ◽  
KF Lam

We apply a maximal likelihood ratio test for the presence of multiple change-points in the covariate effects based on the Cox regression model. The covariate effect is assumed to change smoothly at one or more unknown change-points. The number of change-points is inferred by a sequential approach. Confidence intervals for the regression and change-point parameters are constructed by a bootstrap method based on Bernstein polynomials conditionally on the number of change-points. The methods are assessed by simulations and are applied to two datasets.


Author(s):  
Giovanni Laudanno ◽  
Bart Haegeman ◽  
Daniel L Rabosky ◽  
Rampal S Etienne

Abstract The branching patterns of molecular phylogenies are generally assumed to contain information on rates of the underlying speciation and extinction processes. Simple birth–death models with constant, time-varying, or diversity-dependent rates have been invoked to explain these patterns. They have one assumption in common: all lineages have the same set of diversification rates at a given point in time. It seems likely, however, that there is variability in diversification rates across subclades in a phylogenetic tree. This has inspired the construction of models that allow multiple rate regimes across the phylogeny, with instantaneous shifts between these regimes. Several methods exist for calculating the likelihood of a phylogeny under a specified mapping of diversification regimes and for performing inference on the most likely diversification history that gave rise to a particular phylogenetic tree. Here, we show that the likelihood computation of these methods is not correct. We provide a new framework to compute the likelihood correctly and show, with simulations of a single shift, that the correct likelihood indeed leads to parameter estimates that are on average in much better agreement with the generating parameters than the incorrect likelihood. Moreover, we show that our corrected likelihood can be extended to multiple rate shifts in time-dependent and diversity-dependent models. We argue that identifying shifts in diversification rates is a nontrivial model selection exercise where one has to choose whether shifts in now-extinct lineages are taken into account or not. Hence, our framework also resolves the recent debate on such unobserved shifts. [Diversification; macroevolution; phylogeny; speciation]


Biometrics ◽  
2011 ◽  
Vol 67 (4) ◽  
pp. 1659-1665 ◽  
Author(s):  
Jakub Stoklosa ◽  
Wen-Han Hwang ◽  
Sheng-Hai Wu ◽  
Richard Huggins

1998 ◽  
Vol 28 (1) ◽  
pp. 327-361 ◽  
Author(s):  
Kazuo Yamaguchi

This paper introduces a novel extension of mover-stayer models for duration data that allows time-dependent covariates to be used for both a pair of regression equations, one that identifies the determinants of event timing and one that identifies the determinants of the probability of ultimate event nonoccurrence. Existing models intended to distinguish covariate effects on event timing from those on event nonoccurrence cannot use time-dependent covariates in the equation for the probability of ultimate event nonoccurrence. This paper applies the new model to an analysis of remarriage among American women. The analysis generally demonstrates that some covariates effect remarriage timing while others affect the probability of ultimate remarriage nonoccurrence. Some differences in patterns of remarriage between black women and white women are also reported. Theoretical implications of these findings are discussed.


2019 ◽  
Vol 37 (3) ◽  
pp. 306
Author(s):  
Suely Ruiz GIOLO ◽  
Jaqueline Aparecida RAMINELLI

In survival analysis, multiplicative and additive hazards models provide the two principal frameworks to study the association between the hazard and covariates. When these models are considered for analyzing a given survival dataset, it becomes relevant to evaluate the overall goodness-of-fit and how well each model can predict those subjects who subsequently will or will not experience the event. In this paper, this evaluation is based on a graphical representation of the Cox-Snell residuals and also on a time-dependent version of the area under the receiver operating characteristic (ROC) curve, denoted by AUC(t). A simulation study is carried out to evaluate the performance of the AUC(t) as a tool for comparing the predictive accuracy of survival models. A dataset from the Mayo Clinic trial in primary biliary cirrhosis  (PBC) of the liver is also considered to illustrate the usefulness of these tools to compare survival models formulated under distinct hazards frameworks.


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