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Design and Analysis of Experiments in the Health Sciences.

By: Contributor(s): Material type: TextPublisher: Newark : John Wiley & Sons, Incorporated, 2012Copyright date: ©2012Edition: 1st edDescription: 1 online resource (247 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781118279694
Subject(s): Genre/Form: DDC classification:
  • 610.72/7
Online resources:
Contents:
Design and Analysis of Experiments in the Health Sciences -- Contents -- Preface -- 1 The Basics -- 1.1 Four Basic Questions -- 1.2 Variation -- 1.3 Principles of Design and Analysis -- 1.4 Experiments and Observational Studies -- 1.5 Illustrative Applications of Principles -- 1.6 Experiments in the Health Sciences -- 1.7 Adaptive Allocation -- 1.7.1 Equidistribution -- 1.7.2 Adaptive Allocation Techniques -- 1.8 Sample Size Calculations -- 1.9 Statistical Models for the Data -- 1.10 Analysis and Presentation -- 1.10.1 Graph the Data in Several Ways -- 1.10.2 Assess Assumptions of the Statistical Model -- 1.10.3 Confirmatory and Exploratory Analysis -- 1.10.4 Missing Data Need Careful Accounting -- 1.10.5 Statistical Software -- 1.11 Notes -- 1.11.1 Characterization Studies -- 1.11.2 Additional Comments on Balance -- 1.11.3 Linear and Nonlinear Models -- 1.11.4 Analysis of Variance Versus Regression Analysis -- 1.12 Summary -- 1.13 Problems -- 2 Completely Randomized Designs -- 2.1 Randomization -- 2.2 Hypotheses and Sample Size -- 2.3 Estimation and Analysis -- 2.4 Example -- 2.5 Discussion and Extensions -- 2.5.1 Preparing Data for Computer Analysis -- 2.5.2 Treatment Assignment in this Example -- 2.5.3 Check on Randomization -- 2.5.4 Partitioning the Treatment Sum of Squares -- 2.5.5 Alternative Endpoints -- 2.5.6 Dummy Variables -- 2.5.7 Contrasts -- 2.6 Randomization -- 2.7 Hypotheses and Sample Size -- 2.8 Estimation and Analysis -- 2.9 Example -- 2.10 Discussion and Extensions -- 2.10.1 Two Roles for ANCOVA -- 2.10.2 Partitioning of Sums of Squares -- 2.10.3 Assumption of Parallelism -- 2.11 Notes -- 2.11.1 Constrained Randomization -- 2.11.2 Assumptions of the Analysis of Variance and Covariance -- 2.11.3 When the Assumptions Don't Hold -- 2.11.4 Alternative Graphical Displays -- 2.11.5 Sample Sizes for More Than Two Levels.
