Advances in Statistical Methods for the Health Sciences / SpringerLink (Online service) ; Jean-Louis Auget ; Nagraj Balakrishnan ; Mounir Mesbah ; Geert Molenberghs (2007)  

Público ISBD Título : Advances in Statistical Methods for the Health Sciences : Applications to Cancer and AIDS Studies, Genome Sequence Analysis, and Survival Analysis Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Jean-Louis Auget ; Nagraj Balakrishnan ; Mounir Mesbah ; Geert Molenberghs Editorial: Boston, MA : Birkhäuser Boston Fecha de publicación: 2007 Colección: Statistics for Industry and Technology, ISSN 2364-6241 Número de páginas: XLII, 540 p Il.: online resource ISBN/ISSN/DL: 978-0-8176-4542-7 Idioma : Inglés (eng) Palabras clave: Mathematics  Applied  mathematics  Engineering  Probabilities  Statistics  Probability  Theory  and  Stochastic  Processes  Applications  of  for  Life  Sciences,  Medicine,  Health  Sciences  Statistical  Methods Clasificación: 51 Matemáticas Resumen: Statistical methods have become increasingly important and now form integral part of research in the health sciences. Many sophisticated methodologies have been developed for specific applications and problems. This self-contained volume, an outgrowth of an "International Conference on Statistical Methods in Health Sciences," covers a wide range of topics pertaining to new statistical methods and novel applications in the health sciences. The chapters, written by leading experts in their respective fields, are thematically divided into the following areas: * Prognostic studies and general epidemiology * Pharmacovigilance * Quality of life * Survival analysis * Clustering * Safety and efficacy assessment * Clinical design * Models for the environment * Genomic analysis * Animal health This comprehensive volume will be highly useful an of great interest to the health science community as well as practitioners, researchers, and graduate students in applied probability, statistics, and biostatistics. Nota de contenido: Prognostic Studies and General Epidemiology -- Systematic Review of Multiple Studies of Prognosis: The Feasibility of Obtaining Individual Patient Data -- On Statistical Approaches for the Multivariable Analysis of Prognostic Marker Studies -- Where Next for Evidence Synthesis of Prognostic Marker Studies? Improving the Quality and Reporting of Primary Studies to Facilitate Clinically Relevant Evidence-Based Results -- Pharmacovigilance -- Sentinel Event Methods for Monitoring Unanticipated Adverse Events -- Spontaneous Reporting System Modelling for the Evaluation of Automatic Signal Generation Methods in Pharmacovigilance -- Quality of Life -- Latent Covariates in Generalized Linear Models: A Rasch Model Approach -- Sequential Analysis of Quality of Life Measurements with the Mixed Partial Credit Model -- A Parametric Degradation Model Used in Reliability, Survival Analysis, and Quality of Life -- Agreement Between Two Ratings with Different Ordinal Scales -- Survival Analysis -- The Role of Correlated Frailty Models in Studies of Human Health, Ageing, and Longevity -- Prognostic Factors and Prediction of Residual Survival for Hospitalized Elderly Patients -- New Models and Methods for Survival Analysis of Experimental Data -- Uniform Consistency for Conditional Lifetime Distribution Estimators Under Random Right-Censorship -- Sequential Estimation for the Semiparametric Additive Hazard Model -- Variance Estimation of a Survival Function with Doubly Censored Failure Time Data -- Clustering -- Statistical Models and Artificial Neural Networks: Supervised Classification and Prediction Via Soft Trees -- Multilevel Clustering for Large Databases -- Neural Networks: An Application for Predicting Smear Negative Pulmonary Tuberculosis -- Assessing Drug Resistance in HIV Infection Using Viral Load Using Segmented Regression -- Assessment of Treatment Effects on HIV Pathogenesis Under Treatment By State Space Models -- Safety and Efficacy Assessment -- Safety Assessment Versus Efficacy Assessment -- Cancer Clinical Trials with Efficacy and Toxicity Endpoints: A Simulation Study to Compare Two Nonparametric Methods -- Safety Assessment in Pilot Studies When Zero Events Are Observed -- Clinical Designs -- An Assessment of Up-and-Down Designs and Associated Estimators in Phase I Trials -- Design of Multicentre Clinical Trials with Random Enrolment -- Statistical Methods for Combining Clinical Trial Phases II And III -- SCPRT: A Sequential Procedure That Gives Another Reason to Stop Clinical Trials Early -- Models for the Environment -- Seasonality Assessment for Biosurveillance Systems -- Comparison of Three Convolution Prior Spatial Models for Cancer Incidence -- Longitudinal Analysis of Short-Term Bronchiolitis Air Pollution Association Using Semiparametric Models -- Genomic Analysis -- Are There