SAS GRAPHICS FOR CLINICAL TRIALS BY EXAMPLE

Create industry-compliant graphs with this practical guide for professionals Analysis of clinical trial results is easier when the data is presented in a visual form. However, clinical graphs must conform to specific guidelines in order to satisfy regulatory agency requirements. If you are a program...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Harris, Kriss
Otros Autores: Watson, Richann
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [S.l.] : SAS INSTITUTE, 2020.
Temas:
SAS (Computer file)
Clinical trials > Statistical methods.
Clinical medicine > Research.
Research Design
Études cliniques > Méthodes statistiques.
Médecine clinique > Recherche.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Intro
  • Contents
  • About This Book
  • What Does This Book Cover?
  • Is This Book for You?
  • What Are the Prerequisites for This Book?
  • What Should You Know about the Examples?
  • We Want to Hear from You
  • About These Authors
  • Acknowledgments
  • Acknowledgments
  • Chapter 1: Introduction to Clinical Graphics
  • 1.1 Introduction to Output Delivery System Statistical
  • Graphics
  • 1.2 Reason Why Clinical Graphs Are Needed and What They
  • Should Convey
  • 1.3 Chapter Summary
  • References
  • Chapter 2: Using SAS to Generate Graphs for Adverse Events
  • 2.1 Introduction to Adverse Events
  • 2.2 Reports for Adverse Events
  • 2.3 Adverse Events MedDRA Coding
  • 2.4 Toxicity Grade for Adverse Events
  • 2.5 Report for Maximum Grade 1 Adverse Events by
  • Treatment Group
  • 2.6 Report for Time to Adverse Events by Treatment Group
  • 2.7 Treatment Risk Difference for Adverse Events
  • 2.8 Adverse Event Profile Plot
  • 2.9 Chapter Summary
  • References
  • Chapter 3: Using SAS to Generate Graphs for Data Collected Over Time
  • 3.1 Introduction on Findings Data
  • 3.2 Laboratory Values Over Time for Each Laboratory
  • Parameter
  • 3.3 Individual Patient Changes
  • 3.4 Mean Change from Baseline Over Time
  • 3.5 Laboratory Values Shift from Baseline to Post-Baseline
  • Plot
  • 3.6 Chapter Summary
  • References
  • Chapter 4: Using SAS to Generate Graphs for Exposure and Patient Disposition
  • 4.1 Introduction to the Napoleon Plot/Swimmer Plot
  • 4.2 Creating a Napoleon Plot/Swimmer Plot
  • 4.3 Creating a Napoleon Plot for Many Patients That
  • Highlights Dose Interruptions
  • 4.4 Chapter Summary
  • References
  • Chapter 5: Using SAS to Generate Graphs for Time-to-Event Endpoints
  • 5.1 Introduction to Time-to-Event Endpoints
  • 5.2 Survival Curves for Time-to-Event Endpoints
  • 5.3 Forest Plots from Overall Survival Hazard Ratios
  • 5.4 Chapter Summary
  • References
  • Chapter 6: Using SAS to Generate Graphs for Tumor Response
  • 6.1 Introduction on Tumor Response
  • 6.2 Assessment of Tumor Burden
  • 6.3 Measurement of Longest Diameter Over Time Per
  • Patient
  • 6.4 Waterfall Plot of Best Overall Response with Best
  • Percentage Change in Tumor Burden
  • 6.5 Spider Plot of Percentage Change from Baseline for Each
  • Patient Over Time
  • 6.6 Swimmer Plot for Overall Response During Course of
  • Study
  • 6.7 Other Types of Plots for Oncology Data
  • 6.8 Chapter Summary
  • References
  • Chapter 7: Using SAS to Generate Special Purpose Graphs
  • 7.1 Introduction to Special Purpose Graphs
  • 7.2 Interactive Graphs
  • 7.3 Venn Diagrams
  • 7.4 Chapter Summary
  • References
  • Appendix
  • Chapter 8: Building the Seemingly Impossible
  • 8.1 Introduction to Patient Profile Plot
  • 8.2 Chapter Summary
  • References
  • Chapter 9: Selecting the Ideal Style, Output Format, and Graphical Options
  • 9.1 Introduction to Styles, Output Format, and Graphical
  • Options
  • 9.2 Custom Styles

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