Statistics applied to clinical trials /

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Bibliographic Details
Author / Creator:Cleophas, Ton J. M.
Edition:2nd ed.
Imprint:Dordrecht ; Boston : Kluwer Academic Publishers, c2002.
Description:ix, 210 p. : ill. ; 25 cm.
Language:English
Subject:Drugs -- Testing -- Statistical methods.
Clinical trials -- Statistical methods.
Clinical trials -- Statistical methods.
Drugs -- Testing -- Statistical methods.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/4755290
Hidden Bibliographic Details
Other authors / contributors:Zwinderman, Aeilko H.
Cleophas, Toine F.
ISBN:1402005695 (alk. paper)
1402005709
Notes:Includes bibliographical references and index.
Table of Contents:
  • Preface
  • Foreword
  • Chapter 1. Hypotheses, Data, Stratification
  • 1.. General considerations
  • 2.. Two main hypotheses in drug trials: efficacy and safety
  • 3.. Different types of data: continuous data
  • 4.. Different types of data: proportions, percentages and contingency tables
  • 5.. Different types of data: correlation coefficient
  • 6.. Stratification issues
  • 7.. Randomized versus historical controls
  • 8.. Factorial designs
  • 9.. References
  • Chapter 2. The Analysis of Efficacy Data of Drug Trials
  • 1.. Overview
  • 2.. The principle of testing statistical significance
  • 3.. Unpaired T-Test
  • 4.. Null hypothesis testing of 3 or more unpaired samples
  • 5.. Three methods to test statistically a paired sample
  • 6.. Null-hypothesis testing of 3 or more paired samples
  • 7.. Paired data with a negative correlation
  • 8.. Rank testing
  • 9.. References
  • Chapter 3. The Analysis of Safety Data of Drug Trials
  • 1.. Introduction, summary display
  • 2.. Four methods to analyze two unpaired proportions
  • 3.. Chi-square to analyze more than two unpaired proportions
  • 4.. McNemar's test for paired proportions
  • 5.. Survival analysis
  • 6.. Conclusions
  • Chapter 4. Equivalence Testing
  • 1.. Introduction
  • 2.. Overview of possibilities with equivalence testing
  • 3.. Equivalence testing, a new gold standard?
  • 4.. Validity of equivalence trials
  • Chapter 5. Statistical Power and Sample Size
  • 1.. What is statistical power
  • 2.. Emphasis on statistical power rather than null-hypothesis testing
  • 3.. Power computations
  • 4.. Example of power computation using the T-Table
  • 5.. Calculation of required sample size, rationale
  • 6.. Calculations of required sample size, methods
  • 7.. Testing not only superiority but also inferiority of a new treatment (type III error)
  • 8.. References
  • Chapter 6. Interim Analyses
  • 1.. Introduction
  • 2.. Monitoring
  • 3.. Interim analysis
  • 4.. Group-sequential design of interim analysis
  • 5.. Continuous sequential statistical techniques
  • 6.. Conclusions
  • 7.. References
  • Chapter 7. Multiple Statistical Inferences
  • 1.. Introduction
  • 2.. Multiple comparisons
  • 3.. Primary and secondary variables
  • 4.. Conclusions
  • 5.. References
  • Chapter 8. Principles of Linear Regression
  • 1. Introduction
  • 2. More on paired observations
  • 3. Using statistical software for simple linear regression
  • 4. Multiple linear regression
  • 5. Another real data example of multiple linear regression
  • 6. Conclusions
  • Chapter 9. Subgroup Analysis Using Multiple Linear Regression: Confounding, Interaction, Synergism
  • 1. Introduction
  • 2. Example
  • 3. Model
  • 4. (I.) Increased precision of efficacy
  • 5. (II.) Confounding
  • 6. (III.) Interaction and synergism
  • 7. Estimation, and hypothesis testing
  • 8. Goodness-of-fit
  • 9. Selection procedures
  • 10. Conclusions
  • 11. References
  • Chapter 10. Curvilinear Regression
  • 1.. Summary
  • 2.. An example: curvilinear regression analysis of ambulatory blood pressure measurements
  • 3.. Methods, statistical model
  • 4.. Results
  • 5.. Discussion
  • 6.. References
  • Chapter 11. Meta-Analysis
  • 1.. Introduction
  • 2.. Examples
  • 3.. Clearly defined hypotheses
  • 4.. Thorough search of trials
  • 5.. Strict inclusion criteria
  • 6.. Uniform data analysis
  • 7.. Discussion, where are we now?
  • 8.. References
  • Chapter 12. Crossover Studies with Continuous Variables: Power Analysis
  • 1.. Summary
  • 2.. Introduction
  • 3.. Mathematical model
  • 4.. Hypothesis testing
  • 5.. Statistical power of testing
  • 6.. Conclusions
  • 7.. References
  • Chapter 13. Crossover Studies with Binary Responses
  • 1.. Summary
  • 2.. Introduction
  • 3.. Assessment of carryover and treatment effect
  • 4.. Statistical model for testing treatment and carryover effects
  • 5.. Results
  • 6.. Examples
  • 7.. Discussion
  • 8.. References
  • Chapter 14. Post-Hoc Analysis in Clinical Trials, a Case for Logistic Regression Analysis
  • 1.. Multivariate methods
  • 2.. Examples
  • 3.. Logistic regression equation
  • 4.. Conclusions
  • 5.. References
  • Chapter 15. Quality-of-Life Assessments in Clinical Trials
  • 1.. Summary
  • 2.. Introduction
  • 3.. Some terminology
  • 4.. Defining QOL in a subjective or objective way
  • 5.. The patients' opinion is an important independent-contributor to QOL
  • 6.. Lack of sensitivity of QOL-assessments
  • 7.. Odds ratio analysis of effects of patient characteristics on QOL data provides increased precision
  • 8.. Discussion
  • 9.. References
  • Chapter 16. Statistics for the Analysis of Genetic Data
  • 1.. Introduction
  • 2.. Some terminology
  • 3.. Genetics, genomics, proteonomics, data mining
  • 4.. Genomics
  • 5.. Conclusions
  • 6.. References
  • Chapter 17. Relationship Among Statistical Distributions
  • 1.. Summary
  • 2.. Introduction
  • 3.. Variances
  • 4.. The normal distribution
  • 5.. Null-hypothesis testing with the normal or the t-distribution
  • 6.. Relationship between the normal distribution and chi-square distribution, null-hypothesis testing with the chi-square distribution
  • 7.. Examples of data where variance is more important than mean
  • 8.. Chi-square can be used for multiple samples of data
  • 9.. Conclusions
  • 10.. References
  • Chapter 18. Statistics is Not "Bloodless" Algebra
  • 1.. Introduction
  • 2.. Statistics is fun because it proves your hypothesis was right
  • 3.. Statistical principles can help to improve the quality of the trial
  • 4.. Statistics can provide worthwhile extras to your research
  • 5.. Statistics is not like algebra bloodless
  • 6.. Statistics can turn art into science
  • 7.. Statistics for support rather than illumination?
  • 8.. Statistics can help the clinician to better understand limitations and benefits of current research
  • 9.. Limitations of statistics
  • 10.. Conclusions
  • 11.. References
  • Appendix
  • Index