Correlated Genomic Substitutions? -- Animal Health -- Swiss Federal Veterinary Office Risk Assessments: Advantages and Limitations of The Qualitative Method -- Qualitative Risk Analysis in Animal Health: A Methodological Example En línea: http://dx.doi.org/10.1007/978-0-8176-4542-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34547 Advances in Statistical Methods for the Health Sciences : Applications to Cancer and AIDS Studies, Genome Sequence Analysis, and Survival Analysis [documento electrónico] / SpringerLink (Online service) ; Jean-Louis Auget ; Nagraj Balakrishnan ; Mounir Mesbah ; Geert Molenberghs . - Boston, MA : Birkhäuser Boston, 2007 . - XLII, 540 p : online resource. - (Statistics for Industry and Technology, ISSN 2364-6241) . ISBN : 978-0-8176-4542-7 Idioma : Inglés (eng) Palabras clave: Mathematics  Applied  mathematics  Engineering  Probabilities  Statistics  Probability  Theory  and  Stochastic  Processes  Applications  of  for  Life  Sciences,  Medicine,  Health  Sciences  Statistical  Methods Clasificación: 51 Matemáticas Resumen: Statistical methods have become increasingly important and now form integral part of research in the health sciences. Many sophisticated methodologies have been developed for specific applications and problems. This self-contained volume, an outgrowth of an "International Conference on Statistical Methods in Health Sciences," covers a wide range of topics pertaining to new statistical methods and novel applications in the health sciences. The chapters, written by leading experts in their respective fields, are thematically divided into the following areas: * Prognostic studies and general epidemiology * Pharmacovigilance * Quality of life * Survival analysis * Clustering * Safety and efficacy assessment * Clinical design * Models for the environment * Genomic analysis * Animal health This comprehensive volume will be highly useful an of great interest to the health science community as well as practitioners, researchers, and graduate students in applied probability, statistics, and biostatistics. Nota de contenido: Prognostic Studies and General Epidemiology -- Systematic Review of Multiple Studies of Prognosis: The Feasibility of Obtaining Individual Patient Data -- On Statistical Approaches for the Multivariable Analysis of Prognostic Marker Studies -- Where Next for Evidence Synthesis of Prognostic Marker Studies? Improving the Quality and Reporting of Primary Studies to Facilitate Clinically Relevant Evidence-Based Results -- Pharmacovigilance -- Sentinel Event Methods for Monitoring Unanticipated Adverse Events -- Spontaneous Reporting System Modelling for the Evaluation of Automatic Signal Generation Methods in Pharmacovigilance -- Quality of Life -- Latent Covariates in Generalized Linear Models: A Rasch Model Approach -- Sequential Analysis of Quality of Life Measurements with the Mixed Partial Credit Model -- A Parametric Degradation Model Used in Reliability, Survival Analysis, and Quality of Life -- Agreement Between Two Ratings with Different Ordinal Scales -- Survival Analysis -- The Role of Correlated Frailty Models in Studies of Human Health, Ageing, and Longevity -- Prognostic Factors and Prediction of Residual Survival for Hospitalized Elderly Patients -- New Models and Methods for Survival Analysis of Experimental Data -- Uniform Consistency for Conditional Lifetime Distribution Estimators Under Random Right-Censorship -- Sequential Estimation for the Semiparametric Additive Hazard Model -- Variance Estimation of a Survival Function with Doubly Censored Failure Time Data -- Clustering -- Statistical Models and Artificial Neural Networks: Supervised Classification and Prediction Via Soft Trees -- Multilevel Clustering for Large Databases -- Neural Networks: An Application for Predicting Smear Negative Pulmonary Tuberculosis -- Assessing Drug Resistance in HIV Infection Using Viral Load Using Segmented Regression -- Assessment of Treatment Effects on HIV Pathogenesis Under Treatment By State Space Models -- Safety and Efficacy Assessment -- Safety Assessment Versus Efficacy Assessment -- Cancer Clinical Trials with Efficacy and Toxicity Endpoints: A Simulation Study to Compare Two Nonparametric Methods -- Safety Assessment in Pilot Studies When Zero Events Are Observed -- Clinical Designs -- An Assessment of Up-and-Down Designs and Associated Estimators in Phase I Trials -- Design of Multicentre Clinical Trials with Random Enrolment -- Statistical Methods for Combining Clinical Trial Phases II And III -- SCPRT: A Sequential Procedure That Gives Another Reason to Stop Clinical Trials Early -- Models for the Environment -- Seasonality Assessment for Biosurveillance Systems -- Comparison of Three Convolution Prior Spatial Models for Cancer Incidence -- Longitudinal Analysis of Short-Term Bronchiolitis Air Pollution Association Using Semiparametric Models -- Genomic Analysis -- Are There Correlated Genomic Substitutions? -- Animal Health -- Swiss Federal Veterinary Office Risk Assessments: Advantages and Limitations of The Qualitative Method -- Qualitative Risk Analysis in Animal Health: A Methodological Example En línea: http://dx.doi.org/10.1007/978-0-8176-4542-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34547

