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Design and Analysis of Clinical Experiments (Wiley Classics Library) |  | Author: Joseph L. Fleiss
Publisher: Wiley-Interscience
List Price: $132.95
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Rating:  3 reviews
Sales Rank: 958003
Media: Paperback
Edition: 1
Pages: 448
Number Of Items: 1
Shipping Weight (lbs): 1.6
Dimensions (in): 8.9 x 5.8 x 1.3
ISBN: 0471349917
Dewey Decimal Number: 615.50724
EAN: 9780471349914
ASIN: 0471349917
Publication Date: February 22, 1999
Availability: Usually ships in 1-2 business days
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Product Description
The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T.W. Anderson The Statistical Analysis of Time Series T.S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences Robert G. Bartle The Elements of Integration and Lebesgue Measure George E. P. Box & Norman R. Draper Evolutionary Operation: A Statistical Method for Process Improvement George E. P. Box & George C. Tiao Bayesian Inference in Statistical Analysis R. W. Carter Finite Groups of Lie Type: Conjugacy Classes and Complex Characters R. W. Carter Simple Groups of Lie Type William G. Cochran & Gertrude M. Cox Experimental Designs, Second Edition Richard Courant Differential and Integral Calculus, Volume I Richard Courant Differential and Integral Calculus, Volume II Richard Courant & D. Hilbert Methods of Mathematical Physics, Volume I Richard Courant & D. Hilbert Methods of Mathematical Physics, Volume II D. R. Cox Planning of Experiments Harold S. M. Coxeter Introduction to Geometry, Second Edition Charles W. Curtis & Irving Reiner Representation Theory of Finite Groups and Associative Algebras Charles W. Curtis & Irving Reiner Methods of Representation Theory with Applications to Finite Groups and Orders, Volume I Charles W. Curtis & Irving Reiner Methods of Representation Theory with Applications to Finite Groups and Orders, Volume II Bruno de Finetti Theory of Probability, Volume I Bruno de Finetti Theory of Probability, Volume 2 W. Edwards Deming Sample Design in Business Research Amos de Shalit & Herman Feshbach Theoretical Nuclear Physics, Volume 1—Nuclear Structure Harold F. Dodge & Harry G. Romig Sampling Inspection Tables: Single and Double Sampling J. L. Doob Stochastic Processes Nelson Dunford & Jacob T. Schwartz Linear Operators, Part One, General Theory Nelson Dunford & Jacob T. Schwartz Linear Operators, Part Two, Spectral Theory—Self Adjoint Operators in Hilbert Space Nelson Dunford & Jacob T. Schwartz Linear Operators, Part Three, Spectral Operators Regina C. Elandt-Johnson & Norman L. Johnson Survival Models and Data Analysis Herman Feshbach Theoretical Nuclear Physics: Nuclear Reactions Joseph L. Fleiss Design and Analysis of Clinical Experiments Bernard Friedman Lectures on Applications-Oriented Mathematics Phillip Griffiths & Joseph Harris Principles of Algebraic Geometry Gerald J. Hahn & Samuel S. Shapiro Statistical Models in Engineering Marshall Hall, Jr. Combinatorial Theory, Second Edition Morris H. Hansen, William N. Hurwitz & William G. Madow Sample Survey Methods and Theory, Volume I—Methods and Applications Morris H. Hansen, William N. Hurwitz & William G. Madow Sample Survey Methods and Theory, Volume II—Theory Peter Henrici Applied and Computational Complex Analysis, Volume 1—Power Series—Integration—Conformal Mapping—Location of Zeros Peter Henrici Applied and Computational Complex Analysis, Volume 2—Special Functions—Integral Transforms—Asymptotics—Continued Fractions Peter Henrici Applied and Computational Complex Analysis, Volume 3—Discrete Fourier Analysis— Cauchy Integrals—Construction of Conformal Maps—Univalent Functions Peter Hilton & Yel-Chiang Wu A Course in Modern Algebra Harry Hochstadt Integral Equations Leslie Kish Survey Sampling Shoshichi Kobayashi & Katsumi Nomizu Foundations of Differential Geometry, Volume I Shoshichi Kobayashi & Katsumi Nomizu Foundations of Differential Geometry, Volume 2 Erwin O. Kreyszig Introductory Functional Analysis with Applications William H. Louisell Quantum Statistical Properties of Radiation Rupert G. Miller Jr. Survival Analysis Ali Hasan Nayfeh Introduction to Perturbation Techniques Ali Hasan Nayfeh & Dean T. Mook Nonlinear Oscillations Emanuel Parzen Modern Probability Theory & Its Applications P. M. Prenter Splines and Variational Methods Walter Rudin Fourier Analysis on Groups Lawrence S. Schulman Techniques and Applications of Path Integration Shayle R. Searle Linear Models I. H. Segel Enzyme Kinetics: Behavior and Analysis of Rapid Equilibrium and Steady-State Enzyme Systems C. L. Siegel Topics in Complex Function Theory, Volume I—Elliptic Functions and Uniformization Theory C. L. Siegel Topics in Complex Function Theory, Volume II—Automorphic and Abelian Integrals C. L. Siegel Topics in Complex Function Theory, Volume III—Abelian Functions and Modular Functions of Several Variables L. Spitzer Physical Processes in the Interstellar Medium J. J. Stoker Differential Geometry J. J. Stoker Water Waves: The Mathematical Theory with Applications J. J. Stoker Nonlinear Vibrations in Mechanical and Electrical Systems Richard Zallen The Physics of Amorphous Solids Arnold Zellner Introduction to Bayesian Inference in Econometrics