2.11.6 Limitations of Computer Output -- 2.11.7 Unequal Sample Sizes -- 2.11.8 Design Implications of the CRD -- 2.11.9 Power and Alternative Hypotheses -- 2.11.10 Regression or Analysis of Variance? -- 2.11.11 Bioassay -- 2.12 Summary -- 2.13 Problems -- 3 Randomized Block Designs -- 3.1 Randomization -- 3.2 Hypotheses and Sample Size -- 3.3 Estimation and Analysis -- 3.4 Example -- 3.5 Discussion and Extensions -- 3.5.1 Evaluating Model Assumptions -- 3.5.2 Multiple Comparisons -- 3.5.3 Number of Treatments and Block Size -- 3.5.4 Missing Data -- 3.5.5 Does It Always Pay to Block? -- 3.5.6 Concomitant Variables -- 3.5.7 Imbalance -- 3.6 Randomization -- 3.7 Hypotheses and Sample Size -- 3.8 Estimation and Analysis -- 3.9 Example -- 3.10 Discussion and Extensions -- 3.10.1 Implications of the Model -- 3.10.2 Number of Latin Squares -- 3.11 Randomization -- 3.12 Hypotheses and Sample Size -- 3.13 Estimation and Analysis -- 3.14 Example -- 3.15 Discussion and Extensions -- 3.15.1 Partially Balanced Incomplete Block Designs -- 3.16 Notes -- 3.16.1 Analysis Follows Design -- 3.16.2 Relative Efficiency -- 3.16.3 Additivity of the Model -- 3.17 Summary -- 3.18 Problems -- 4 Factorial Designs -- 4.1 Randomization -- 4.2 Hypotheses and Sample Size -- 4.3 Estimation and Analysis -- 4.4 Example 1 -- 4.5 Example 2 -- 4.6 Notes -- 4.6.1 Regression Analysis Approaches -- 4.6.2 Almost Factorial -- 4.6.3 Design Structure and Factor Structure -- 4.6.4 Effect and Interaction Tables -- 4.6.5 Balanced Design -- 4.6.6 Missing Data -- 4.6.7 Fixed, Random, and Mixed Effects Models -- 4.6.8 Fractional Factorials -- 4.7 Summary -- 4.8 Problems -- 5 Multilevel Designs -- 5.1 Randomization -- 5.2 Hypotheses and Sample Size -- 5.3 Estimation and Analysis -- 5.4 Example -- 5.5 Discussion and Extensions -- 5.5.1 Whole-Plot and Split-Plot Variability.
5.5.2 Getting the Computer to Do the Right Analysis -- 5.6 Notes -- 5.6.1 Fractional Factorials-Example -- 5.6.2 Missing Data -- 5.7 Summary -- 5.8 Problems -- 6 Repeated Measures Designs -- 6.1 Randomization -- 6.2 Hypotheses and Sample Size -- 6.3 Estimation and Analysis -- 6.4 Example -- 6.5 Discussion and Extensions -- 6.6 Notes -- 6.6.1 RBD and RMD -- 6.6.2 Missing Data: The Fundamental Challenge in RMD -- 6.6.3 Correlation Structure -- 6.6.4 Derived Variable Analysis -- 6.7 Summary -- 6.8 Problems -- 7 Randomized Clinical Trials -- 7.1 Endpoints -- 7.2 Randomization -- 7.3 Hypotheses and Sample Size -- 7.4 Follow-Up -- 7.5 Estimation and Analysis -- 7.6 Examples -- 7.7 Discussion and Extensions -- 7.7.1 Statistical Significance and Clinical Importance -- 7.7.2 Ethics -- 7.7.3 Reporting -- 7.8 Notes -- 7.8.1 Multicenter Trials -- 7.8.2 International Harmonization -- 7.8.3 Data Safety Monitoring -- 7.8.4 Ancillary Studies -- 7.8.5 Subgroup Analysis and Data Mining -- 7.8.6 Meta-Analysis -- 7.8.7 Authorship and Recognition -- 7.8.8 Communication -- 7.8.9 Data Sharing -- 7.8.10 N-of-1 Trials -- 7.9 Resources -- 7.10 Summary -- 7.11 Problems -- 8 Microarrays -- 8.1 Introduction -- 8.2 Genes, Gene Expression, and Microarrays -- 8.2.1 Genes and Gene Expression -- 8.2.2 Gene Expression Microarrays -- 8.3 Examples of Microarray Studies -- 8.4 Replication and Sample Size -- 8.5 Blocking and Microarrays -- 8.6 Randomization and Microarrays -- 8.7 Microarray Data Analysis Issues -- 8.7.1 Image Analysis -- 8.7.2 Data Preprocessing -- 8.7.3 Identifying Differentially Expressed Genes -- 8.7.4 Multiple Testing -- 8.7.5 Gene Set Analysis -- 8.7.6 The Class Prediction Problem -- 8.8 Data Analysis Example -- 8.9 Notes -- 8.9.1 Sample Size -- 8.9.2 FDR Estimation -- 8.9.3 Evaluation of Data Preprocessing Methods -- 8.10 Summary -- 8.11 Problems -- Bibliography.
