Download Dynamic Regression Models for Survival Data by Torben Martinussen PDF

By Torben Martinussen

ISBN-10: 144191904X

ISBN-13: 9781441919045

This publication stories and applies glossy versatile regression types for survival information with a different specialise in extensions of the Cox version and substitute types with the purpose of describing time-varying results of explanatory variables. Use of the steered versions and techniques is illustrated on genuine facts examples, utilizing the R-package timereg constructed through the authors, that is utilized in the course of the e-book with labored examples for the information units.

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Example text

Then if n n −1/2 D Xi → U i=1 it follows from the conditional multiplier central limit theorem that also n−1/2 n D Gi Xi → U, i=1 under suitably conditions (van der Vaart & Wellner, 1996) given almost every sequence of X1 , · · · , Xn . One practical use of this is that when Xi are the residuals from some regression model then it will often also be true that n−1/2 n D ˆ i → U, Gi X i=1 ˆ i are estimated based on the data, and this result can also be exwhere X panded to functional cases where for example Xi is a residual process on D[0, τ ].

We define independent filtering as the situation where the intensity of the filtered counting process is equivalent to the intensity of the underlying counting process that is not fully observed, or phrased more explicitly, that λ∗ (t) = λ(t) when C(t) = 1. Our definition is very general and encompasses the cases of primary interest, namely right-censoring and left-truncation as well as repeated combinations of these. 1 (Independent filtering) Let N ∗ be a multivariate counting process with compensator Λ∗ with respect to a filtration Ft∗ , and 52 3.

A key notion in this treatment is a generalization of counting processes, or point processes, to marked point processes, which will be introduced in the following. To a large extent we follow the exposition of marked point processes given by Br´emaud (1981), see also the recent Last & Brandt (1995). The idea is that instead of just recording the time points Tk at which specific events occur (as for the counting processes) we also observe an additional variable Zk (the response variable in the longitudinal data setting) at each time point Tk .

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Dynamic Regression Models for Survival Data by Torben Martinussen


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