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| Customer Reviews:
 great reference, now a classic February 8, 2008
Michael R. Chernick (Holland PA)
26 out of 26 found this review helpful
This book was first published in 1986. As Fleiss states in his preface, the intention is to fill a gap in the standard texts on experimental designs by emphasizing and illustrating those that are useful in clinical studies. This book was clearly marketed for the rapidly growing and highly regulated pharmaceutical industry. In addition to the classic experimental designs, Fleiss covers cross-over designs and repeated measure designs that are important in clinical trials. He writes clearly and deals with the important issues in clinical trials including potential biases, blinding, randomized controls, multiple comparisons and repeated measures. The book starts off with a chapter that emphasizes the effect of measurement error and also provides some simple experiments on reliability of measurements.
There is a wealth of methods included, many different designs and various parametric and nonparametric analysis techniques. It is aimed at biostatisticians in the pharmaceutical industry and the medical research field. The book is very much suited for an advanced undergraduate or graduate level course for students majoring in statistics or biostatistics. The level of mathematics is high but not excessively used. Mathematical results on sample size determination are deferred to an appendix.
The Wiley editors choose only successful books to be included in their Classics Library series. The intent of the Classics series is to take popular books by distinguished authors and create a paperback edition that may be more affordable than the hardcovered edition still in print. It is not a revision of the book. This book entered the Classics series in 1999.
It is a great reference source and I plan to consult it a great deal in the future. The only drawback to it that I see is that it is not up to date. The last 15 years has seen many advances in group sequential methods, Bayesian designs and longitudinal data analysis that this text misses. So Fleiss' book is not one stop shopping for a clinical biostatistician but it does offer a lot and presents it eloquently.
With regard to reviewer Izadi's comments on Amazon, I think the appropriate way to ask this question is really to write the author. Since it is here in print for the readers, I will attempt a reply. I think there is a misinterpretation of the terminology. When Fleiss refers to mean logarithms he is not referring to the population means on the log scale but rather the logarithm of the population mean on the original scale. With the latter interpretation equation 3.25 makes perfect sense. It is the former interpretation of the parameters that the reviewers point addresses. The importance in the example is to demonstrate the lack of robustness of t or F tests to non-normal (e.g. lognormal) data and to show that tests and confidence intervals can still be accomplished using the normal theory after the transformation. The key point is that it is the ratio of the parameters that is transformed into differences of the log of the parameters.
 A must for biostatisticians December 22, 2000
This is the standard text on this subject. Recommend you have at least a Master's degree in statistics to take full advantage of this book. This book is too technical for non-statisticians, although they may get some useful infomation from the non-statistical discussions.
A question from the autour December 11, 2000
Shahrokh Izadi (Tehran University, Public Health School, Iran)
1 out of 6 found this review helpful
Dear Sirs I do not know if I can use this part as a tool for contacting the autour or not; however please excuse me if I have not used it properly: I am a student of epidemiology, and presently we are studying the following book: The Design and Analysis of Clinical Experiments; Joseph L. Fleiss; John Wiley & Sons; 1986. But on page 67 it seems that there is some misunderstandings: When we do a log transformation, in fact we are changing (or shifting) the previous distribution to a normal distribution. Without any doubts in this transformation the means of our previous distributions do not transfer to the means of the new distributions! In fact these are the medians of the previous distributions which are transferred to the place of the means of new distributions (of course if we presume that the new distributions are almost perfectly normal) and by finding the confidence interval of the difference of the means of new distributions (lambda1 - lambda2) we are finding the CI of ratio Median1/Median2. In this way it seems reasonable that the formula 3.25 be changed to Median1/ Median2. It would be very kind of you if you help me in this problem!With best regards Dr. Shahrokh Izadi
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