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Models for Discrete Longitudinal Data / Geert Molenberghs (2005)  

Público ISBD Título : Models for Discrete Longitudinal Data Tipo de documento: documento electrónico Autores: Geert Molenberghs ; SpringerLink (Online service) ; Geert Verbeke Editorial: New York, NY : Springer New York Fecha de publicación: 2005 Colección: Springer Series in Statistics, ISSN 0172-7397 Número de páginas: XXII, 687 p Il.: online resource ISBN/ISSN/DL: 978-0-387-28980-9 Idioma : Inglés (eng) Palabras clave: Mathematics  Probabilities  Statistics  Probability  Theory  and  Stochastic  Processes  Statistical  Methods  for  Life  Sciences,  Medicine,  Health  Sciences Clasificación: 51 Matemáticas Resumen: This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. The authors begin with models for the full marginal distribution of the outcome vector. This allows model fitting to be based on maximum likelihood principles, immediately implying inferential tools for all parameters in the models. At the same time, they formulate computationally less complex alternatives, including generalized estimating equations and pseudo-likelihood methods. They then briefly introduce conditional models and move on to the random-effects family, encompassing the beta-binomial model, the probit model and, in particular the generalized linear mixed model. Several frequently used procedures for model fitting are discussed and differences between marginal models and random-effects models are given attention The authors consider a variety of extensions, such as models for multivariate longitudinal measurements, random-effects models with serial correlation, and mixed models with non-Gaussian random effects. They sketch the general principles for how to deal with the commonly encountered issue of incomplete longitudinal data. The authors critique frequently used methods and propose flexible and broadly valid methods instead, and conclude with key concepts of sensitivity analysis. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The text is organized so the reader can skip the software-oriented chapters and sections without breaking the logical flow. Geert Molenberghs is Professor of Biostatistics at the Universiteit Hasselt in Belgium and has published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and the analysis of non-response in clinical and epidemiological studies. He served as Joint Editor for Applied Statistics (2001–2004) and as Associate Editor for several journals, including Biometrics and Biostatistics. He was President of the International Biometric Society (2004–2005). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society. Geert Verbeke is Professor of Biostatistics at the Biostatistical Centre of the Katholieke Universiteit Leuven in Belgium. He has published a number of methodological articles on various aspects of models for longitudinal data analyses, with particular emphasis on mixed models. Geert Verbeke is Past President of the Belgian Region of the International Biometric Society, International Program Chair for the International Biometric Conference in Montreal (2006), and Joint Editor of the Journal of the Royal Statistical Society, Series A (2005–2008). He has served as Associate Editor for several journals including Biometrics and Applied Statistics. The authors also wrote a monograph on linear mixed models for longitudinal data (Springer, 2000) and received the American Statistical Association's Excellence in Continuing Education Award, based on short courses on longitudinal and incomplete data at the Joint Statistical Meetings of 2002 and 2004. Nota de contenido: Motivating Studies -- Generalized Linear Models -- Linear Mixed Models for Gaussian Longitudinal Data -- Model Families -- The Strength of Marginal Models -- Likelihood-based Marginal Models -- Generalized Estimating Equations -- Pseudo-Likelihood -- Fitting Marginal Models with SAS -- Conditional Models -- Pseudo-Likehood -- From Subject-specific to Random-effects Models -- The Generalized Linear Mixed Model (GLMM) -- Fitting Generalized Linear Mixed Models with SAS -- Marginal versus Random-effects Models -- The Analgesic Trial -- Ordinal Data -- The Epilepsy Data -- Non-linear Models -- Pseudo-Likelihood for a Hierarchical Model -- Random-effects Models with Serial Correlation -- Non-Gaussian Random Effects -- Joint