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Dynamic Prediction in Clinical Survival Analysis

By Hans van Houwelingen, Hein Putter

CRC Press – 2012 – 250 pages

Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

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    978-1-43-983533-3
    November 8th 2011

Description

There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on the Cox proportional hazards model and to present those as static models for remaining lifetime after diagnosis or treatment. In contrast, Dynamic Prediction in Clinical Survival Analysis focuses on dynamic models for the remaining lifetime at later points in time, for instance using landmark models.

Designed to be useful to applied statisticians and clinical epidemiologists, each chapter in the book has a practical focus on the issues of working with real life data. Chapters conclude with additional material either on the interpretation of the models, alternative models, or theoretical background. The book consists of four parts:

  • Part I deals with prognostic models for survival data using (clinical) information available at baseline, based on the Cox model
  • Part II is about prognostic models for survival data using (clinical) information available at baseline, when the proportional hazards assumption of the Cox model is violated
  • Part III is dedicated to the use of time-dependent information in dynamic prediction
  • Part IV explores dynamic prediction models for survival data using genomic data

Dynamic Prediction in Clinical Survival Analysis summarizes cutting-edge research on the dynamic use of predictive models with traditional and new approaches. Aimed at applied statisticians who actively analyze clinical data in collaboration with clinicians, the analyses of the different data sets throughout the book demonstrate how predictive models can be obtained from proper data sets.

Reviews

"It offers several original viewpoints that make it a worthwhile addition to the literature. … For the researcher wishing to gain knowledge of survival analysis beyond that of standard introductions, this is an excellent book. It contains a lot of very useful procedures and demonstrates them in practical applications on real data from the authors’ own experience. The datasets are described in Appendix A and most of the data are available from the package dynpred (Appendix B), which also contains suitable software. On the book’s website one may find R code for each chapter in the book; this is a highly useful feature. Output and plots are also available, which makes the book useful for teaching purposes."

—Odd O. Aalen, Journal of the American Statistical Association, September 2014, Vol. 109

Contents

Prognostic models for survival data using (clinical) information available at baseline, based on the Cox model

The special nature of survival data

Introduction

Basic statistical concepts

Predictive use of the survival function

Additional remarks

Cox regression model

The hazard function

The proportional hazards model

Fitting the Cox model

Example: Breast Cancer II

Extensions of the data structure

Alternative models

Additional remarks

Measuring the predictive value of a Cox model

Introduction

Visualizing the relation between predictor and survival

Measuring the discriminative ability

Measuring the prediction error

Dealing with overfitting

Cross-validated partial likelihood

Additional remarks

Calibration and revision of Cox models

Validation by calibration

Internal calibration

External calibration

Model revision

Additional remarks

Prognostic models for survival data using (clinical) information available at baseline, when the proportional hazards assumption of the Cox model is violated

Mechanisms explaining violation of the Cox model

The Cox model is just a model

Heterogeneity

Measurement error in covariates

Cause specific hazards and competing risks

Additional remarks

Non-proportional hazards models

Cox model with time-varying coefficients

Models inspired by the frailty concept

Enforcing parsimony through reduced rank models

Additional remarks

Dealing with non-proportional hazards

Robustness of the Cox model

Obtaining dynamic predictions by landmarking

Additional remarks

Dynamic prognostic models for survival data using time-dependent information

Dynamic predictions using biomarkers

Prediction in a dynamic setting

Landmark prediction model

Application

Additional remarks

Dynamic prediction in multi-state models

Multi-state models in clinical applications

Dynamic prediction in multi-state models

Application

Additional remarks

Dynamic prediction in chronic disease

General description

Exploration of the EORTC breast cancer data set

Dynamic prediction models for breast cancer

Dynamic assessment of "cure"

Additional remarks

Dynamic prognostic models for survival data using genomic data

Penalized Cox models

Introduction

Ridge and lasso

Application to Data Set 3

Adding clinical predictors

Additional remarks

Dynamic prediction based on genomic data

Testing the proportional hazards assumption

Landmark predictions

Additional remarks

Appendices

Data sets

Advanced ovarian cancer

Chronic Myeloid Leukemia (CML)

Breast Cancer I (NKI)

Gastric Cancer

Breast Cancer II (EORTC)

Acute Lymphatic Leukemia (ALL)

B Software and website

R packages used

The dynpred package

Additional remarks

References

Index

Author Bio

Hans van Houwelingen received his Ph.D. in Mathematical Statistics from the University of Utrecht in 1973. He stayed at the Mathematics Department in Utrecht until 1986. In that time his theoretical research interest was empirical Bayes methodology as developed by Herbert Robbins. His main contribution was the finding that empirical Bayes rules could be improved by monitonization.

On the practical side, he was involved in all kinds of collaborations with researchers in psychology, chemistry and medicine. The latter brought him to Leiden in 1986 where he was appointed chair and department head of Medical Statistics at the Leiden Medical School, which was transformed into the Leiden University Medical Center (LUMC) in 1996.

Together with his Ph.D. students he developed several research lines in logistic regression, survival analysis, meta-analysis, statistical genetics and statistical bioinformatics. In the meantime, the department grew into the Department of Medical Statistics and Bioinformatics, which also includes the chair and staff in Molecular Epidemiology.

Dr. van Houwelingen was editor-in-chief of Statistica Neerlandica and served on the editorial boards of Statistical Methods In Medical Research, Lifetime Data Analysis, Biometrics, Biostatistics, Biometrical Journal and Statistics and Probability Letters. He is elected member of ISI, fellow of ASA, honorary member of the Dutch Statistical Society (VVS) and ANed, the Dutch Region of the International Biometric Society (IBS).

Dr. van Houwelingen retired on January 1, 2009. On that occasion he was appointed Knight in the Order of the Dutch Lion.

Hein Putter received his Ph.D. in mathematical statistics from the University of Leiden in 1994, under the supervision of Willem van Zwet, on the topic of resampling methods. After post-doc positions in the Department of Mathematics of the University of Amsterdam and the Free University Amsterdam, and at the Statistical Laboratory of the University of Cambridge, he turned to medical statistics in 1998, working for the HIV Monitoring Fund and the International Antiviral Therapy Evaluation Center (IATEC), based at the Amsterdam Medical Center. In 2000, Dr. Putter was appointed assistant professor in the Department of Medical Statistics and Bioinformatics of the Leiden University Medical Center.

Dr. Putter’s research interests include: statistical genetics, dynamical models in HIV and survival analysis—in particular competing risks and multi-state models. Dr. Putter collaborates closely with the Department of Surgery and the Department of Oncology of the LUMC, and with international organizations like the European Organisation for the Research and Treatment of Cancer (EORTC) and the European Group for Blood and Marrow Transplantation (EBMT). He serves as associate editor of Statistics and Probability Letters and Statistics in Medicine, and he was guest editor of special issues of Biometrical Journal and Journal of Statistical Software. He was one of the initiators of the IBS Channel Network.

In 2010, Dr. Putter was appointed full professor in the Department of Medical Statistics and Bioinformatics of the LUMC.

Name: Dynamic Prediction in Clinical Survival Analysis (Hardback)CRC Press 
Description: By Hans van Houwelingen, Hein Putter. There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on...
Categories: Statistical Computing, Statistical Theory & Methods, Statistics for the Biological Sciences