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Design and Analysis of Subgroups with Biopharmaceutical Applications [[electronic resource] /] / edited by Naitee Ting, Joseph C. Cappelleri, Shuyen Ho, (Din) Ding-Geng Chen
| Design and Analysis of Subgroups with Biopharmaceutical Applications [[electronic resource] /] / edited by Naitee Ting, Joseph C. Cappelleri, Shuyen Ho, (Din) Ding-Geng Chen |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (404 pages) |
| Disciplina | 615.10727 |
| Collana | Emerging Topics in Statistics and Biostatistics |
| Soggetto topico |
Statistics
Biostatistics Pharmaceutical technology Statistics for Life Sciences, Medicine, Health Sciences Pharmaceutical Sciences/Technology |
| ISBN | 3-030-40105-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Data-driven and Confirmatory Subgroup Analysis in Clinical Trials -- 2. Subgroup Analysis – A View from Industry -- 3. Biomarker-Targeted Confirmatory Trials -- 4. Considerations on Subgroup Analysis in Design and Analysis of Multi-Regional Clinical Trials -- 5. Practical Subgroup Identification Strategies in Late-stage Clinical Trials -- 6. Exploratory Subgroup Identification for Biopharmaceutical Development -- 7. Logical Inference on Treatment Efficacy When Subgroup Exists -- 8. The GUIDE Approach to Subgroup Identification and Inference -- 9. Use of the VG (Virtual Twins Combined with GUIDE) Method in the Development of Precision Medicines -- 10. Subgroups Identification for Tailored Therapies: a System of Methods, a Framework for Consistent Methodology Evaluation, and an Integrated Learn-and-confirm Approach -- 11. Developing and Validating Predictive Classifiers in Randomized Clinical Trials -- 12. Issues Related to Subgroup Analysis -- 13. Subgroup Analysis with Partial Linear Model -- 14. Subgroup Analysis in the 21st Century -- 15. Power of Statistical Tests for Subgroup Analysis in Meta-Analysis -- 16. Heterogeneity and Subgroup Analysis in Network Meta-Analysis. |
| Record Nr. | UNISA-996418272103316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Design and Analysis of Subgroups with Biopharmaceutical Applications / / edited by Naitee Ting, Joseph C. Cappelleri, Shuyen Ho, (Din) Ding-Geng Chen
| Design and Analysis of Subgroups with Biopharmaceutical Applications / / edited by Naitee Ting, Joseph C. Cappelleri, Shuyen Ho, (Din) Ding-Geng Chen |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (404 pages) |
| Disciplina | 615.10727 |
| Collana | Emerging Topics in Statistics and Biostatistics |
| Soggetto topico |
Biometry
Pharmaceutical chemistry Biostatistics Pharmaceutics |
| ISBN | 3-030-40105-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Data-driven and Confirmatory Subgroup Analysis in Clinical Trials -- 2. Subgroup Analysis – A View from Industry -- 3. Biomarker-Targeted Confirmatory Trials -- 4. Considerations on Subgroup Analysis in Design and Analysis of Multi-Regional Clinical Trials -- 5. Practical Subgroup Identification Strategies in Late-stage Clinical Trials -- 6. Exploratory Subgroup Identification for Biopharmaceutical Development -- 7. Logical Inference on Treatment Efficacy When Subgroup Exists -- 8. The GUIDE Approach to Subgroup Identification and Inference -- 9. Use of the VG (Virtual Twins Combined with GUIDE) Method in the Development of Precision Medicines -- 10. Subgroups Identification for Tailored Therapies: a System of Methods, a Framework for Consistent Methodology Evaluation, and an Integrated Learn-and-confirm Approach -- 11. Developing and Validating Predictive Classifiers in Randomized Clinical Trials -- 12. Issues Related to Subgroup Analysis -- 13. Subgroup Analysis with Partial Linear Model -- 14. Subgroup Analysis in the 21st Century -- 15. Power of Statistical Tests for Subgroup Analysis in Meta-Analysis -- 16. Heterogeneity and Subgroup Analysis in Network Meta-Analysis. |
| Record Nr. | UNINA-9910484444203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Dose Finding and Beyond in Biopharmaceutical Development / / edited by Jingjing Ye, Ding-Geng Chen, Wen Zhou, Qiqi Deng, Joseph C. Cappelleri
| Dose Finding and Beyond in Biopharmaceutical Development / / edited by Jingjing Ye, Ding-Geng Chen, Wen Zhou, Qiqi Deng, Joseph C. Cappelleri |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (XVII, 254 p. 43 illus.) |
| Disciplina | 570.15195 |
| Collana | ICSA Book Series in Statistics |
| Soggetto topico |
Biometry
Experimental design Data mining Biostatistics Design of Experiments Data Mining and Knowledge Discovery |
| ISBN |
9783031671104
