Common Statistical Methods for Clinical Research with SAS Examples, Third Edition, 3rd Edition

Common Statistical Methods for Clinical Research with SAS Examples, Third Edition, 3rd Edition

by Glenn Walker, Jack Shostak
Released February 2010
Publisher(s): SAS Institute
ISBN: 9781607644255

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Book description


Glenn Walker and Jack Shostak's Common Statistical Methods for Clinical Research with SAS Examples, Third Edition, is a thoroughly updated edition of the popular introductory statistics book for clinical researchers. This new edition has been extensively updated to include the use of ODS graphics in numerous examples as well as a new emphasis on PROC MIXED. Straightforward and easy to use as either a text or a reference, the book is full of practical examples from clinical research to illustrate both statistical and SAS methodology. Each example is worked out completely, step by step, from the raw data.

Common Statistical Methods for Clinical Research with SAS Examples, Third Edition, is an applications book with minimal theory. Each section begins with an overview helpful to nonstatisticians and then drills down into details that will be valuable to statistical analysts and programmers. Further details, as well as bonus information and a guide to further reading, are presented in the extensive appendices. This text is a one-source guide for statisticians that documents the use of the tests used most often in clinical research, with assumptions, details, and some tricks--all in one place.

This book is part of the SAS Press program.

Table of contents

  1. Copyright
  2. PREFACE TO THE THIRD EDITION
  3. PREFACE TO THE SECOND EDITION
  4. PREFACE TO THE FIRST EDITION
  5. 1. Introduction & Basics
    1. 1.1 Statistics—the Field
      1. Populations
      2. Samples
      3. Characterization
    2. 1.2 Probability Distributions
      1. Discrete Distributions
      2. Continuous Distributions
      3. The Central Limit Theorem
    3. 1.3 Study Design Features
      1. Controlled Studies
      2. Randomization
      3. Blinded Randomization
      4. Selection of Statistical Methods
    4. 1.4 Descriptive Statistics
    5. 1.5 Inferential Statistics
      1. Confidence Intervals
      2. Hypothesis Testing
    6. 1.6 Summary
  6. 2. Topics in Hypothesis Testing
    1. 2.1 Significance Levels
    2. 2.2 Power
    3. 2.3 One-Tailed and Two-Tailed Tests
    4. 2.4 p-Values
    5. 2.5 Sample Size Determination
      1. One-Sample Test of a Mean
      2. Two-Sample Comparison of Means
      3. Two-Sample Comparison of Proportions
    6. 2.6 Multiple Testing
      1. Multiple Comparisons of Treatment Response
      2. Multiple Response Variables
      3. Interim Analyses
        1. Pocock's Approach
        2. O'Brien-Fleming Approach
        3. Lan-DeMets α-Spending Function
      4. Multiple Studies
    7. 2.7 Summary
  7. 3. The Data Set TRIAL
    1. 3.1 Introduction
    2. 3.2 Data Collection
    3. 3.3 Creating the Data Set TRIAL
    4. 3.4 Statistical Summarization
    5. 3.5 Summary
  8. 4. The One-Sample t-Test
    1. 4.1 Introduction
    2. 4.2 Synopsis
    3. 4.3 Examples
        1. Solution
        2. SAS Analysis of Example 4.1
        3. Solution
        4. SAS Analysis of Example 4.2
    4. 4.4 Details & Notes
  9. 5. The Two-Sample t-Test
    1. 5.1 Introduction
