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E-grāmata: Statistics Applied to Clinical Trials

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  • Formāts: PDF+DRM
  • Izdošanas datums: 30-Apr-2016
  • Izdevniecība: Springer-Verlag New York Inc.
  • Valoda: eng
  • ISBN-13: 9781402046506
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Statistics Applied to Clinical TrialsPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 30-Apr-2016
  • Izdevniecība: Springer-Verlag New York Inc.
  • Valoda: eng
  • ISBN-13: 9781402046506
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The previous three editions of this book, rather than having been comprehensive, concentrated on the most relevant aspects of statistical analysis. Although well-received by students, clinicians, and researchers, these editions did not answer all of their questions. This updated and extended edition has been written to serve as a more complete guide and reference-text to students, physicians, and investigators, and, at the same time, preserves the common sense approach to statistical problem-solving of the previous editions.

In 1948 the first randomized controlled trial was published by the English Medical Research Council in the British Medical Journal. Until then, observations had been uncontrolled. Initially, trials frequently did not confirm hypotheses to be tested. This phenomenon was attributed to little sensitivity due to small samples, as well as inappropriate hypotheses based on biased prior trials. Additional flaws were being recognized and, subsequently were better accounted for: carryover effects due to insufficient washout from previous treatments, time effects due to external factors and the natural history of the condition under study, bias due to asymmetry between treatment groups, lack of sensitivity due to a negative correlation between treatment responses etc. Such flaws mainly of a technical nature have been largely implemented and lead to trials after 1970 being of significantly better quality than before. The past decade focused, in addition to technical aspects, on the need for circumspection in planning and conducting of clinical trials. As a consequence, prior to approval, clinical trial protocols are now routinely scrutinized by different circumstantial organs, including ethic committees, institutional and federal review boards, national and international scientific organizations, and monitoring committees charged with conducting interim analyses. The present book not only explains classical statistical analyses of clinical trials, but also addresses relatively novel issues, including equivalence testing, interim analyses, sequential analyses, meta-analyses, and provides a framework of the best statistical methods currently available for such purpose. This book is not only useful for investigators involved in the field of clinical trials, but also for students and physicians who wish to better understand the data of trials as published currently.

Recenzijas

From the reviews of the fourth edition:



"Readership: Students, physicians and investigators interested in statistical methods for clinical trials. This book was originally written for a course in medical statistics given in the EU sponsored program European Interuniversity Diploma of Pharmaceutical Medicine starting in the year 2000. it has been expanded and updated in order to serve as a guide and reference-text to students, physicians and investigators." (Andreas Rosenblad, International Statistical Review, Vol. 77 (3), 2009)