3031671104 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Emerging Topics in Dose-Finding and Beyond -- Understanding FDA Guidance On Dosage Optimization For Oncology Therapies -- FDA Project Optimus: The "Paradigm-Shifting" Initiative for Oncology Drug Development -- Challenges and Practical Guidance on the Implementation of Novel Oncology Dose Escalation Designs -- Challenges and Practical Guidance on the Implementation of Novel Oncology Dose Escalation Designs -- Monotonic Dose Response Assumption and Curve-Free Designs for Phase I Dose Finding Trials -- Dose Selection with 2-in-1 Design -- A Rank-Based Approach to Improve the Efficiency of Inferential Seamless Phase 2/3 Clinical Trials with Dose Optimization -- Comparing MCP-MOD and Ordinal Linear Contrast Test in Dose Finding Clinical Trials: A Thorough Examination -- Patient-Reported Tolerability in Drug Development -- Endpoint Development and Validation. |
| Record Nr. | UNINA-9910983317403321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Phase II Clinical Development of New Drugs / / by Naitee Ting, Ding-Geng Chen, Shuyen Ho, Joseph C. Cappelleri
| Phase II Clinical Development of New Drugs / / by Naitee Ting, Ding-Geng Chen, Shuyen Ho, Joseph C. Cappelleri |
| Autore | Ting Naitee |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XVII, 241 p. 25 illus., 17 illus. in color.) |
| Disciplina | 615.19 |
| Collana | ICSA Book Series in Statistics |
| Soggetto topico |
Statistics
Pharmaceutical technology Management Statistics for Life Sciences, Medicine, Health Sciences Pharmaceutical Sciences/Technology |
| ISBN | 981-10-4194-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1 Introduction -- Chapter 2 Concept of Alpha -- Chapter 3 Confirmation and Exploration -- Chapter 4 Design a Proof of Concept (PoC) Trial -- Chapter 5 Design of Dose-Ranging Trials -- Chapter 6 Combining PoC and Dose Ranging Trials -- Chapter 7 Risks of Inconclusiveness -- Chapter 8 Analysis of a PoC Study -- Chapter 9 Data Analysis for Dose-Ranging Trials with Continuous Outcome -- Chapter 10 Data Analysis of Dose-Ranging Trials for Binary Outcomes -- Chapter 11 Bayesian Methods -- Chapter 12 Overview of Phase III Clinical Trials. |
| Record Nr. | UNINA-9910254273803321 |
Ting Naitee
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| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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A Practical Approach to Quantitative Validation of Patient-Reported Outcomes : A Simulation-Based Guide Using SAS
| A Practical Approach to Quantitative Validation of Patient-Reported Outcomes : A Simulation-Based Guide Using SAS |
| Autore | Bushmakin Andrew G |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (365 pages) |
| Altri autori (Persone) | CappelleriJoseph C |
| Collana | Statistics in Practice Ser. |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-37635-1
1-119-37631-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- About the Authors -- Chapter 1 Introduction -- 1.1 What Is a PRO Measure? -- 1.2 Development of a PRO Measure -- 1.2.1 Concept Identification -- 1.2.1.1 Literature and Instrument Review -- 1.2.1.2 Patient-Centered Input -- 1.2.2 Item Development -- 1.2.3 Cognitive Interviews -- 1.2.4 Additional Considerations -- 1.2.5 Documentation of Development Process with Conceptual Framework -- 1.3 Psychometric Validation -- 1.3.1 Psychometric Evaluation Data -- 1.3.2 Psychometric Properties -- 1.3.2.1 Distributional Characteristics -- 1.3.2.2 Measurement Model Structure -- 1.3.2.3 Reliability -- 1.3.2.4 Construct Validity -- 1.3.2.5 Ability to Detect Change -- 1.3.2.6 Interpretation -- 1.4 Learning Through Simulations -- 1.5 Summary -- References -- Chapter 2 Validation Workflow -- 2.1 Clinical Trials as a Data Source for Validation -- 2.2 Validation Workflow for Single-Item Scales -- 2.3 Confirmatory Validation Workflow for Multi-item Multi-domain Scales -- 2.4 Validation Flow for a New Multi-item Multi-domain Scale -- 2.4.1 New Scale with Known Conceptual Framework -- 2.4.2 New Scale with Unknown Measurement Structure -- 2.5 Cross-Sectional Studies and Field Tests -- 2.6 Summary -- References -- Chapter 3 An Assessment of Classical Test Theory and Item Response Theory -- 3.1 Overview of Classical Test Theory -- 3.1.1 Basics -- 3.1.2 Illustration -- 3.1.3 Another Look -- 3.2 Person-Item Maps -- 3.2.1 CTT Revisited -- 3.2.2 Note on IRT -- 3.2.3 Implementation of Person-Item Maps -- 3.2.4 CTT-Based Scoring vs. IRT-Based Scoring -- 3.3 Summary -- References -- Chapter 4 Reliability -- 4.1 Reproducibility/Test-Retest -- 4.1.1 Measurement Error Model -- 4.1.2 Two Time Points -- 4.1.3 Random-Effects Model for ICC Estimation.
