The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform
David M Zucker - One of the best experts on this subject based on the ideXlab platform.
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on the cox model with time varying Regression Coefficients
Journal of the American Statistical Association, 2005Co-Authors: Lu Tian, David M ZuckerAbstract:In the analysis of censored failure time observations, the standard Cox proportional hazards model assumes that the Regression Coefficients are time invariant. Often, these parameters vary over time, and the temporal covariate effects on the failure time are of great interest. In this article, following previous work of Cai and Sun, we propose a simple estimation procedure for the Cox model with time-varying Coefficients based on a kernel-weighted partial likelihood approach. We construct pointwise and simultaneous confidence intervals for the Regression parameters over a properly chosen time interval via a simple resampling technique. We derive a prediction method for future patients' survival with any specific set of covariates. Building on the estimates for the time-varying Coefficients, we also consider the mixed case and present an estimation procedure for time-independent parameters in the model. Furthermore, we show how to use an integrated function of the estimate for a specific Regression coeffic...
Paul H C Eilers - One of the best experts on this subject based on the ideXlab platform.
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bayesian proportional hazards model with time varying Regression Coefficients a penalized poisson Regression approach
Statistics in Medicine, 2005Co-Authors: Philippe Lambert, Paul H C EilersAbstract:One can fruitfully approach survival problems without covariates in an actuarial way. In narrow time bins, the number of people at risk is counted together with the number of events. The relationship between time and probability of an event can then be estimated with a parametric or semi-parametric model. The number of events observed in each bin is described using a Poisson distribution with the log mean specified using a flexible penalized B-splines model with a large number of equidistant knots. Regression on pertinent covariates can easily be performed using the same log-linear model, leading to the classical proportional hazard model. We propose to extend that model by allowing the Regression Coefficients to vary in a smooth way with time. Penalized B-splines models will be proposed for each of these Coefficients. We show how the Regression parameters and the penalty weights can be estimated efficiently using Bayesian inference tools based on the Metropolis-adjusted Langevin algorithm.
Lu Tian - One of the best experts on this subject based on the ideXlab platform.
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on the cox model with time varying Regression Coefficients
Journal of the American Statistical Association, 2005Co-Authors: Lu Tian, David M ZuckerAbstract:In the analysis of censored failure time observations, the standard Cox proportional hazards model assumes that the Regression Coefficients are time invariant. Often, these parameters vary over time, and the temporal covariate effects on the failure time are of great interest. In this article, following previous work of Cai and Sun, we propose a simple estimation procedure for the Cox model with time-varying Coefficients based on a kernel-weighted partial likelihood approach. We construct pointwise and simultaneous confidence intervals for the Regression parameters over a properly chosen time interval via a simple resampling technique. We derive a prediction method for future patients' survival with any specific set of covariates. Building on the estimates for the time-varying Coefficients, we also consider the mixed case and present an estimation procedure for time-independent parameters in the model. Furthermore, we show how to use an integrated function of the estimate for a specific Regression coeffic...
Philippe Lambert - One of the best experts on this subject based on the ideXlab platform.
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bayesian proportional hazards model with time varying Regression Coefficients a penalized poisson Regression approach
Statistics in Medicine, 2005Co-Authors: Philippe Lambert, Paul H C EilersAbstract:One can fruitfully approach survival problems without covariates in an actuarial way. In narrow time bins, the number of people at risk is counted together with the number of events. The relationship between time and probability of an event can then be estimated with a parametric or semi-parametric model. The number of events observed in each bin is described using a Poisson distribution with the log mean specified using a flexible penalized B-splines model with a large number of equidistant knots. Regression on pertinent covariates can easily be performed using the same log-linear model, leading to the classical proportional hazard model. We propose to extend that model by allowing the Regression Coefficients to vary in a smooth way with time. Penalized B-splines models will be proposed for each of these Coefficients. We show how the Regression parameters and the penalty weights can be estimated efficiently using Bayesian inference tools based on the Metropolis-adjusted Langevin algorithm.
Ronald E Gangnon - One of the best experts on this subject based on the ideXlab platform.
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cluster detection of spatial Regression Coefficients
Statistics in Medicine, 2017Co-Authors: Ronald E GangnonAbstract:Popular approaches to spatial cluster detection, such as the spatial scan statistic, are defined in terms of the responses. Here, we consider a varying-coefficient Regression and spatial clusters in the Regression Coefficients. For varying-coefficient Regression, such as the geographically weighted Regression, different Regression Coefficients are obtained for different spatial units. It is often of interest to the practitioners to identify clusters of spatial units with distinct patterns in a Regression coefficient, but there is no formal statistical methodology for that. Rather, cluster identification is often ad-hoc such as by eyeballing the map of fitted Regression Coefficients and discerning patterns. In this paper, we develop new methodology for spatial cluster detection in the Regression setting based on hypotheses testing. We evaluate our methods in terms of power and coverages for true clusters via simulation studies. For illustration, our methodology is applied to a cancer mortality dataset. Copyright © 2016 John Wiley & Sons, Ltd.