Author Index -- Subject Index.
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Design and Analysis of Experiments in the Health Sciences -- Contents -- Preface -- 1 The Basics -- 1.1 Four Basic Questions -- 1.2 Variation -- 1.3 Principles of Design and Analysis -- 1.4 Experiments and Observational Studies -- 1.5 Illustrative Applications of Principles -- 1.6 Experiments in the Health Sciences -- 1.7 Adaptive Allocation -- 1.7.1 Equidistribution -- 1.7.2 Adaptive Allocation Techniques -- 1.8 Sample Size Calculations -- 1.9 Statistical Models for the Data -- 1.10 Analysis and Presentation -- 1.10.1 Graph the Data in Several Ways -- 1.10.2 Assess Assumptions of the Statistical Model -- 1.10.3 Confirmatory and Exploratory Analysis -- 1.10.4 Missing Data Need Careful Accounting -- 1.10.5 Statistical Software -- 1.11 Notes -- 1.11.1 Characterization Studies -- 1.11.2 Additional Comments on Balance -- 1.11.3 Linear and Nonlinear Models -- 1.11.4 Analysis of Variance Versus Regression Analysis -- 1.12 Summary -- 1.13 Problems -- 2 Completely Randomized Designs -- 2.1 Randomization -- 2.2 Hypotheses and Sample Size -- 2.3 Estimation and Analysis -- 2.4 Example -- 2.5 Discussion and Extensions -- 2.5.1 Preparing Data for Computer Analysis -- 2.5.2 Treatment Assignment in this Example -- 2.5.3 Check on Randomization -- 2.5.4 Partitioning the Treatment Sum of Squares -- 2.5.5 Alternative Endpoints -- 2.5.6 Dummy Variables -- 2.5.7 Contrasts -- 2.6 Randomization -- 2.7 Hypotheses and Sample Size -- 2.8 Estimation and Analysis -- 2.9 Example -- 2.10 Discussion and Extensions -- 2.10.1 Two Roles for ANCOVA -- 2.10.2 Partitioning of Sums of Squares -- 2.10.3 Assumption of Parallelism -- 2.11 Notes -- 2.11.1 Constrained Randomization -- 2.11.2 Assumptions of the Analysis of Variance and Covariance -- 2.11.3 When the Assumptions Don't Hold -- 2.11.4 Alternative Graphical Displays -- 2.11.5 Sample Sizes for More Than Two Levels.

2.11.6 Limitations of Computer Output -- 2.11.7 Unequal Sample Sizes -- 2.11.8 Design Implications of the CRD -- 2.11.9 Power and Alternative Hypotheses -- 2.11.10 Regression or Analysis of Variance? -- 2.11.11 Bioassay -- 2.12 Summary -- 2.13 Problems -- 3 Randomized Block Designs -- 3.1 Randomization -- 3.2 Hypotheses and Sample Size -- 3.3 Estimation and Analysis -- 3.4 Example -- 3.5 Discussion and Extensions -- 3.5.1 Evaluating Model Assumptions -- 3.5.2 Multiple Comparisons -- 3.5.3 Number of Treatments and Block Size -- 3.5.4 Missing Data -- 3.5.5 Does It Always Pay to Block? -- 3.5.6 Concomitant Variables -- 3.5.7 Imbalance -- 3.6 Randomization -- 3.7 Hypotheses and Sample Size -- 3.8 Estimation and Analysis -- 3.9 Example -- 3.10 Discussion and Extensions -- 3.10.1 Implications of the Model -- 3.10.2 Number of Latin Squares -- 3.11 Randomization -- 3.12 Hypotheses and Sample Size -- 3.13 Estimation and Analysis -- 