Statistics applied to clinical trials /
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Author / Creator: | Cleophas, Ton J. M. |
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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 |
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