PREFACE xiii
FOREWORD xv
CHAPTER 1 HYPOTHESES, DATA, STRATIFICATION
1. General considerations
1(1)
2. Two main hypotheses in drug trials: efficacy and safety
2(1)
3. Different types of data: continuous data
3(5)
4. Different types of data: proportions, percentages and contingency tables
8(3)
5. Different types of data: correlation coefficient
11(2)
6. Stratification issues
13(1)
7. Randomized versus historical controls
14(1)
8. Factorial designs
15(1)
9. Conclusions
15(1)
10. References
16(1)
CHAPTER 2 THE ANALYSIS OF EFFICACY DATA OF DRUG TRIALS
1. Overview
17(1)
2. The principle of testing statistical significance
18(3)
3. The T-Value = standardized mean result of study
21(1)
4. Unpaired T-Test
22(2)
5. Null-hypothesis testing of 3 or more unpaired samples
24(1)
6. Three methods to test statistically a paired sample
25(3)
7. Null-hypothesis testing of 3 or more paired samples
28(2)
8. Paired data with a negative correlation
30(6)
9. Rank testing
36(3)
10. Conclusions
39(1)
11. References
39(2)
CHAPTER 3 THE ANALYIS OF SAFETY DATA OF DRUG TRIALS
1. Introduction, summary display
41(1)
2. Four methods to analyze two unpaired proportions
42(6)
3. Chi-square to analyze more than two unpaired proportions
48(3)
4. McNemar's test for paired proportions
51(1)
5. Survival analysis
52(2)
6. Odds ratio method for analyzing two unpaired proportions
54(3)
7. Odds ratios for 1 group, two treatments
57(1)
8. Conclusions
57(2)
CHAPTER 4 EQUIVALENCE TESTING
1. Introduction
59(2)
2. Overview of possibilities with equivalence testing
61(1)
3. Calculations
62(1)
4. Equivalence testing, a new gold standard?
63(1)
5. Validity of equivalence trials
63(1)
6. Special point: level of correlation in paired equivalence studies
64(1)
7. Conclusions
65(2)
CHAPTER 5 STATISTICAL POWER AND SAMPLE SIZE
1. What is statistical power
67(1)
2. Emphasis on statistical power rather than null-hypothesis testing
68(2)
3. Power computations
70(1)
4. Example of power computation using the T-Table
71(2)
5. Calculation of required sample size, rationale
73(1)
6. Calculations of required sample size, methods
73(3)
7. Testing not only superiority but also inferiority of a new treatment (the type III error)
76(2)
8. Conclusions
78(1)
9. References
78(1)
CHAPTER 6 INTERIM ANALYSES
1. Introduction
79(1)
2. Monitoring
79(1)
3. Interim analysis
80(3)
4. Group-sequential design of interim analysis
83(1)
5. Continuous sequential statistical techniques
83(2)
6. Conclusions
85(1)
7. References
85(2)
CHAPTER 7 MULTIPLE STATISTICAL INFERENCES
1. Introduction
87(1)
2. Multiple comparisons
87(5)
3. Multiple variables
92(3)
4. Conclusions
95(1)
5. References
95(2)
CHAPTER 8 CONTROLLING THE RISK OF FALSE POSITIVE CLINICAL TRIALS
1. Introduction
97(1)
2. Bonferroni test
98(1)
3. Least significant difference test (LSD) test
99(1)
4. Other tests for adjusting the p-values
99(1)
5. Composite endpoint procedures
100(1)
6. No adjustments at all, and pragmatic solutions
100(1)
7. Conclusions
101(1)
8. References
101(2)
CHAPTER 9 THE INTERPRETATION OF THE P-VALUES
1. Introduction
103(1)
2. Renewed attention to the interpretation of the p-values
103(1)
3. Standard interpretation of p-values
104(2)
4. Common misunderstandings of the p-values
106(1)
5. Renewed interpretations of p-values, little difference between p = 0.06 and p = 0.04
106(1)
6. The real meaning of very large p-values like p>0.95
107(1)
7. P-values larger than 0.95, examples (Table 2)
108(2)
8. The real meaning of very small p-values like p less than 0.0001 109
9. P-values smaller than 0.0001, examples (Table 3)
110(1)
10. Discussion
111(1)
11. Recommendations
111(2)
12. Conclusions
113(4)
13. References
CHAPTER 10 RESEARCH DATA CLOSER TO EXPECTATION THAN COMPATIBLE WITH RANDOM SAMPLING
1. Introduction
117(1)
2. Methods and results
118(1)
3. Discussion
