Clinical Trial Optimization Using R
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- List of Contributors -- 1: Clinical Scenario Evaluation and Clinical Trial Optimization -- 1.1 Introduction -- 1.2 Clinical Scenario Evaluation -- 1.2.1 Components of Clinical Scenario Evaluation -- 1.2.2 Software implementation -- 1.2.3 Case study 1.1: Clinical trial with a normally distributed endpoint -- 1.2.4 Case study 1.2: Clinical trial with two time-to-event endpoints -- 1.3 Clinical trial optimizatio…
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Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- List of Contributors -- 1: Clinical Scenario Evaluation and Clinical Trial Optimization -- 1.1 Introduction -- 1.2 Clinical Scenario Evaluation -- 1.2.1 Components of Clinical Scenario Evaluation -- 1.2.2 Software implementation -- 1.2.3 Case study 1.1: Clinical trial with a normally distributed endpoint -- 1.2.4 Case study 1.2: Clinical trial with two time-to-event endpoints -- 1.3 Clinical trial optimization -- 1.3.1 Optimization strategies -- 1.3.2 Optimization algorithm -- 1.3.3 Sensitivity assessments -- 1.4 Direct optimization -- 1.4.1 Case study 1.3: Clinical trial with two patient populations -- 1.4.2 Qualitative sensitivity assessment -- 1.4.3 Quantitative sensitivity assessment -- 1.4.4 Optimal selection of the target parameter -- 1.5 Tradeoff-based optimization -- 1.5.1 Case study 1.4: Clinical trial with an adaptive design -- 1.5.2 Optimal selection of the target parameter -- 2: Clinical Trials with Multiple Objectives -- 2.1 Introduction -- 2.2 Clinical Scenario Evaluation framework -- 2.2.1 Data models -- 2.2.2 Analysis models -- 2.2.3 Evaluation models -- 2.3 Case study 2.1: Optimal selection of a multiplicity adjustment -- 2.3.1 Clinical trial -- 2.3.2 Qualitative sensitivity assessment -- 2.3.3 Quantitative sensitivity assessment -- 2.3.4 Software implementation -- 2.3.5 Conclusions and extensions -- 2.4 Case study 2.2: Direct selection of optimal procedure parameters -- 2.4.1 Clinical trial -- 2.4.2 Optimal selection of the target parameter in Procedure B1 -- 2.4.3 Optimal selection of the target parameters in Procedure B2 -- 2.4.4 Sensitivity assessments -- 2.4.5 Software implementation -- 2.4.6 Conclusions and extensions
2.5 Case study 2.3: Tradeoff-based selection of optimal procedure parameters -- 2.5.1 Clinical trial -- 2.5.2 Optimal selection of the target parameter in Procedure H -- 2.5.3 Software implementation -- 2.5.4 Conclusions and extensions -- 3: Subgroup Analysis in Clinical Trials -- 3.1 Introduction -- 3.2 Clinical Scenario Evaluation in confirmatory subgroup analysis -- 3.2.1 Clinical Scenario Evaluation framework -- 3.2.2 Multiplicity adjustments -- 3.2.3 Decision-making framework -- 3.3 Case study 3.1: Optimal selection of a multiplicity adjustment -- 3.3.1 Clinical trial -- 3.3.2 Direct optimization based on disjunctive power -- 3.3.3 Direct optimization based on weighted power -- 3.3.4 Qualitative sensitivity assessment -- 3.3.5 Quantitative sensitivity assessment -- 3.3.6 Software implementation -- 3.3.7 Conclusions and extensions -- 3.4 Case study 3.2: Optimal selection of decision rules to support two potential claims -- 3.4.1 Clinical trial -- 3.4.2 Influence condition -- 3.4.3 Optimal selection of the influence threshold -- 3.4.4 Software implementation -- 3.4.5 Conclusions and extensions -- 3.5 Case study 3.3: Optimal selection of decision rules to support three potential claims -- 3.5.1 Clinical trial -- 3.5.2 Interaction condition -- 3.5.3 Optimal selection of the influence and interaction thresholds -- 3.5.4 Software implementation -- 3.5.5 Conclusions and extensions -- 4: Decision Making in Clinical Development -- 4.1 Introduction -- 4.2 Clinical Scenario Evaluation in Go/No-Go decision making and determination of probability of success -- 4.2.1 Clinical Scenario Evaluation approach -- 4.2.2 Go/No-Go decision criteria -- 4.2.3 Probability of success -- 4.2.4 Probability of success applications -- 4.3 Motivating example -- 4.3.1 Clinical trial -- 4.3.2 Software implementation
4.4 Case study 4.1: Bayesian Go/No-Go decision criteria -- 4.4.1 Clinical trial -- 4.4.2 General sensitivity assessments -- 4.4.3 Bayesian Go/No-Go evaluation using informative priors -- 4.4.4 Sample size considerations -- 4.4.5 Software implementation -- 4.4.6 Conclusions and extensions -- 4.5 Case study 4.2: Bayesian Go/No-Go evaluation using an alternative decision criterion -- 4.5.1 Clinical trial -- 4.5.2 Software implementation -- 4.5.3 Conclusions and extensions -- 4.6 Case study 4.3: Bayesian Go/No-Go evaluation in a trial with an interim analysis -- 4.6.1 Clinical trial -- 4.6.2 Software implementation -- 4.6.3 Conclusions and extensions -- 4.7 Case study 4.4: Decision criteria in Phase II trials based on Probability of Success -- 4.7.1 Clinical trial -- 4.7.2 Software implementation -- 4.7.3 Conclusions and extensions -- 4.8 Case study 4.5: Updating POS using interim or external information -- 4.8.1 Clinical trial -- 4.8.2 Software implementation -- Bibliography -- Index
Edition Statements
- Publication Date:2017
- Edition:1st ed
Publication Statements
- Publisher(s):CRC Press
- Place of Publication:Portland
- Language:eng
- Publication Date:2017
The Physical Object
- Description:1 Online-Ressource (338 Seiten)
Edition Identifiers
- ISBN:9781498735087, 1498735088, 9781498735070, 149873507X
- oclc:on1021207961
- Dewey Decimal:610.727
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