3.14 Example -- 3.15 Discussion and Extensions -- 3.15.1 Partially Balanced Incomplete Block Designs -- 3.16 Notes -- 3.16.1 Analysis Follows Design -- 3.16.2 Relative Efficiency -- 3.16.3 Additivity of the Model -- 3.17 Summary -- 3.18 Problems -- 4 Factorial Designs -- 4.1 Randomization -- 4.2 Hypotheses and Sample Size -- 4.3 Estimation and Analysis -- 4.4 Example 1 -- 4.5 Example 2 -- 4.6 Notes -- 4.6.1 Regression Analysis Approaches -- 4.6.2 Almost Factorial -- 4.6.3 Design Structure and Factor Structure -- 4.6.4 Effect and Interaction Tables -- 4.6.5 Balanced Design -- 4.6.6 Missing Data -- 4.6.7 Fixed, Random, and Mixed Effects Models -- 4.6.8 Fractional Factorials -- 4.7 Summary -- 4.8 Problems -- 5 Multilevel Designs -- 5.1 Randomization -- 5.2 Hypotheses and Sample Size -- 5.3 Estimation and Analysis -- 5.4 Example -- 5.5 Discussion and Extensions -- 5.5.1 Whole-Plot and Split-Plot Variability.

5.5.2 Getting the Computer to Do the Right Analysis -- 5.6 Notes -- 5.6.1 Fractional Factorials-Example -- 5.6.2 Missing Data -- 5.7 Summary -- 5.8 Problems -- 6 Repeated Measures Designs -- 6.1 Randomization -- 6.2 Hypotheses and Sample Size -- 6.3 Estimation and Analysis -- 6.4 Example -- 6.5 Discussion and Extensions -- 6.6 Notes -- 6.6.1 RBD and RMD -- 6.6.2 Missing Data: The Fundamental Challenge in RMD -- 6.6.3 Correlation Structure -- 6.6.4 Derived Variable Analysis -- 6.7 Summary -- 6.8 Problems -- 7 Randomized Clinical Trials -- 7.1 Endpoints -- 7.2 Randomization -- 7.3 Hypotheses and Sample Size -- 7.4 Follow-Up -- 7.5 Estimation and Analysis -- 7.6 Examples -- 7.7 Discussion and Extensions -- 7.7.1 Statistical Significance and Clinical Importance -- 7.7.2 Ethics -- 7.7.3 Reporting -- 7.8 Notes -- 7.8.1 Multicenter Trials -- 7.8.2 International Harmonization -- 7.8.3 Data Safety Monitoring -- 7.8.4 Ancillary Studies -- 7.8.5 Subgroup Analysis and Data Mining -- 7.8.6 Meta-Analysis -- 7.8.7 Authorship and Recognition -- 7.8.8 Communication -- 7.8.9 Data Sharing -- 7.8.10 N-of-1 Trials -- 7.9 Resources -- 7.10 Summary -- 7.11 Problems -- 8 Microarrays -- 8.1 Introduction -- 8.2 Genes, Gene Expression, and Microarrays -- 8.2.1 Genes and Gene Expression -- 8.2.2 Gene Expression Microarrays -- 8.3 Examples of Microarray Studies -- 8.4 Replication and Sample Size -- 8.5 Blocking and Microarrays -- 8.6 Randomization and Microarrays -- 8.7 Microarray Data Analysis Issues -- 8.7.1 Image Analysis -- 8.7.2 Data Preprocessing -- 8.7.3 Identifying Differentially Expressed Genes -- 8.7.4 Multiple Testing -- 8.7.5 Gene Set Analysis -- 8.7.6 The Class Prediction Problem -- 8.8 Data Analysis Example -- 8.9 Notes -- 8.9.1 Sample Size -- 8.9.2 FDR Estimation -- 8.9.3 Evaluation of Data Preprocessing Methods -- 8.10 Summary -- 8.11 Problems -- Bibliography.

Author Index -- Subject Index.

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