Quantitative Decisions in Drug Development (eBook, PDF) - Chuang-Stein, Christy; Kirby, Simon
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Produktbeschreibung
Autorenporträt
Inhaltsangabe

Clinical Testing of a New Drug.
A Frequentist Decision
making Framework.
Characteristics of a Diagnostic Test.
The Parallel Between Clinical Trials and Diagnostic Tests.
Incorporating Information from Completed Trials in Future Trial Planning.
Choosing Metrics Appropriate for Different Stages of Drug Development.
Designing Proof
of
Concept Trials with Desired Characteristics.
Designing Dose
response Studies with Desired Characteristics.
Designing Confirmatory Trials with Desired Characteristics.
Designing Phase 4 Trials.
Other Metrics That Have Been Proposed to Optimize Drug Development Decisions.
Discounting Prior Results to Account for Selection Bias.
Index.
Appendix.

Clinical Testing of a New Drug.- A Frequentist Decision-making Framework.- Characteristics of a Diagnostic Test.- The Parallel Between Clinical Trials and Diagnostic Tests.- Incorporating Information from Completed Trials in Future Trial Planning.- Choosing Metrics Appropriate for Different Stages of Drug Development.- Designing Proof-of-Concept Trials with Desired Characteristics.- Designing Dose-response Studies with Desired Characteristics.- Designing Confirmatory Trials with Desired Characteristics.- Designing Phase 4 Trials.- Other Metrics That Have Been Proposed to Optimize Drug Development Decisions.- Discounting Prior Results to Account for Selection Bias.- Index.- Appendix.

Rezensionen
"This work offers useful algorithms, classifications, and other general points to statisticians or 'quantitative scientists'. But, it is also really useful to regulatory affairs managers, clinicians, medical writers, and all kinds of decision-makers in the industry." (Andrei Myslivets, ISCB News, Vol. 68, December, 2019)
"It is presented in a concise, structured, friendly, and illustrative way that allows for a good understanding of the underlying ideas ... . the book from Chuang-Stein and Kirby is a valuable, interesting and easy read for statisticians and clinicians with some methodological background who are involved in clinical development or drug approval and who are looking for a structured way to make clinical development decisions." (Norbert Benda, Biometrical Journal, Vol. 61 (4), July, 2016)