D'Agostino Tutorials in Biostatistics, Volume 1, Statistical Methods in Clinical Studies

1., Volume 1
ISBN: 978-0-470-02366-2
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 466 Seiten, E-Book

The Tutorials in Biostatistics have become a very popularfeature of the prestigious Wiley journal, Statistics inMedicine (SIM). The introductory style and practical focus makethem accessible to a wide audience including medical practitionerswith limited statistical knowledge. This book represents thefirst of two volumes presenting the best tutorials published inSIM, focusing on statistical methods in clinical studies. Topicsinclude the design and analysis of clinical trials, epidemiology,survival analysis, and data monitoring. Each tutorial is focused ona medical problem, has been fully peer-reviewed and edited, and isauthored by leading researchers in biostatistics. Manyarticles include an appendix on the latest developments sincepublication in the journal and additional references.
This will appeal to statisticians working in medical research,as well as statistically-minded clinicians, biologists,epidemiologists and geneticists. It will also appeal to graduatestudents of biostatistics.

Autoren/Hrsg.

D'Agostino, Ralph

D'Agostino, Ralph B.

Inhaltsverzeichnis

Preface.
Preface to Volume 1.
Part I: OBSERVATIONAL STUDIES/EPIDEMIOLOGY.
1.1 Epidemiology.
Computing Estimates of Incidence, including Lifetime Risk:Alzheimer's Disease in the Framingham Study. The PracticalIncidence Estimators (PIE) Macro. (Alexa Beiser et al).
The Applications of Capture-Recapture Models to EpidemiologicalData. (Anne Chao et al).
1.2 Adjustment Methods.
Propensity Score Methods for Bias Reduction in the Comparison ofa Treatment to a Non-Randomized Control Group (Ralph B.D'Agostino Jr.).
1.3 Agreement Statistics.
Kappa Coefficients in Medical Research (Helen Chmura Kraemer etal).
1.4 Survival Models.
Survival Analysis in Observational Studies (Kate Bull and DavidJ. Spiegelhalter).
Methods for Interval-Censored Data (Jane C. Lindsey and LouiseM. Ryan).
Analysis of Binary Outcomes in Longitudinal Studies UsingWeighted Estimating Equations and Discrete-Time Survival Methods:Prevalence and Incidence of Smoking in an Adolescent Cohort (JohnB. Carlin et al).
Part II: PROGNOSTIC/CLINICAL PREDICTION MODELS.
2.1 Prognostic Variables.
Categorizing a Prognostic Variable: Review of Methods, Code forEasy Implementation and Applications to Decision-Making aboutCancer Treatments (Madhu Mazumdar and Jill R. Glassman).
2.2 Prognostic/Clinical Prediction Models.
Development of Health Risk Appraisal Functions in the Presenceof Multiple Indicators: The Framingham Study Nursing HomeInstitutionalization Model (R. B. D'Agostino et al).
Multivariable Prognostic Models: Issues in Developing Models,Evaluating Assumptions and Adequacy, and Measuring and ReducingErrors (Frank E. Harrell Jr et al).
Development of a Clinical Prediction Model for an OrdinalOutcome: The World Health Organization Multicentre Study ofClinical Signs and Etiological Agents of Pneumonia, Sepsis andMeningitis in Young Infants (Frank E. Harrell Jr. et al).
Using Observational Data to Estimate Prognosis: An Example Usinga Coronary Artery Disease Registry (Elizabeth R. DeLong et al).
Part III: CLINICAL TRIALS.
3.1 Design.
Designing Studies for Dose Response (Weng Kee Wong and Peter A.Lachenbruch.).
3.2 Monitoring.
Bayesian Data Monitoring in Clinical Trials (Peter M. Fayers etal).
3.3 Analysis.
Longitudinal Data Analysis (Repeated Measures) in ClinicalTrials (Paul S. Albert).
Repeated Measures in Clinical Trials: Simple Strategies forAnalysis Using Summary Measures (Stephen Senn et al).
Strategies for Comparing Treatments on a Binary Response withMulti-Centre Data (Alan Agresti, and Jonathan Hartzel).
A Review of Tests for Detecting a Monotone Dose-ResponseRelationship with Ordinal Response Data (Christy Chuang-Stein andAlan Agresti).