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Chapter 2.2 50 p=0.002). Previous IFN therapy resulted in a signifi cantly lower chance of sustained response in patients with genotype A or D (OR 0.21 [0.07 – 0.58], p=0.003). We found no differences in the predictors of response for the two treatment groups. Performance of the model The distribution of the predicted probabilities of sustained response in genotypes A – D is shown in fi gure 1. The agreement between the predicted probabilities and the observed frequency of sustained response was good (p=0.27 by the Hosmer–Lemeshow goodness-of-fi t test). A multivariable model including the variables age, sex, ALT, HBV DNA, HBV genotype and previous interferon therapy had adequate discriminative ability as shown by an area under the receiver-operating characteristic curve (AUC) of 0.72 (95%-CI, 0.67 – 0.77). The AUC was 0.75 (95%-CI, 0.65 – 0.85), 0.65 (95%-CI, 0.55 – 0.75), 0.68 (95%-CI, 0.61 – 0.75) and 0.78 (95%-CI, 0.65 – 0.92) for genotypes A to D, respectively. After bootstrap validation, the area under the ROC curve was 0.69 (95%- CI, 0.60 – 0.77). Since the infl uence of the predictors was signifi cantly different across genotypes, a validated formula for the prediction of sustained response was generated for each HBV genotype separately. The PEG-IFN HBV Treatment Index is based on these formulas (fi gure 2). An automated calculator can be found on www.liver-gi.nl/peg-ifn. �� Figure 1. Distribution of predicted probabilities of sustained response in patients with HBV genotype A-D. Boxplots show the distribution of the predicted probabilities of sustained response, defi ned as HBeAg loss and HBV DNA
�� Factors that predict response to PEG-IFN 51 Figure 2: PEG-IFN HBV treatment index These nomograms can be used to obtain a patient-tailored predicted probability of sustained response in IFN-naïve patients with genotype A-D based on ALT, HBV DNA, sex and age. The probability of sustained response is calculated by drawing a vertical line to the top Points axis for each of the four variables ALT, HBV DNA, age and sex. The points for each variable are then summed and located on the Total points axis, and a vertical line is projected from the Total points axis to the bottom scale to get the predicted probability of sustained response. For example: a genotype C infected female (62 points), 25 years old (67 points), with serum ALT of 2.7 x ULN (25 points) and serum HBV DNA of 9.2 log10 copies/ml (40 points) has a total score of 194 points which converts to a probability of sustained response of 37%. Logistic regression formulas for HBV genotypes A-D: A: Logit(p)=1.48-0.05(sex=female)+0.04(age)+0.43(lnALT)-0.43(log HBV DNA)-1.46(previous IFN=yes) B: Logit(p)=-2.69-0.12(sex=female)+0.03(age)+0.43(lnALT)+0.01(log HBV DNA)+0.67(previous IFN=yes) C: Logit(p)=0.71+1.02(sex=female)-0.02(age)+0.43(lnALT)-0.23(log HBV DNA)+0.67(previous IFN=yes) D: Logit(p)=-2.38+1.74(sex=female)+0.04(age)+0.43(lnALT)-0.26(log HBV DNA)-1.46(previous IFN=yes)
Page 1: Statistical Models of Treatment Eff
Page 4 and 5: ISBN: 978-90-8559-069-9 © Bettina
Page 6 and 7: PROMOTIECOMMISSIE Promotor: Prof.dr
Page 9 and 10: CONTENTS Chapter 1. Introduction 11
Page 13: Cha pter 1 Introduction
Page 16 and 17: Chapter 1 14 Epidemiology Worldwide
Page 18 and 19: Chapter 1 16 Prediction models with
Page 20 and 21: Chapter 1 18 standard method fails
Page 22 and 23: Chapter 1 20 The extended non-linea
Page 24 and 25: Chapter 1 22 References 1. Blumberg
Page 27: Cha pter 2 Prediction of response t
Page 30 and 31: Chapter 2.1 28 ABSTRACT The aim of
Page 32 and 33: Chapter 2.1 30 been hepatitis B sur
Page 34 and 35: Chapter 2.1 32 HBV DNA decline The
Page 36 and 37: Chapter 2.1 34 Figure 2 continued.
Page 38 and 39: Chapter 2.1 36 Figure 4. Estimated
Page 40 and 41: Chapter 2.1 38 DNA was observed in
Page 42: Chapter 2.1 40 15. Kao JH. Role of
Page 46 and 47: Chapter 2.2 44 ABSTRACT Background:
Page 48 and 49: Chapter 2.2 46 low baseline HBV DNA
Page 50 and 51: Chapter 2.2 48 patient subgroups wa
Page 54 and 55: Chapter 2.2 52 Application of the m
Page 56 and 57: Chapter 2.2 54 PEG-IFN, the proport
Page 58 and 59: Chapter 2.2 56 References 1. Lavanc
Page 60 and 61: Chapter 2.2 58 26. Bonino F, Lau GK
Page 63 and 64: Chap ter 2.3 Prediction of the resp
Page 65 and 66: INTRODUCTION Dynamic prediction of
Page 67 and 68: Statistical analysis Dynamic predic
Page 69 and 70: Dynamic prediction of response to P
Page 77 and 78: References Dynamic prediction of re
Page 81 and 82: Cha pter 2.4 Dynamic prediction of
Page 83 and 84: INTRODUCTION Dynamic prediction of
Page 85 and 86: For the indirect approach we rewrit
Page 87 and 88: logit pi,j = logit Pr(Ri =1|visit =
Page 89 and 90: Dynamic prediction of response usin
Page 101 and 102: Table 1. Results of different stopp
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Dynamic prediction of response usin
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Dynamic prediction of response usin
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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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Chapter 2.5 118 13. Werle-Lapostoll
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Chapter 2.5 120 Greece - G Germanid
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Chap ter 3.1 Long-term clinical out
Page 129 and 130:
INTRODUCTION Glycyrrhizin for IFN-n
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Statistics Glycyrrhizin for IFN-non
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Glycyrrhizin for IFN-non responders
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Table 2. Time dependent Cox regress
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Cha pter 3.2 Discriminant analysis
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Introduction Discriminant analysis
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Discriminant analysis using a MLMM
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Estimation Discriminant analysis us
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where wi,k(y i, θ) = (k =1,...,K).
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Appendix Discriminant analysis usin
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Cha pter 4 Statistical models of ea
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Chapter 4.1 176 ABSTRACT Nucleoside
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Chapter 4.1 178 at day 1 at t =0 an
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Chapter 4.1 180 Table 1. Baseline c
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Chapter 4.1 182 Figure 1.
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Chapter 4.1 184 Figure 1. Viral dec
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Chapter 4.1 186 suppression in seru
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Chapter 4.1 188 13. Seifer M, Hamat
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Cha pter 4.2 Viral dynamics during
Page 195 and 196:
INTRODUCTION Viral dynamics during
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Viral dynamics during tenofovir the
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log HBV DNA (copies/mL) 10.0 9.0 8.
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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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