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Chapter 2.4 90 more or less no change from baseline load. Differences in ALT-behavior are less pronounced. Figure 2. Observed HBV DNA and ALT during treatment (week 0-52) and during follow-up (week 52-78) by response, together with the marginal predicted means.
Dynamic prediction of response using longitudinal profi les 91 Results of the longitudinal prediction with the direct approach First the result of the logistic regression method is presented. In a previous study13,14 of the response to therapy a prognostic model of baseline factors were designed and validated, and a alongside prognostic indexes (PI0) were calculated (as described in Section 3). In this updating pooled logistic regression model the subject specific PI’s are used as offsets. The final model to predict response using the data of all patients, n=125 and the repeated measurements of the load and the ALT the first 32 weeks of therapy is logit pi,j = logit Pr(Ri =1|visit week = ti,j, loadi,j,ln(alt)i,j, offset = PIi,0) = α + βT ti,j + βloadloadi,j + βaltln(alti,j)+PIi,0 This model was obtained by comparing Akaike’s Information Criterium (AIC’s), the log likelihood and the Area Under the receiver operating Curve (AUC); i.e. the cstatistic of different models, Steyerberg, chapter 11 and 16. 11 The model did not improve by adding B-splines (Steyerberg, chapter 12) 11 of load, of ALT or of time, neither by adding interaction terms with baseline variables nor by extending with the load or the ALT of the previous visit. For individual prediction of the response for subject i, the model above was refitted leaving out subject i. This gives a cross-validated prediction for each patient. To illustrate the results of the fitted model the individual prediction of 3 typical subjects are plotted in figure 3. Figure 3.a is a responder with an early drop of HBV DNA around week 16 and a steady decline of ALT to normal-levels, the baseline prediction of response was 62%. The dynamic prediction of response changes to around 90% after week 16. Two non-responders are plotted in 3.b and 3.c. The subject in 3.b had at baseline already a low prediction of response around 10%, the load declines 0.5 log the first 20 weeks and only from week 24 a decline is seen, the prediction of response changes little over time and stays around 10%. In 3.c the subject with genotype C experiences a sudden drop of HBV DNA at week 12 just to return to baseline levels at the next visit. This pattern is not often seen (in this specific case probably an error occurred at the laboratory, however this could not be confirmed) but may illustrate what happens in the case where a dose-reduction is anticipated or the subject does not adhere to treatment. However the direct prediction approach with the observed markers do not seem to suffer from this; the prediction of response gave an increase of response at week 12 and a decrease afterwards. The individual predictions of response over time for all subjects are plotted in figure 4 separate for non-responders and responders.
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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
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 52 and 53: Chapter 2.2 50 p=0.002). Previous I
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
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Page 101 and 102: Table 1. Results of different stopp
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Page 108 and 109: Chapter 2.5 106 ABSTRACT Peginterfe
Page 110 and 111: Chapter 2.5 108 and placebo daily.
Page 112 and 113: Chapter 2.5 110 for patients with a
Page 114 and 115: Chapter 2.5 112 Mean HBV DNA declin
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
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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
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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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