rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Statistical Methods in Biomarker and Early Clinical Development
  name:
    name part: Fang, Liang.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Su, Cheng.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XII, 348 pages 54 illustrations, 31 illustrations in color.)
  abstract: This contributed volume offers a much-needed overview of the statistical methods in early clinical drug and biomarker development. Chapters are written by expert statisticians with extensive experience in the pharmaceutical industry and regulatory agencies. Because of this, the data presented is often accompanied by real world case studies, which will help make examples more tangible for readers. The many applications of statistics in drug development are covered in detail, making this volume a must-have reference. Biomarker development and early clinical development are the two critical areas on which the book focuses. By having the two sections of the book dedicated to each of these topics, readers will have a more complete understanding of how applying statistical methods to early drug development can help identify the right drug for the right patient at the right dose. Also presented are exciting applications of machine learning and statistical modeling, along with innovative methods and state-of-the-art advances, making this a timely and practical resource. This volume is ideal for statisticians, researchers, and professionals interested in pharmaceutical research and development. Readers should be familiar with the fundamentals of statistics and clinical trials.
  table of contents: Predictive Subgroup/Biomarker Identification and Machine Learning Methods -- Characterize and Dichotomize a Continuous Biomarker -- Surrogate Biomarkers -- Innovative Designs for Biomarker Guided Trials -- Statistical Considerations in the Development of Companion Diagnostic Device -- Biomarker Assay Development, Qualification and Validation -- Validation of Genomic Based Assay -- Clinical Application of Molecular Features in Therapeutic Selection and Drug Development -- Big data, real-world data, and machine learning -- Design and Analysis of Clinical Pharmacology Studies -- Statistical Considerations in Proof of Concept Studies -- Safety in Early Phase Studies -- Statistical Evaluation of QT/QTc Interval Prolongation -- Phase II Dose Finding -- Pharmacometrics.
  note: edited by Liang Fang, Cheng Su.
  subject:
    topic: Statistics
  subject:
    topic: Biomathematics
  subject:
    topic: Biostatistics
  subject:
    topic: Statistics for Life Sciences, Medicine, Health Sciences
  subject:
    topic: Physiological, Cellular and Medical Topics
  subject:
    topic: Biostatistics
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer Nature eBook
  identifier: 9783030315030
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 191226
    record change date: 20230330185458.6
    record identifier: in00004423369