Continuous and Discrete Responses -- High-dimensional Joint Models -- Missing Data Concepts -- Simple Methods, Direct Likelihood, and Weighted Generalized Estimating Equations -- Multiple Imputation and the Expectation-Maximization Algorithm -- Selection Models -- Pattern-mixture Models -- Sensitivity Analysis -- Incomplete Data and SAS En línea: http://dx.doi.org/10.1007/0-387-28980-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35157 Models for Discrete Longitudinal Data [documento electrónico] / Geert Molenberghs ; SpringerLink (Online service) ; Geert Verbeke . - New York, NY : Springer New York, 2005 . - XXII, 687 p : online resource. - (Springer Series in Statistics, ISSN 0172-7397) . ISBN : 978-0-387-28980-9 Idioma : Inglés (eng) Palabras clave: Mathematics  Probabilities  Statistics  Probability  Theory  and  Stochastic  Processes  Statistical  Methods  for  Life  Sciences,  Medicine,  Health  Sciences Clasificación: 51 Matemáticas Resumen: This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. The authors begin with models for the full marginal distribution of the outcome vector. This allows model fitting to be based on maximum likelihood principles, immediately implying inferential tools for all parameters in the models. At the same time, they formulate computationally less complex alternatives, including generalized estimating equations and pseudo-likelihood methods. They then briefly introduce conditional models and move on to the random-effects family, encompassing the beta-binomial model, the probit model and, in particular the generalized linear mixed model. Several frequently used procedures for model fitting are discussed and differences between marginal models and random-effects models are given attention The authors consider a variety of extensions, such as models for multivariate longitudinal measurements, random-effects models with serial correlation, and mixed models with non-Gaussian random effects. They sketch the general principles for how to deal with the commonly encountered issue of incomplete longitudinal data. The authors critique frequently used methods and propose flexible and broadly valid methods instead, and conclude with key concepts of sensitivity analysis. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The text is organized so the reader can skip the software-oriented chapters and sections without breaking the logical flow. Geert Molenberghs is Professor of Biostatistics at the Universiteit Hasselt in Belgium and has published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and the analysis of non-response in clinical and epidemiological studies. He served as Joint Editor for Applied Statistics (2001–2004) and as Associate Editor for several journals, including Biometrics and Biostatistics. He was President of the International Biometric Society (2004–2005). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society. Geert Verbeke is Professor of Biostatistics at the Biostatistical Centre of the Katholieke Universiteit Leuven in Belgium. He has published a number of methodological articles on various aspects of models for longitudinal data analyses, with particular emphasis on mixed models. Geert Verbeke is Past President of the Belgian Region of the International Biometric Society, International Program Chair for the International Biometric Conference in Montreal (2006), and Joint Editor of the Journal of the Royal Statistical Society, Series A (2005–2008). He has served as Associate Editor for several journals including Biometrics and Applied Statistics. The authors also wrote a monograph on linear mixed models for longitudinal data (Springer, 2000) and received the American Statistical Association's Excellence in Continuing Education Award, based on short courses on longitudinal and incomplete data at the Joint Statistical Meetings of 2002 and 2004. Nota de contenido: Motivating Studies -- Generalized Linear Models -- Linear Mixed Models for Gaussian Longitudinal Data -- Model Families -- The Strength of Marginal Models -- Likelihood-based Marginal Models -- Generalized Estimating Equations -- Pseudo-Likelihood -- Fitting Marginal Models with SAS -- Conditional Models -- Pseudo-Likehood -- From Subject-specific to Random-effects Models -- The Generalized Linear Mixed Model (GLMM) -- Fitting Generalized Linear Mixed Models with SAS -- Marginal versus Random-effects Models -- The Analgesic Trial -- Ordinal Data -- The Epilepsy Data -- Non-linear Models -- Pseudo-Likelihood for a Hierarchical Model -- Random-effects Models with Serial Correlation -- Non-Gaussian Random Effects -- Joint Continuous and Discrete Responses -- High-dimensional Joint Models -- Missing Data Concepts -- Simple Methods, Direct Likelihood, and Weighted Generalized Estimating Equations -- Multiple Imputation and the Expectation-Maximization Algorithm -- Selection Models -- Pattern-mixture Models -- Sensitivity Analysis -- Incomplete Data and SAS En línea: http://dx.doi.org/10.1007/0-387-28980-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35157

