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Robust Methods in Biostatistics


Description: Robust Methods in Biostatistics will be the first book on robust methodology to be directed specifically at biostatistics, which would benefit both fields greatly. It will allow students, biostatisticians and researchers of all levels easy access by introducing each chapters topic with the classical approach before explaining the robust alternatives. The proposal is well timed to meet a growing interest in these techniques.

Regulatory bodies and medical journals regularly raise their standards in terms of the quality of statistical analyses presented, wanting to ensure statistical methods are not misused leading to wrong conclusions. Following this trend there is a growing need for robust statistics in medical research.  Robust statistics is an extension of classical statistics that specifically takes into account that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable withy respect to changes in the data or model departures. Robust Methods in Biostatistics is the first book on robust methodology to be directed specifically at biostatistics, which would benefit both fields greatly. It allows students, biostatisticians and researchers of all levels easy access by introducing each chapters topic with the classical approach before explaining the robust alternatives.


Contents: 1 Introduction
1.1 What is Robust Statistics?
1.2 Against What is Robust Statistics Robust?
1.3 Are Diagnostic Methods an Alternative to Robust Statistics?
1.4 How do Robust Statistics Compare with Other Statistical Procedures in Practice?

2 Key Measures and Results
2.1 Introduction
2.2 Statistical Tools for Measuring Robustness Properties
2.3 General Approaches for Robust Estimation
2.4 Statistical Tools for Measuring the Robustness of Tests
2.5 General Approaches for Robust Testing

3 Linear Regression
3.1 Introduction
3.2 Estimating the Regression Parameters
3.3 Testing the Regression Parameters
3.4 Checking and Selecting the Model
3.5 Cardiovascular Risk Factors Data Example

4 Mixed Linear Models
4.1 Introduction
4.2 The MLM
4.3 Classical Estimation and Inference
4.4 Robust Estimation
4.5 Robust Inference
4.6 Checking the Model
4.7 Further Examples
4.8 Discussion and Extensions

5 Generalized Linear Models
5.1 Introduction
5.2 The GLM
5.3 A Class of M-estimators for GLMs
5.4 Robust Inference
5.5 Breastfeeding Data Example
5.6 Doctor Visits Data Example
5.7 Discussion and Extensions

6 Marginal Longitudinal Data Analysis
6.1 Introduction
6.2 The Marginal Longitudinal Data Model (MLDA) and Alternatives
6.3 A Robust GEE-type Estimator
6.4 Robust Inference
6.5 LEI Data Example
6.6 Stillbirth in Piglets Data Example
6.7 Discussion and Extensions

7 Survival Analysis
7.1 Introduction
7.2 The Cox Model
7.3 Robust Estimation and Inference in the Cox Model
7.4 The Veterans Administration Lung Cancer Data
7.5 Structural Misspecifications
7.6 Censored Regression Quantiles

Appendices
A Starting Estimators for MM-estimators of Regression Parameters
B Efficiency, LRTÁ , RAIC and RCp with Biweight Á-function for the Regression Model
C An Algorithm Procedure for the Constrained S-estimator
D Some Distributions of the Exponential Family
E Computations for the Robust GLM Estimator
E.1 FisherConsistencyCorrections
E.2 AsymptoticVariance
E.3 IRWLSAlgorithmforRobustGLM
F Computations for the Robust GEE Estimator
F.1 IRWLSAlgorithmforRobustGEE
F.2 FisherConsistencyCorrections
G Computation of the CRQ

References

Index


Author Dr Stephane Heritier NHMRC Clinical Trials Centre, University of Sydney, Australia. A senior lecturer in statistics for four years, Dr Heritier also has over a decade of research to her name, and has published numerous articles in a variety of journals. Dr Eva Cantoni Department of Econometrics, University of Geneva, Switzerland. Also a senior lecturer in statistics, Dr Cantoni has many years teaching and research experience, and written a number journal articles. Dr Samuel Copt NHMRC Clinical Trials Centre, University of Sydney, Australia. Having completed his PhD in 2004, Dr Copt has already spent a year as a lecturer and published six journal articles. He is now a visiting scholar at the University of Sydney. Professor Maria-Pia Victoria-Feser, HEC Section, University of Geneva, Switzerland. Professor Victoria-Feser has over 10 years of teaching experience and has written many journal articles.


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