4.1.4 Test-Retest Reliability Assessment in the Context of Clinical Studies -- 4.1.4.1 Pre-Treatment/Pre-Baseline Data -- 4.1.4.2 Post-Baseline Data -- 4.1.4.3 Time Period Between Observations -- 4.1.5 Spearman-Brown Prophecy Formula -- 4.1.6 Domain Score Test-Retest vs. Item Test-Retest -- 4.1.7 Observer-Based and Interviewer-Based Scales -- 4.1.8 Uncovering True Relationship Between Measurements -- 4.1.8.1 Accounting for Measurement Error -- 4.1.8.2 Measurement Error Model with Two Observations -- 4.2 Cronbach's Alpha -- 4.2.1 Likert-Type Scales -- 4.2.2 Dichotomous Items -- 4.3 Summary -- References -- Chapter 5 Construct Validity and Criterion Validity -- 5.1 Exploratory Factor Analyses -- 5.1.1 Modeling Assumptions -- 5.1.2 Exploratory Factor Analysis Implementation -- 5.1.3 Evaluating the Number of Factors and Factor Loadings -- 5.1.3.1 Scree Plot -- 5.1.3.2 Correlated Latent Factors -- 5.1.3.3 Parallel Analysis with Reduced Correlation Matrix -- 5.1.3.4 Factor Loadings -- 5.2 Confirmatory Factor Analyses -- 5.2.1 Confirmatory Factor Analysis Model -- 5.2.2 Confirmatory Factor Analysis Model Implementation -- 5.2.3 Confirmatory Factor Analysis with Domains Represented by a Single Item -- 5.2.4 Second-Order Confirmatory Factor Analysis -- 5.2.4.1 Implementation of the Model with at Least Three First-Order Latent Domains -- 5.2.4.2 Implementation of the Model with Two First-Order Latent Domains -- 5.2.5 Formative vs. Reflective Model -- 5.2.6 Bifactor Model -- 5.2.7 Confirmatory Factor Analysis Using Polychoric Correlations -- 5.3 Convergent and Discriminant Validity -- 5.3.1 Convergent and Discriminant Validity Assessment -- 5.3.2 Convergent and Discriminant Validity Evaluation in a Clinical Study -- 5.4 Known-Groups Validity -- 5.5 Criterion Validity -- 5.6 Summary -- References -- Chapter 6 Responsiveness and Sensitivity. 6.1 Ability to Detect Change -- 6.1.1 Definitions and Concepts -- 6.1.2 Ability to Detect Change Analysis Implementation -- 6.1.3 Correlation Analysis to Support Ability to Detect Change -- 6.1.4 Deconstructing Correlation Between Changes -- 6.2 Sensitivity to Treatment -- 6.2.1 What Is the Sensitivity to Treatment? -- 6.2.2 Concurrent Estimation of the Treatment Effects for a Multi-Domain Scale -- 6.2.2.1 Assessment of the Treatment Effect for a Single Domain -- 6.2.2.2 Assessment of the Treatment Effects for a Multi-Domain Scale -- 6.3 Summary -- References -- Chapter 7 Interpretation of Patient-Reported Outcome Findings -- 7.1 Meaningful Within-Patient Change -- 7.1.1 Definitions and Concepts -- 7.1.2 Anchor-Based Method to Assess Meaningful Within-Patient Change -- 7.1.3 Cumulative Distribution Functions to Supplement Anchor-Based Methods -- 7.2 Clinical Important Difference -- 7.2.1 Meaningful Within-Patient Change Versus Between-Group Difference -- 7.2.2 Anchor-Based Method to Assess Clinically Important Difference -- 7.3 Responder Analyses and Cumulative Distribution Functions -- 7.3.1 Treatment Effect Model -- 7.3.2 MWPC Application: A Responder Analysis -- 7.3.3 Using CDFs for Interpretation of Results -- 7.4 Summary -- References -- Index -- EULA. |
| Record Nr. | UNINA-9910623984003321 |
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Bushmakin Andrew G
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| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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