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The Evaluation of Surrogate Endpoints / SpringerLink (Online service) ; Tomasz Burzykowski ; Geert Molenberghs ; Marc Buyse (2005)  

Público ISBD Título : The Evaluation of Surrogate Endpoints Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Tomasz Burzykowski ; Geert Molenberghs ; Marc Buyse Editorial: New York, NY : Springer New York Fecha de publicación: 2005 Colección: Statistics for Biology and Health, ISSN 1431-8776 Número de páginas: XXIV, 410 p Il.: online resource ISBN/ISSN/DL: 978-0-387-27080-7 Idioma : Inglés (eng) Palabras clave: Statistics  for  Life  Sciences,  Medicine,  Health  Sciences Clasificación: 51 Matemáticas Resumen: Both humanitarian and commercial considerations have spurred intensive search for methods to reduce the time and cost required to develop new therapies. The identification and use of surrogate endpoints, i.e., measures that can replace or supplement other endpoints in evaluations of experimental treatments or other interventions, is a general strategy that has stimulated both enthusiasm and skepticism. Surrogate endpoints are useful when they can be measured earlier, more conveniently, or more frequently than the "true" endpoints of primary interest. Regulatory agencies around the globe, particularly in the United States, Europe, and Japan, are introducing provisions and policies relating to the use of surrogate endpoints in registration studies. But how can one establish the adequacy of a surrogate? What kind of evidence is needed, and what statistical methods portray that evidence most appropriately? This book offers a balanced account on this controversial topic. The text presents major developments of the last couple of decades, together with a unified, meta-analytic framework within which surrogates can be evaluated from several angles. Methodological development is coupled with perspectives on various therapeutic areas. Academic views are juxtaposed with standpoints of scientists working in the biopharmaceutical industry as well as of colleagues from the regulatory authorities. Tomasz Burzykowski is Assistant Professor of Biostatistics at the Limburgs Universitair Centrum in Belgium. Dr. Burzykowski has published methodological work on the analysis of survey data, meta-analyses of clinical trials, and validation of surrogate endpoints. He is a co-author of numerous papers applying statistical methods to clinical data in different disease areas (cancer, cardiovascular diseases, dermatology, orthodontics). Geert Molenberghs is Professor of Biostatistics at the Limburgs Universitair Centrum in Belgium. Dr. Molenberghs published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and on the analysis of non-response in clinical and epidemiological studies. He serves as Joint Editor for Applied Statistics (2001-2004) and is President of the International Biometric Society (2004-2005). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society. Marc Buyse founded the International Drug Development Institute in 1991. He is Past President of the International Society for Clinical Biostatistics, Past President of the Quetelet Society, and Past Board Member of the Society for Clinical Trials. He is currently the Executive Director of IDDI (International Drug Development Institute) and Associate Professor of biostatistics at the Limburgs Universitair Centrum, Center for Statistics, Diepenbeek, Belgium. He has published extensively in the fields of biostatistics and oncology. His research interests include meta-analysis, surrogate endpoints, statistical detection of fraud, and the design and statistical analysis of clinical trials Nota de contenido: Setting the Scene -- Regulatory Aspects in Using Surrogate Markers in Clinical Trials -- Notation and Motivating Studies -- The History of Surrogate Endpoint Validation -- Validation Using Single-trial Data: Mixed Binary and Continuous Outcomes -- A Meta-analytic Validation Framework for Continuous Outcomes -- The Choice of Units -- Extensions of the Meta-analytic Approach to Surrogate Endpoints -- Meta-analytic Validation with Binary Outcomes -- Validation in the Case of Two Failure-time Endpoints -- An Ordinal Surrogate for a Survival True Endpoint -- A Combination of Longitudinal and Survival Endpoints -- Repeated Measures and Surrogate Endpoint Validation -- Bayesian Evaluation of Surrogate Endpoints -- Surrogate Marker Validation in Mental Health -- The Evaluation of Surrogate Endpoints in Practice: Experience in HIV -- An Alternative Measure for Meta-analytic Surrogate Endpoint Validation -- Discussion: