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Chapter 3.2 156 Figure 1. Dutch PBC Study. Observed evolution (in grey) of serum bilirubin, albumin and alkaline phosphatase levels for Group 0, patients who were alive without liver transplantation at time T = 10 years (left panel) and for Group 1, patients who encountered either liver related death or liver transplantation by time T = 10 years (right panel). Black solid lines show fi tted mean profi les of a patient with median values of included covariates.
Discriminant analysis using a MLMM with a normal mixture 157 a random intercept and time effects (time in months). That is, each matrix Z g i,r contains an intercept column and the column of the times at which the markers have been measured. Hence, in each prognostic group, there are 6-dimensional random effects. Weakly informative priors as described in Section ’Prior distributions’ have been used for the model parameters. Models with K = 1 and K = 2 mixture components have been fitted for comparative purposes. The reported results are based on 10 000 iterations of 1:10 thinned MCMC after a burn-in period of 5 000 iterations. MCMC sampling took about 120s for Group 0 and about 30s for Group 1 on an Intel Core 2 Duo 3 GHz CPU with 3.25 GB RAM. Convergence of the MCMC was evaluated using the R package coda (Plummer et al. 26 ). Tables 1 and 2 show posterior summary statistics for the fixed effects, means and standard deviations of the random effects and standard deviations of the error terms in models with K = 2 mixture components. The results for models with K =1are similar. The mean trajectories for a patient with median values of the covariates and based on the posterior medians of the model parameters from Table 1 are shown as black lines in Figure 1. The fitted mean profiles confirm differences between both prognostic groups and our earlier conclusions taken from the observed longitudinal profiles. The fact that the observed profiles differ from the fitted mean profiles more considerably in Group 1 than in Group 0 is seen also from the posterior medians of Table 1. Dutch PBC Study. Posterior summary statistics for fi xed effects (α) and means of random effects (β) in models with K = 2. Group 0 Group 1 Posterior Median 95% HPD Interval Posterior Median 95% HPD Interval Bilirubin intercept 1.13 (1.07, 1.20) -2.48 (-3.02, -1.98) time (months) 0.77 · 10-3 (-0.02, 1.61) · 10-3 0.11 (0.06, 0.17) age (years) -0.78 · 10-2 (-0.87, -0.69) · 10-2 0.056 (0.049, 0.062) UDCA dosis (mg/day) -0.59 · 10-4 (-1.00, 0.12) · 10-4 0.85 · 10-3 (0.34, 1.34) · 10-3 Albumin intercept 1.26 (1.21, 1.31) 1.27 (1.13, 1.40) time (months) -0.30 · 10-3 (-0.50, -0.10) · 10-3 -0.33 · 10-2 (-0.54, -0.12) · 10-2 age (years) -0.11 · 10-2 (-0.27, 0.05) · 10-2 -0.47 · 10-2 (-0.93, 0.00) · 10-2 UDCA dosis (mg/day) -0.35 · 10-4 (-0.96, 0.24) · 10-3 -0.07 · 10-5 (-19.41, 18.63) · 10-5 Alkal. phosph. intercept 3.66 (3.48, 3.84) 7.38 (6.74, 8.00) time (months) -0.50 · 10-2 (-0.61, -0.38) · 10-2 -0.021 (-0.042, -0.003) age (years) -0.30 · 10-1 (-0.32, -0.29) · 10-1 -0.053 (-0.058, -0.046) UDCA dosis (mg/day) 0.30 · 10-4 (-0.52, 1.12) · 10-4 -0.15 · 10-2 (-0.19, -0.11) · 10-2
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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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Page 116 and 117: Chapter 2.5 114 peginterferon and n
Page 118 and 119: Chapter 2.5 116 hepatocellular carc
Page 120 and 121: Chapter 2.5 118 13. Werle-Lapostoll
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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
Page 133 and 134: Glycyrrhizin for IFN-non responders
Page 135 and 136: Table 2. Time dependent Cox regress
Page 143 and 144: Cha pter 3.2 Discriminant analysis
Page 145 and 146: Introduction Discriminant analysis
Page 147 and 148: Discriminant analysis using a MLMM
Page 149 and 150: Estimation Discriminant analysis us
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Page 171 and 172: Appendix Discriminant analysis usin
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Page 178 and 179: Chapter 4.1 176 ABSTRACT Nucleoside
Page 180 and 181: Chapter 4.1 178 at day 1 at t =0 an
Page 182 and 183: Chapter 4.1 180 Table 1. Baseline c
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Page 186 and 187: Chapter 4.1 184 Figure 1. Viral dec
Page 188 and 189: Chapter 4.1 186 suppression in seru
Page 190 and 191: Chapter 4.1 188 13. Seifer M, Hamat
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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Chap ter 4.3 Modelling of early vir
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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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