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Chapter 3.2 168 32. Tsiatis AA, Davidian M. Joint modeling of longitudinal and time-to-event data: An overview. Statistica Sinica 2004;14:809-834. 33. Jackson CH, Sharples LD. Hidden Markov models for the onset and progression of bronchiolitis obliterans syndrome in lung transplant recipients. Statistics in Medicine 2002;21:113- 128. 34. De la Cruz-Mesía R, Quintana FA, Marshall G. Model-based clustering for longitudinal data. Computational Statistics and Data Analysis 2008;52:1441-1457.
Appendix Discriminant analysis using a MLMM with a normal mixture 169 We briefly show here how to run the MCMC sampling and perform subsequent discrimination with the PBC data in R using the extended version of the R package mixAK (Komárek14 ). It is assumed that the training data for both groups are stored in data.frames called Data0 and Data1 whose structure is depicted in Table 4. Note that it is not necessary that at each visit, measurements of all three markers have been taken. For example, for patient id=1, the bilirubin value at his last visit at time 119.69 is unknown. Nevertheless, the values of albumin and alkaline phosphatase obtained at this visit are still used in the estimation of the mixed model. The MCMC algorithm to obtain a sample from the posterior distribution of model parameters in Group 0 in a model with K = 2 mixture components (specified within the argument prior.b) is run using the following code. The sampled values and additional information are stored in an object mod0. ¿ library(”mixAK”) ¿ mod0 ¡- GLMM˙MCMC(y = Data0[, c(”bili”, ”albu”, ”lap”)], dist = c(”gaussian”, ”gaussian”, ”gaussian”), id = Data0[, ”id”], x = list(bili = Data0[, c(”age”, ”dosis”)], albu = Data0[, c(”age”, ”dosis”)], lap = Data0[, c(”age”, ”dosis”)]), z = list(bili = Data0[, ”time”], albu = Data0[, ”time”], lap = Data0[, ”time”]), random.intercept = c(bili = TRUE, albu = TRUE, lap = TRUE), prior.b = list(Kmax = 2), nMCMC = c(burn = 5000, keep = 10000, thin = 10, info = 500)) Basic summary statistics of the posterior distribution can be seen (output not shown) with ¿ print(mod0) Similarly, the sample from the posterior distribution of model parameters in Group 1 can be obtained and stored in an object mod1. Further suppose that the values of the observed longitudinal markers and related covariates for new patients are stored in a data.frame DataNew which has the same structure as shown in Table 4. The values of Pg,i(τ) fori∈ group of new patients
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Statistical Models of Treatment Eff
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ISBN: 978-90-8559-069-9 © Bettina
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PROMOTIECOMMISSIE Promotor: Prof.dr
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CONTENTS Chapter 1. Introduction 11
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Cha pter 1 Introduction
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Chapter 1 14 Epidemiology Worldwide
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Chapter 1 16 Prediction models with
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Chapter 1 18 standard method fails
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Chapter 1 20 The extended non-linea
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Chapter 1 22 References 1. Blumberg
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Cha pter 2 Prediction of response t
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Chapter 2.1 28 ABSTRACT The aim of
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Chapter 2.1 30 been hepatitis B sur
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Chapter 2.1 32 HBV DNA decline The
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Chapter 2.1 34 Figure 2 continued.
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Chapter 2.1 36 Figure 4. Estimated
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Chapter 2.1 38 DNA was observed in
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Chapter 2.1 40 15. Kao JH. Role of
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Chapter 2.2 44 ABSTRACT Background:
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Chapter 2.2 46 low baseline HBV DNA
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Chapter 2.2 48 patient subgroups wa
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Chapter 2.2 50 p=0.002). Previous I
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Chapter 2.2 52 Application of the m
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Chapter 2.2 54 PEG-IFN, the proport
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Chapter 2.2 56 References 1. Lavanc
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Chapter 2.2 58 26. Bonino F, Lau GK
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Chap ter 2.3 Prediction of the resp
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INTRODUCTION Dynamic prediction of
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Statistical analysis Dynamic predic
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Dynamic prediction of response to P
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References Dynamic prediction of re
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Cha pter 2.4 Dynamic prediction of
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INTRODUCTION Dynamic prediction of
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For the indirect approach we rewrit
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logit pi,j = logit Pr(Ri =1|visit =
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Dynamic prediction of response usin
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Table 1. Results of different stopp
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Chapter 2.5 106 ABSTRACT Peginterfe
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Chapter 2.5 108 and placebo daily.
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Chapter 2.5 110 for patients with a
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Chapter 2.5 112 Mean HBV DNA declin
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Chapter 2.5 114 peginterferon and n
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Chapter 2.5 116 hepatocellular carc
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Page 127 and 128: Chap ter 3.1 Long-term clinical out
Page 129 and 130: INTRODUCTION Glycyrrhizin for IFN-n
Page 131 and 132: Statistics Glycyrrhizin for IFN-non
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Page 135 and 136: Table 2. Time dependent Cox regress
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Page 145 and 146: Introduction Discriminant analysis
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Page 149 and 150: Estimation Discriminant analysis us
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Page 186 and 187: Chapter 4.1 184 Figure 1. Viral dec
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Page 193 and 194: Cha pter 4.2 Viral dynamics during
Page 195 and 196: INTRODUCTION Viral dynamics during
Page 197 and 198: Viral dynamics during tenofovir the
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Page 211 and 212: Chap ter 4.3 Modelling of early vir
Page 213 and 214: INTRODUCTION HBV viral kinetics dur
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HBV viral kinetics during PEG-IFN 2
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SUMMARY AND CONCLUSION Summary and
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Summary and conclusion 233 The mode
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Summary and conclusion 235 The infe
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Summary and conclusion 237 Early st
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SAMENVATTING EN CONCLUSIE Samenvatt
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Samenvatting en conclusie 243 Er we
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Samenvatting en conclusie 245 Data
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Samenvatting en conclusie 247 Behan
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Dan kwoord
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Dankwoord 254 Lieve Marion en Margr
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BIBLI OGRAPHY Bibliography 257 1. K
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Bibliography 259 Interferon-ribavir
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Bibliography 261 Long term clinical
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Bibliography 263 Flares in chronic
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Bibliography 265 Genetic characteri
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Bibliography 267 106. Reijnders JG,
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