Surrogate Endpoint Definition and Evaluation -- The Promise and Peril of Surrogate Endpoints in Cancer Research En línea: http://dx.doi.org/10.1007/b138566 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35106 The Evaluation of Surrogate Endpoints [documento electrónico] / SpringerLink (Online service) ; Tomasz Burzykowski ; Geert Molenberghs ; Marc Buyse . - New York, NY : Springer New York, 2005 . - XXIV, 410 p : online resource. - (Statistics for Biology and Health, ISSN 1431-8776) . ISBN : 978-0-387-27080-7 Idioma : Inglés (eng) Palabras clave: Statistics  for  Life  Sciences,  Medicine,  Health  Sciences Clasificación: 51 Matemáticas Resumen: Both humanitarian and commercial considerations have spurred intensive search for methods to reduce the time and cost required to develop new therapies. The identification and use of surrogate endpoints, i.e., measures that can replace or supplement other endpoints in evaluations of experimental treatments or other interventions, is a general strategy that has stimulated both enthusiasm and skepticism. Surrogate endpoints are useful when they can be measured earlier, more conveniently, or more frequently than the "true" endpoints of primary interest. Regulatory agencies around the globe, particularly in the United States, Europe, and Japan, are introducing provisions and policies relating to the use of surrogate endpoints in registration studies. But how can one establish the adequacy of a surrogate? What kind of evidence is needed, and what statistical methods portray that evidence most appropriately? This book offers a balanced account on this controversial topic. The text presents major developments of the last couple of decades, together with a unified, meta-analytic framework within which surrogates can be evaluated from several angles. Methodological development is coupled with perspectives on various therapeutic areas. Academic views are juxtaposed with standpoints of scientists working in the biopharmaceutical industry as well as of colleagues from the regulatory authorities. Tomasz Burzykowski is Assistant Professor of Biostatistics at the Limburgs Universitair Centrum in Belgium. Dr. Burzykowski has published methodological work on the analysis of survey data, meta-analyses of clinical trials, and validation of surrogate endpoints. He is a co-author of numerous papers applying statistical methods to clinical data in different disease areas (cancer, cardiovascular diseases, dermatology, orthodontics). Geert Molenberghs is Professor of Biostatistics at the Limburgs Universitair Centrum in Belgium. Dr. Molenberghs published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and on the analysis of non-response in clinical and epidemiological studies. He serves as Joint Editor for Applied Statistics (2001-2004) and is President of the International Biometric Society (2004-2005). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society. Marc Buyse founded the International Drug Development Institute in 1991. He is Past President of the International Society for Clinical Biostatistics, Past President of the Quetelet Society, and Past Board Member of the Society for Clinical Trials. He is currently the Executive Director of IDDI (International Drug Development Institute) and Associate Professor of biostatistics at the Limburgs Universitair Centrum, Center for Statistics, Diepenbeek, Belgium. He has published extensively in the fields of biostatistics and oncology. His research interests include meta-analysis, surrogate endpoints, statistical detection of fraud, and the design and statistical analysis of clinical trials Nota de contenido: Setting the Scene -- Regulatory Aspects in Using Surrogate Markers in Clinical Trials -- Notation and Motivating Studies -- The History of Surrogate Endpoint Validation -- Validation Using Single-trial Data: Mixed Binary and Continuous Outcomes -- A Meta-analytic Validation Framework for Continuous Outcomes -- The Choice of Units -- Extensions of the Meta-analytic Approach to Surrogate Endpoints -- Meta-analytic Validation with Binary Outcomes -- Validation in the Case of Two Failure-time Endpoints -- An Ordinal Surrogate for a Survival True Endpoint -- A Combination of Longitudinal and Survival Endpoints -- Repeated Measures and Surrogate Endpoint Validation -- Bayesian Evaluation of Surrogate Endpoints -- Surrogate Marker Validation in Mental Health -- The Evaluation of Surrogate Endpoints in Practice: Experience in HIV -- An Alternative Measure for Meta-analytic Surrogate Endpoint Validation -- Discussion: Surrogate Endpoint Definition and Evaluation -- The Promise and Peril of Surrogate Endpoints in Cancer Research En línea: http://dx.doi.org/10.1007/b138566 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35106

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