Statistics Applied to Clinical Trials

Name: Statistics Applied to Clinical Trials
Price: 27.91 USD
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ISBN: 9781402005701

ISBN-10: 1402005709

ISBN-13: 9781402005701

Edition: 3rd 2002 (Revised)

Authors: Ton J. M. Cleophas, Aeilko H. Zwinderman, Toine F. Cleophas

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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.

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



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