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Chapter 2.4 80 ABSTRACT Dynamic updating of the prediction of a significant clinical event is essential for the individual patient when new information becomes available. If the patterns of longitudinal markers change during follow-up along changes the clinical prognosis. Our aim is to incorporate these longitudinal profiles in a dynamic way to repeatedly update the individual prediction of the event. The general concept is presented specifically in the logistic regression setup when the clinical event is a binary outcome. We introduce a newly developed method and elaborate on existing ones. A new direct approach is proposed extending the usual logistic regression of baseline variables with the observed repeated measurements of the markers. The model is designed to update the prognosis of the outcome each time new information becomes available. An other direct approach using the behavior of the markers over time is discussed. Proceeding in this way first linear mixed modeling is applied to fit the subject specific patterns of the markers and afterwards the random effects are entered in the logistic regression while adjusting for the estimation error of the random effects. We finally apply an indirect prediction method using multivariate mixed effects models. The patterns of the markers are allowed to vary depending on the outcome variable. Thereafter, the empirical Bayes estimates are used to obtain posterior probabilities that are subsequently used to update the probability of the outcome variable each time new information becomes available. The different methods are illustrated with data on treatment of chronic hepatitis B patients and an extensive comparison of the performance of the different methods is made. We conclude that the prediction of response obtained at baseline can be significantly improved with the above mentioned methods and may be useful tools to update the prognosis for the individual patient. The direct approach is easy in use and furthermore performed best in our application.
INTRODUCTION Dynamic prediction of response using longitudinal profi les 81 Dynamic updating of an individuals prediction of a specific outcome based on new gathered information is not routinely implemented in prediction models, but can be of great importance for the individual patient, for the clinician and the further choice of treatment. We shall introduce the general problem in the setting of therapeutic treatment of chronic hepatitis B. In the last years treatment options for chronic hepatitis B have largely been extended. Still the virus is very difficult to eliminate and only 10-36% of the treated patients remains in remission after therapy. 1,2 Peg-interferon (PEG-IFN) has proven effective, but also has its limitations regarding multiple and possible serious side-effects. 3 However, it has been demonstrated that the response to a course of interferon is durable and leads to both improved survival and reduction of the incidence of hepatocellular carcinoma. 3,4 Therefore, prediction of response to PEG-IFN is of great importance. During therapy the patient is monitored at frequently scheduled followup visits and several markers are measured to anticipate continuation. Different patterns of these markers have been described, ter Borg et al. 5 , and also flares (sudden increase) of markers have been identified as possible predictors of response, Flink et al. 6 Up to now however, these measurements have not been implemented routinely to update the individual prediction of response. These may even proof helpful in guiding and supporting the patient through the long treatment and identify patients who will have little benefit by continuation of treatment. The above sketched situation is the inspiration of this paper. Our aim is to develop a dynamic prediction model to update the prognosis of the individual patient dependent on the new information that becomes available after each follow-up visit. The statistical challenge is to find a powerful tool to make use of the multivariate longitudinal profiles to predict the binary outcome variable. We introduce a new method and elaborate on existing methods. The performance of the different methods are furthermore extensively compared. Two different views are considered: (1) directly modeling the prediction of the outcome variable with the use of logistic regression techniques with repeated updates and (2) indirectly classifying individuals into an outcome category over time using Bayes’ theorem. For the direct approach (1) either the observed marker value or the subject specific pattern of the marker is used as predictor. We suggest a GEE solution directly entering the observed marker values. 7 In contrast, Maruyama et al. 8 first fitted a linear mixed effect model to obtain the subject specific patterns of the longitudinal markers and then entered the estimated random effects in a logistic regression. Since the estimated random effects have measurement errors an adjustment of the
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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
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 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: Cha pter 2.4 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
Page 105: Dynamic prediction of response usin
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
Page 122: Chapter 2.5 120 Greece - G Germanid
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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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
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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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