    2. 5.2 Synopsis
    3. 5.3 Examples
        1. Solution
        2. SAS Analysis of Example 5.1
    4. 5.4 Details & Notes
  10. 6. One-Way ANOVA
    1. 6.1 Introduction
    2. 6.2 Synopsis
    3. 6.3 Examples
        1. Solution
        2. SAS Analysis of Example 6.1
    4. 6.4 Details & Notes
  11. 7. Two-Way ANOVA
    1. 7.1 Introduction
    2. 7.2 Synopsis
    3. 7.3 Examples
        1. Solution
        2. SAS Analysis of Example 7.1
        3. Solution
        4. SAS Analysis of Example 7.2
      2. "Mixed" Effects
      3. Using PROC MIXED
    4. 7.4 Details & Notes
  12. 8. Repeated Measures Analysis
    1. 8.1 Introduction
    2. 8.2 Synopsis
      1. The 'Univariate' Approach
    3. 8.3 Examples
        1. Solution
        2. SAS Analysis of Example 8.1
      2. The PROC MIXED Approach
        1. Solution
        2. SAS Analysis of Example 8.2
      3. Missing Data
        1. Solution
        2. SAS Analysis of Example 8.3
    4. 8.4 Details & Notes
  13. 9. The Crossover Design
    1. 9.1 Introduction
    2. 9.2 Synopsis
    3. 9.3 Examples
      1. Solution
      2. SAS Analysis of Example 9.1
      3. Solution
      4. SAS Analysis of Example 9.2
    4. 9.4 Details & Notes
  14. 10. Linear Regression
    1. 10.1 Introduction
    2. 10.2 Synopsis
    3. 10.3 Examples
      1. Solution
      2. SAS Analysis of Example 10.1
        1. Multiple Linear Regression
      3. Solution
      4. SAS Analysis of Example 10.2
    4. 10.4 Details & Notes
  15. 11. Analysis of Covariance
    1. 11.1 Introduction
    2. 11.2 Synopsis
    3. 11.3 Examples
      1. Solution
      2. SAS Analysis of Example 11.1
      3. SAS Analysis of Example 11.2
    4. 11.4 Details & Notes
  16. 12. The Wilcoxon Signed-Rank Test
    1. 12.1 Introduction
    2. 12.2 Synopsis
    3. 12.3 Examples
      1. Solution
      2. SAS Analysis of Example 12.1
    4. 12.4 Details & Notes
  17. 13. The Wilcoxon Rank-Sum Test
    1. 13.1 Introduction
    2. 13.2 Synopsis
    3. 13.3 Examples
      1. Solution
      2. SAS Analysis of Example 13.1
    4. 13.4 Details & Notes
  18. 14. The Kruskal-Wallis Test
    1. 14.1 Introduction
    2. 14.2 Synopsis
    3. 14.3 Examples
      1. Solution
      2. SAS Analysis of Example 14.1
    4. 14.4 Details & Notes
  19. 15. The Binomial Test
    1. 15.1 Introduction
    2. 15.2 Synopsis
    3. 15.3 Examples
      1. Solution
        1. Normal Approximation
      2. SAS Analysis of Example 15.1
    4. 15.4 Details & Notes
  20. 16. The Chi-Square Test
    1. 16.1 Introduction
    2. 16.2 Synopsis
    3. 16.3 Examples
      1. Solution
      2. SAS Analysis of Example 16.1
        1. Chi-Square Tests for the gxr Contingency Table
        2. Binomial Response with More Than Two Groups
      3. SAS Analysis of Example 16.2
        1. Multinomial Responses
      4. SAS Analysis of Example 16.3
    4. 16.4 Details & Notes
  21. 17. Fisher's Exact Test
    1. 17.1 Introduction
    2. 17.2 Synopsis
    3. 17.3 Examples
      1. Solution
      2. SAS Analysis of Example 17.1
    4. 17.4 Details & Notes
  22. 18. McNemar's Test
    1. 18.1 Introduction
    2. 18.2 Synopsis
    3. 18.3 Examples
      1. Solution
      2. SAS Analysis of Example 18.1
        1. Three Response Categories
      3. Solution
      4. SAS Analysis of Example 18.2
    4. 18.4 Details & Notes
  23. 19. The Cochran-Mantel-Haenszel Test
    1. 19.1 Introduction