119(3)
4. Conclusions
122(1)
5. References
122(3)
CHAPTER 11 PRINCIPLES OF LINEAR REGRESSION
1. Introduction
125(1)
2. More on paired observations
126(3)
3. Using statistical software for simple linear regression
129(3)
4. Multiple linear regression
132(1)
5. Multiple linear regression, example
133(4)
6. Purposes of linear regression analysis
137(1)
7. Another real data example of multiple linear regression (exploratory purpose)
138(2)
8. Conclusions
140(1)
CHAPTER 12 SUBGROUP ANALYSIS USING MULTIPLE LINEAR REGRESSION: CONFOUNDING, INTERACTION, SYNERGISM
1. Introduction
141(1)
2. Example
141(1)
3. Model
142(2)
4. (I.) Increased precision of efficacy
144(1)
5. (II.) Confounding
145(1)
6. (III.) Interaction and synergism
146(1)
7. Estimation, and hypothesis testing
147(1)
8. Goodness-of-fit
148(1)
9. Selection procedures
149(1)
10. Conclusion
149(1)
11. References
150(1)
CHAPTER 13 CURVILINEAR REGRESSION
1. Introduction
151(1)
2. Methods, statistical model
152(2)
3. Results
154(6)
4. Discussion
160(2)
5. Conclusions
162(1)
6. References
162(3)
CHAPTER 14 LOGISTIC AND COX REGRESSION, PROBLEMS WITH REGRESSION MODELING, MARKOW MODELS
1. Introduction
165(1)
2. Linear regression
165(4)
3. Logistic regression
169(2)
4. Cox regression
171(3)
5. Markow models
174(1)
6. Discussion
175(2)
7. Conclusions
177(1)
8. References
177(2)
CHAPTER 15 REGRESSION MODELING FOR IMPROVED PRECISION
1. Introduction
179(1)
2. Regression modeling for improved precision of clinical trials, the underlying mechanism
179(2)
3. Regression model for parallel-group trials with continuous efficacy data
181(1)
4. Regression model for parallel-group trials with proportions or odds as efficacy data
182(1)
5. Discussion
183(2)
6. Conclusions
185(1)
7. References
185(2)
CHAPTER 16 POST-HOC ANALYSIS IN CLINICAL TRIALS, A CASE FOR LOGISTIC REGRESSION ANALYSIS
1. Multivariate methods
187(1)
2. Examples
187(3)
3. Logistic regression equation
190(1)
4. Conclusions
191(1)
5. References
191(2)
CHAPTER 17 INTERACTION EFFECTS IN CLINICAL TRIALS
1. Introduction
193(1)
2. What exactly is interaction, a hypothesized example
193(3)
3. How to test the presence of interaction effects statistically, a real data example
196(2)
4. Additional real data examples of interaction effects
198(5)
5. Discussion
203(1)
6. Conclusions
203(1)
7. References
204(1)
CHAPTER 18 META-ANALYSIS
1. Introduction
205(1)
2. Examples
206(2)
3. Clearly defined hypotheses
208(1)
4. Thorough search of trials
208(1)
5. Strict inclusion criteria
208(1)
6. Uniform data analysis
209(8)
7. Discussion, where are we now?
217(1)
8. Conclusions
218(1)
9. References
218(1)
CHAPTER 19 CROSSOVER STUDIES WITH CONTINUOUS VARIABLES: POWER ANALYSIS
1. Introduction
219(1)
2. Mathematical model
220(1)
3. Hypothesis testing
221(2)
4. Statistical power of testing
223(3)
5. Discussion
226(1)
6. Conclusion
227(1)
7. References
228(1)
CHAPTER 20 CROSSOVER STUDIES WITH BINARY RESPONSES
1. Introduction
229(1)
2. Assessment of carryover and treatment effect
230(1)
3. Statistical model for testing treatment and carryover effects
231(1)
4. Results
232(2)
5. Examples
234(1)
6. Discussion
235(1)
7. Conclusions
236(1)
8. References
236(3)
CHAPTER 21 CROSS-OVER TRIALS SHOULD NOT BE USED TO TEST TREATMENTS WITH DIFFERENT CHEMICAL CLASS
1. Introduction
239(2)
2. Examples from the literature in which cross-over trials are correctly used
241(2)
3. Examples from the literature in which cross-over trials should not have been used
243(2)
4. Estimate of the size of the problem by review of hypertension trials published
245(1)
5. Discussion
246(1)
6. Conclusions
247(1)
7. References
248(1)
CHAPTER 22 QUALITY-OF-LIFE ASSESSMENTS IN CLINICAL TRIALS
1. Introduction
249(1)
2. Some terminology
249(2)
3. Defining QOL in a subjective or objective way
251(1)
