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1. Quantitative Decisions in Drug Development. 2nd ed. 2021
by Christy Chuang-Stein, Simon Kirby
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2021
シリーズ名: Springer Series in Pharmaceutical Statistics ;
オンライン: https://doi.org/10.1007/978-3-030-79731-7
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目次情報: 続きを見る
Chapter 1 - Clinical Testing of a New Drug
Chapter 2 - A Frequentist Decision-making Framework
Chapter 3 - Characteristics of a Diagnostic Test
Chapter 4 - The Parallel Between Clinical Trials and Diagnostic Tests
Chapter 5 - Incorporating Information from Completed Trials in Future Trial Planning
Chapter 6 - Choosing Metrics Appropriate for Different Stages of Drug Development
Chapter 7 - Designing Proof-of-Concept Trials with Desired Characteristics
Chapter 8 - Designing Dose-response Studies with Desired Characteristics
Chapter 9 - Designing Confirmatory Trials with Desired Characteristics
Chapter 10 - Designing Phase 4 Trials
Chapter 11 - Other Metrics That Have Been Proposed to Optimize Drug Development Decisions
Chapter 12 - Discounting Prior Results to Account for Selection Bias
Chapter 13 - Adaptive Designs
Chapter 14 - Additional Topics
Chapter 1 - Clinical Testing of a New Drug
Chapter 2 - A Frequentist Decision-making Framework
Chapter 3 - Characteristics of a Diagnostic Test
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2. Sampling Designs Dependent on Sample Parameters of Auxiliary Variables. 2nd ed. 2021
by Janusz L. Wywiał
出版情報: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2021
シリーズ名: SpringerBriefs in Statistics ;
オンライン: https://doi.org/10.1007/978-3-662-63413-4
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Introduction and Basic Sampling Strategies
Sampling Designs Dependent on Sample Moments of Auxiliary Variables
Sampling Designs Based on Order Statistics of Auxiliary Variable
Simulation Analysis of the Efficiency of the Strategies
Sampling Designs dependent on a Continuous Auxiliary Variable
Introduction and Basic Sampling Strategies
Sampling Designs Dependent on Sample Moments of Auxiliary Variables
Sampling Designs Based on Order Statistics of Auxiliary Variable
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3. Ten Projects in Applied Statistics. 1st ed. 2022
by Peter McCullagh
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2022
シリーズ名: Springer Series in Statistics ;
オンライン: https://doi.org/10.1007/978-3-031-14275-8
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1. Rat Surgery
2. Chain Saws
3. Fruit Flies
4. Growth Curves
5. Louse Evolution
6. Time Series I
7. Time Series II
8. Out of Africa
9. Environmental Projects
10. Fulmar Fitness
11. Basic Concepts
12. Principles
13. Initial Values
14. Probability Distributions
15. Gaussian Distributions
16. Space-Time Processes
17. Likelihood
18. Residual Likelihood
19. Response Transformation
20. Presentations and Reports
21. Q & A
1. Rat Surgery
2. Chain Saws
3. Fruit Flies
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4. Discrete Choice Analysis with R. 1st ed. 2022
by Antonio Páez, Geneviève Boisjoly
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2022
シリーズ名: Use R! ;
オンライン: https://doi.org/10.1007/978-3-031-20719-8
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目次情報: 続きを見る
1. Data, Models, and Software
2. Exploratory Data Analysis
3. Fundamental Concepts
4. 4 Logit
5. Practical Issues in the Specification and Estimation of Discrete Choice Models
6. Behavioral Insights from Choice Models
7. Non-Proportional Substitution Patterns I: Generalized Extreme Value Models
8. Non-Proportional Substitution Patterns II: The Probit Model
9. Dealing with Heterogeneity I: The Latent Class Logit Model
10 Dealing with Heterogeneity II: The Mixed Logit Model
11. Models for Ordinal Responses
Epilogue
References
1. Data, Models, and Software
2. Exploratory Data Analysis
3. Fundamental Concepts
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5. Fundamentals of Statistical Inference : What is the Meaning of Random Error?. 1st ed. 2022
by Norbert Hirschauer, Sven Grüner, Oliver Mußhoff
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2022
シリーズ名: SpringerBriefs in Applied Statistics and Econometrics ;
オンライン: https://doi.org/10.1007/978-3-030-99091-6
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6. How Data Quality Affects our Understanding of the Earnings Distribution. 1st ed. 2022
by Reza Che Daniels
出版情報: Singapore : Springer Nature Singapore : Imprint: Springer, 2022
オンライン: https://doi.org/10.1007/978-981-19-3639-5
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Introduction
A Framework for Investigating Micro Data Quality, with Application to South African Labour Market Household Surveys
Questionnaire Design and Response Propensities for Labour Income Micro Data
Univariate Multiple Imputation for Coarse Employee Income Data
Conclusion: How Data Quality Affects our Understanding of the Earnings Distribution
Introduction
A Framework for Investigating Micro Data Quality, with Application to South African Labour Market Household Surveys
Questionnaire Design and Response Propensities for Labour Income Micro Data