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Statistics Applied to Clinical Trials

ISBN-10: 1402005709
ISBN-13: 9781402005701
Edition: 3rd 2002 (Revised)
List price: $99.00
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Description: This title explains statistical analyses of clinical trials, as well as novel issues, such as equivalence testing, interim analyses, sequential analyses and meta-analyses.

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Book details

List price: $99.00
Edition: 3rd
Copyright year: 2002
Publisher: Springer London, Limited
Publication date: 3/31/2002
Binding: E-Book 
Pages: 228
Size: 6.25" wide x 9.25" long x 0.50" tall
Weight: 0.792
Language: English

This title explains statistical analyses of clinical trials, as well as novel issues, such as equivalence testing, interim analyses, sequential analyses and meta-analyses.

Preface
Foreword
Hypotheses, Data, Stratification
General considerations
Two main hypotheses in drug trials: efficacy and safety
Different types of data: continuous data
Different types of data: proportions, percentages and contingency tables
Different types of data: correlation coefficient
Stratification issues
Randomized versus historical controls
Factorial designs
References
The Analysis of Efficacy Data of Drug Trials
Overview
The principle of testing statistical significance
Unpaired T-Test
Null hypothesis testing of 3 or more unpaired samples
Three methods to test statistically a paired sample
Null-hypothesis testing of 3 or more paired samples
Paired data with a negative correlation
Rank testing
References
The Analysis of Safety Data of Drug Trials
Introduction, summary display
Four methods to analyze two unpaired proportions
Chi-square to analyze more than two unpaired proportions
McNemar's test for paired proportions
Survival analysis
Conclusions
Equivalence Testing
Introduction
Overview of possibilities with equivalence testing
Equivalence testing, a new gold standard?
Validity of equivalence trials
Statistical Power and Sample Size
What is statistical power
Emphasis on statistical power rather than null-hypothesis testing
Power computations
Example of power computation using the T-Table
Calculation of required sample size, rationale
Calculations of required sample size, methods
Testing not only superiority but also inferiority of a new treatment (type III error)
References
Interim Analyses
Introduction
Monitoring
Interim analysis
Group-sequential design of interim analysis
Continuous sequential statistical techniques
Conclusions
References
Multiple Statistical Inferences
Introduction
Multiple comparisons
Primary and secondary variables
Conclusions
References
Principles of Linear Regression
Introduction
More on paired observations
Using statistical software for simple linear regression
Multiple linear regression
Another real data example of multiple linear regression
Conclusions
Subgroup Analysis Using Multiple Linear Regression: Confounding, Interaction, Synergism
Introduction
Example
Model
(I.) Increased precision of efficacy
(II.) Confounding
(III.) Interaction and synergism
Estimation, and hypothesis testing
Goodness-of-fit
Selection procedures
Conclusions
References
Curvilinear Regression
Summary
An example: curvilinear regression analysis of ambulatory blood pressure measurements
Methods, statistical model
Results
Discussion
References
Meta-Analysis
Introduction
Examples
Clearly defined hypotheses
Thorough search of trials
Strict inclusion criteria
Uniform data analysis
Discussion, where are we now?
References
Crossover Studies with Continuous Variables: Power Analysis
Summary
Introduction
Mathematical model
Hypothesis testing
Statistical power of testing
Conclusions
References
Crossover Studies with Binary Responses
Summary
Introduction
Assessment of carryover and treatment effect
Statistical model for testing treatment and carryover effects
Results
Examples
Discussion
References
Post-Hoc Analysis in Clinical Trials, a Case for Logistic Regression Analysis
Multivariate methods
Examples
Logistic regression equation
Conclusions
References
Quality-of-Life Assessments in Clinical Trials
Summary
Introduction
Some terminology
Defining QOL in a subjective or objective way
The patients' opinion is an important independent-contributor to QOL
Lack of sensitivity of QOL-assessments
Odds ratio analysis of effects of patient characteristics on QOL data provides increased precision
Discussion
References
Statistics for the Analysis of Genetic Data
Introduction
Some terminology
Genetics, genomics, proteonomics, data mining
Genomics
Conclusions
References
Relationship Among Statistical Distributions
Summary
Introduction
Variances
The normal distribution
Null-hypothesis testing with the normal or the t-distribution
Relationship between the normal distribution and chi-square distribution, null-hypothesis testing with the chi-square distribution
Examples of data where variance is more important than mean
Chi-square can be used for multiple samples of data
Conclusions
References
Statistics is Not "Bloodless" Algebra
Introduction
Statistics is fun because it proves your hypothesis was right
Statistical principles can help to improve the quality of the trial
Statistics can provide worthwhile extras to your research
Statistics is not like algebra bloodless
Statistics can turn art into science
Statistics for support rather than illumination?
Statistics can help the clinician to better understand limitations and benefits of current research
Limitations of statistics
Conclusions
References
Appendix
Index

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