Repeated measurements and cross-over designs /

An introduction to state-of-the-art experimental design approaches to better understand and interpret repeated measurement data in cross-over designs. Repeated Measurements and Cross-Over Designs: Features the close tie between the design, analysis, and presentation of resultsPresents principles and...

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Bibliographic Details
Main Author: Raghavarao, Damaraju
Format: eBook
Language:English
Published: Hoboken, New Jersey : Wiley, [2014]
Subjects:
Online Access:Connect to the full text of this electronic book

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245 1 0 |a Repeated measurements and cross-over designs /  |c the late Damaraju Raghavarao, Laura H. Carnell Professor, Department of Statistics, Temple University, Philadelphia, Pennsylvania, Lakshmi Padgett, Janssen Research & Development, LLC, Spring House, Pennsylvania. 
264 1 |a Hoboken, New Jersey :  |b Wiley,  |c [2014] 
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505 0 |a Repeated Measurements and Cross-Over Designs; Copyright; Contents; Preface; 1. Introduction; 1.1 INTRODUCTION; 1.2 ONE-SAMPLE RMD; 1.3 k-SAMPLE RMD; 1.4 SPLIT-PLOT DESIGNS; 1.5 GROWTH CURVES; 1.6 CROSS-OVER DESIGNS; 1.7 TWO-PERIOD CROSS-OVER DESIGNS; 1.8 MODIFICATIONS IN CROSS-OVER DESIGNS; 1.9 NONPARAMETRIC METHODS; REFERENCES; 2 One-Sample Repeated Measurement Designs; 2.1 INTRODUCTION; 2.2 TESTING FOR SPHERICITY CONDITION; 2.3 UNIVARIATE ANOVA FOR ONE-SAMPLE RMD; 2.4 MULTIVARIATE METHODS FORONE-SAMPLE RMD; 2.5 UNIVARIATE ANOVA UNDERNONSPHERICITY CONDITION; 2.6 NUMERICAL EXAMPLE. 
505 8 |a 2.7 CONCORDANCE CORRELATION COEFFICIENT2.8 MULTIRESPONSE CONCORDANCECORRELATION COEFFICIENT; 2.9 REPEATED MEASUREMENTS WITH BINARYRESPONSE; REFERENCES; 3 k-Sample Repeated Measurements Design; 3.1 INTRODUCTION; 3.2 TEST FOR THE EQUALITY OF DISPERSIONMATRICES AND SPHERICITY CONDITIONOF k-DISPERSION MATRICES; 3.3 UNIVARIATE ANOVA FOR k-SAMPLE RMD; 3.4 MULTIVARIATE METHODS FORk-SAMPLE RMD; 3.5 NUMERICAL EXAMPLE; 3.6 MULTIVARIATE METHODS WITH UNEQUALDISPERSION MATRICES; 3.7 ANALYSIS WITH ORDERED CATEGORICALRESPONSE; REFERENCES; 4 Growth Curve Models; 4.1 INTRODUCTION; 4.2 SIGMOIDAL CURVES. 
505 8 |a 4.3 ANALYSIS OF MIXED MODELS4.4 SIMPLE LINEAR GROWTH CURVE MODEL; 4.5 NONLINEAR GROWTH CURVE MODEL; 4.6 NUMERICAL EXAMPLE; 4.7 JOINT ACTION MODELS; REFERENCES; 5 Cross-Over Designs without Residual Effects; 5.1 INTRODUCTION; 5.2 FIXED EFFECTS ANALYSIS OF CODWOR; 5.3 CONNECTEDNESS IN CODWOR; 5.4 ORTHOGONALITY IN CODWOR; 5.5 LATIN SQUARE DESIGNS; 5.6 YOUDEN SQUARE DESIGN ANDGENERALIZATION; 5.7 F-SQUARES; 5.8 LATTICE SQUARE DESIGNS; 5.9 ANALYSIS OF CODWOR WHEN THE UNITSEFFECTS ARE RANDOM; 5.10 NUMERICAL EXAMPLE; 5.11 ORTHOGONAL LATIN SQUARES; REFERENCES. 
505 8 |a 6 Cross-Over Designs with Residual Effects6.1 INTRODUCTION; 6.2 ANALYSIS OF CODWR; 6.3 BRED; 6.4 PBCOD (m); 6.5 NUMERICAL EXAMPLE; 6.6 ANALYSIS WITH UNIT (OR SUBJECT)EFFECTS RANDOM; 6.7 CONCLUDING REMARKS; REFERENCES; 7 Two-Period Cross-Over Designs with Residual Effects; 7.1 INTRODUCTION; 7.2 TWO-PERIOD, TWO-TREATMENT CODWRANALYSIS: PARAMETRIC METHODS; 7.2.1 Analysis of the design based on themodel (7.2.9); 7.2.2 Decomposition of the model (7.2.9)into intra- and interunit components; 7.2.3 Estimating direct effects contrast usingcross-over nature of the treatments. 
505 8 |a 7.2.4 Modified two-period, two-treatment design7.2.5 Cost analysis; 7.3 TWO-PERIOD, TWO-TREATMENT CODWRANALYSIS: NONPARAMETRIC METHODS; 7.4 TWO-PERIOD t TREATMENTCROSS-OVER DESIGN; 7.5 NUMERICAL EXAMPLES; REFERENCES; 8 Other Cross-Over Designs with Residual Effects; 8.1 INTRODUCTION; 8.2 EXTRA-PERIOD DESIGNS; 8.2.1 Residual effect of a treatment effect on itselfis the same as residual effect on other treatments; 8.2.2 Residual effect of a treatment on itselfis different from the residual effect on othertreatments; 8.3 RESIDUAL EFFECTS PROPORTIONALTO DIRECT EFFECTS; 8.4 UNDIMINISHED RESIDUAL EFFECTS DESIGNS. 
520 |a An introduction to state-of-the-art experimental design approaches to better understand and interpret repeated measurement data in cross-over designs. Repeated Measurements and Cross-Over Designs: Features the close tie between the design, analysis, and presentation of resultsPresents principles and rules that apply very generally to most areas of research, such as clinical trials, agricultural investigations, industrial procedures, quality control procedures, and epidemiological studiesIncludes many practical examples, such as PK/PD studies in the pharmaceutical industry, k-sample and one s. 
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650 0 |a Statistics  |x Longtitudinal method. 
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