    2. 19.2 Synopsis
    3. 19.3 Examples
      1. Solution
      2. SAS Analysis of Example 19.1
      3. Solution
      4. SAS Analysis of Example 19.2
    4. 19.4 Details & Notes
  24. 20. Logistic Regression
    1. 20.1 Introduction
    2. 20.2 Synopsis
      1. The Logit Model: One Covariate
      2. The Odds Ratio
      3. Model Estimation
      4. The Logit Model: Multiple Covariates
    3. 20.3 Examples
        1. Solution
        2. SAS Analysis of Example 20.1
        3. Multinomial Responses
        4. Solution
        5. SAS Analysis of Example 20.2
      2. Clustered Binomial Data
        1. Solution
        2. SAS Analysis of Example 20.3
    4. 20.4 Details & Notes
  25. 21. The Log-Rank Test
    1. 21.1 Introduction
    2. 21.2 Synopsis
    3. 21.3 Examples
      1. Solution
      2. SAS Analysis of Example 21.1
    4. 21.4 Details & Notes
  26. 22. The Cox Proportional Hazards Model
    1. 22.1 Introduction
    2. 22.2 Synopsis
    3. 22.3 Examples
      1. Solution
      2. SAS Analysis of Example 22.1
      3. Solution
      4. SAS Analysis of Example 22.2
    4. 22.4 Details & Notes
  27. 23. Exercises
    1. 23.1 Introduction
    2. 23.2 Exercises
    3. 23.3 Appropriateness of the Methods
    4. 23.4 Summary
  28. A. Probability Tables
    1. Appendix A.1 Probabilities of the Standard Normal Distribution
    2. Appendix A.2 Critical Values of the Student t-Distribution
    3. Appendix A.3 Critical Values of the Chi-Square Distribution
  29. B. Common Distributions Used in Statistical Inference
    1. B.1 Notation
    2. B.2 Properties
    3. B.3 Results
    4. B.4 Distributional Shapes
  30. C. Basic ANOVA Concepts
    1. C.1 Within- vs. Between-Group Variation
    2. C.2 Noise Reduction by Blocking
    3. C.3 Least Squares Mean (LS-mean)
  31. D. SS Types I, II, III, and IV Methods for an Unbalanced Two-Way Layout
    1. D.1 SS Types Computed by SAS
    2. D.2 How to Determine the Hypotheses Tested
      1. Type I Hypotheses
      2. Type II Hypotheses
      3. Type III Hypotheses
      4. Type IV Hypotheses
    3. D.3 Empty Cells
    4. D.4 More Than Two Treatment Groups
    5. D.5 Summary
  32. E. Multiple Comparison Methods
    1. E.1 Introduction
    2. E.2 Multiple Comparisons of Means
      1. The Tukey-Kramer Method
      2. Dunnett's Test
      3. p-Value Adjustment Methods
      4. 'Closed' Testing
    3. E.3 Multiple Comparisons of Binomial Proportions
      1. Resampling Methods
    4. E.4 Summary
  33. F. Data Transformations
    1. F.1 Introduction
    2. F.2 The Log Transformation
  34. G. SAS Code for Exercises in Chapter 23
  35. H. Finding Adjusted Alphas for Interim Analyses−SAS Examples
    1. H.1 Introduction
    2. H.2 Pocock's Approach
    3. H.3 O'Brien-Fleming Approach
    4. H.4 Lan-DeMets Cumulative α-Spending Function
    5. H.5 Lan-DeMets α-Spending Function
  36. I. Commonly Used Parameterizations in Logistic Regression Models
    1. I.1 Introduction
    2. I.2 The 'Reference' Parameterization
    3. I.3 The 'Effect' Parameterization
    4. I.4 More Than Two Nominal Levels
    5. I.5 Other Parameterizations
  37. REFERENCES

Product information

  • Title: Common Statistical Methods for Clinical Research with SAS Examples, Third Edition, 3rd Edition
  • Author(s): Glenn Walker, Jack Shostak
  • Release date: February 2010
  • Publisher(s): SAS Institute
  • ISBN: 9781607644255