4. The patients' opinion is an important independent-contributor to QOL
252(1)
5. Lack of sensitivity of QOL-assessments
253(1)
6. Odds ratio analysis of effects of patient characteristics on QOL data provides increased precision
254(3)
7. Discussion
257(1)
8. Conclusions
258(1)
9. References
258(3)
CHAPTER 23 STATISTICAL ANALYSIS OF GENETIC DATA
1. Introduction
261(1)
2. Some terminology
262(2)
3. Genetics, genomics, proteonomics, data mining
264(1)
4. Genomics
265(4)
5. Conclusions
269(1)
6. References
269(2)
CHAPTER 24 RELATIONSHIP AMONG STATISTICAL DISTRIBUTIONS
1. Introduction
271(1)
2. Variances
271(1)
3. The normal distribution
272(2)
4. Null-hypothesis testing with the normal or t-distribution
274(2)
5. Relationship between the normal-distribution and chi-square distribution, null-hypothesis testing with chi-square distribution
276(2)
6. Examples of data where variance is more important than mean
278(1)
7. Chi-square can be used for multiple samples of data
279(3)
8. Discussion
282(1)
9. Conclusions
283(1)
10. References
283(2)
CHAPTER 25 TESTING CLINICAL TRIALS FOR RANDOMNESS
1. Introduction
285(1)
2. Individual data available
285(6)
3. Individual data not available
291(2)
4. Discussion
293(1)
5. Conclusions
294(1)
6. References
295(2)
CHAPTER 26 CLINICAL DATA WHERE VARIABILITY IS MORE IMPORTANT THAN AVERAGES
1. Introduction
297(1)
2. Examples
297(1)
3. An index for variability in the data
298(1)
4. How to analyze variability, one sample
299(2)
5. How to analyze variability, two samples
301(1)
6. How to analyze variability, three or more samples
302(2)
7. Discussion
304(1)
8. Conclusions
305(1)
9. References
305(2)
CHAPTER 27 TESTING REPRODUCIBILITY
1. Introduction
307(1)
2. Testing reproducibility of quantitative data (continuous data)
307(3)
3. Testing reproducibility of qualitative data (proportions and scores)
310(2)
4. Incorrect methods to assess reproducibility
312(1)
5. Additional real data examples
312(4)
6. Discussion
316(1)
7. Conclusions
316(1)
8. References
317(2)
CHAPTER 28 ACCURACY OF DIAGNOSTIC TESTS
1. Introduction
319(1)
2. Overall accuracy of a qualitative diagnostic test
319(2)
3. Overall accuracy of a quantitative diagnostic test
321(2)
4. Determining the most accurate threshold for positive quantitative tests
323(4)
5. Discussion
327(1)
6. Conclusions
328(1)
7. References
328(1)
CHAPTER 29 ADVANCED ANALYSIS OF VARIANCE
1. Introduction
329(1)
2. Type II ANOVA, random effects model
330(1)
3. Type III ANOVA, mixed models
331(2)
4. Repeated measures experiments
333(2)
5. Discussion
335(1)
6. Conclusions
336(1)
7. References
336(1)
CHAPTER 30 STATISTICS IS NO "BLOODLESS" ALGEBRA
1. Introduction
337(1)
2. Statistics is fun because it proves your hypothesis was right
337(1)
3. Statistical principles can help to improve the quality of the trial
338(1)
4. Statistics can provide worthwhile extras to your research
338(1)
5. Statistics is not like algebra bloodless
339(1)
6. Statistics can turn art into science
340(1)
7. Statistics for support rather than illumination?
340(1)
8. Statistics can help the clinician to better understand limitations and benefits of current research
341(1)
9. Limitations of statistics
341(1)
10. Conclusions
342(1)
11. References
343(2)
CHAPTER 31 BIAS DUE TO CONFLICTS OF INTERESTS, SOME GUIDELINES
1. Introduction
345(1)
2. The randomized controlled clinical trial as the gold standard
345(1)
3. Need for circumspection recognized
346(1)
4. The expanding commend of the pharmaceutical industry over clinical trials
346(1)
5. Flawed procedures jeopardizing current clinical trials
347(1)
6. The good news
348(1)
7. Further solutions to the dilemma between sponsored research and the independence of science
348(2)
8. Conclusions
350(1)
9. References
350(3)
APPENDIX 353(8)
INDEX 361


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