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Chapter 2.4 98 Finally some comparison of classification outcomes. In table 1 several stopping rules were applied with different cut-points. Our aim for a clinical useful stopping rule are to stop as many subjects without a true response as possible(i.e. not to stop a possible responder); therefore a high negative predictive value (NPV) and a high sensitivity (SENS) are important while the specificity (SPEC) and the positive predicted value(PPV) are of less importance. Comparing the results the direct approach especially the logistic regression using the patterns of the markers gives the best option. Figure 6. The estimated calibration slope for the different methods by visit week. DISCUSSION Dynamic updates of prediction of treatment response are not done routinely when new information of longitudinal biomarkers becomes available. In this paper dynamic prediction methods for the case of a binary outcome are presented and compared. We introduce a direct approach extending the logistic regression model with the observed marker values along with time. We elaborate on the direct approach extending with a set of parameters describing the patterns of the biomarkers over time. In the indirect approach the distribution of the markers estimated by linear
Table 1. Results of different stopping rules with a selection of different classifi cation cutoff’s of the predicted response Classifi cation Stop during Rx Stop week 24 Method Cutoff % stop PPV NPV SENS SPEC % stop PPV NPV SENS SPEC direct method 0.1 12.0 36.4 73.3 90.9 13.6 8.0 36.5 80.0 95.5 9.9 by observed 0.15 17.6 35.9 68.2 84.1 18.5 24.0 41.1 83.3 88.6 30.9 markers 0.2 26.4 37.0 69.7 77.3 28.4 31.2 45.3 87.2 88.6 42.0 direct method 0.1 13.6 38.0 82.4 93.2 17.3 11.2 37.8 85.7 95.5 14.8 by patterns of 0.15 16.8 39.4 85.7 93.2 22.2 24.8 42.6 87.1 90.9 33.3 markers 0.2 24.0 41.1 83.3 88.6 30.9 36.8 49.4 89.1 88.6 50.6 indirect method 0.001 44.0 50.0 83.6 79.5 56.8 22.4 39.2 78.6 86.4 27.2 marginal 0.01 59.2 54.9 78.4 63.6 71.6 52.8 52.5 80.3 70.5 65.4 0.05 70.4 56.8 73.9 47.7 80.2 63.2 52.2 74.7 54.5 72.8 Dynamic prediction of response using longitudinal profi les 99 indirect method 0.001 48.8 51.6 82.0 75.0 61.7 23.2 39.6 79.3 86.4 28.4 conditional 0.01 64.8 59.1 77.8 59.1 77.8 57.6 52.8 77.8 63.6 69.1 0.05 75.2 38.7 66.0 27.3 76.5 68.8 53.8 73.3 47.7 77.8 indirect method 0.1 18.4 38.2 78.3 88.6 22.2 0.0 35.2 0.0 100.0 0.0 random 0.15 20.8 39.4 80.8 88.6 25.9 1.6 34.1 0.0 95.5 0.0 effect 0.2 33.6 42.2 78.6 79.5 40.7 14.4 36.4 72.2 88.6 16.0 0.3 44.0 47.1 80.0 75.0 54.3 38.4 45.5 81.3 79.5 48.1
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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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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 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
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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Discriminant analysis using a MLMM
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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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