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  <titleInfo>
    <title>Innovative Designs and Analyses for Small Population Clinical Trials</title>
    <subTitle>Development Strategies and Operational Engagement for Pediatric and Rare Diseases</subTitle>
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    <namePart>Ye, Jingjing.</namePart>
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    <namePart>Nie, Lei.</namePart>
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    <dateIssued encoding="marc">2024</dateIssued>
    <edition>1st ed. 2024.</edition>
    <issuance>monographic</issuance>
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    <extent>XIX, 473 p. 50 illus., 48 illus. in color. online resource.</extent>
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  <abstract>This book discusses a multidisciplinary and multi-stakeholder collaborative approach to small population clinical trials. It provides and illustrates a systematic framework that depends upon a collaborative infrastructure between the patient community, patient advocacy groups, researchers and investigators, pharmaceutical companies, global regulatory agencies, and customers. The book also highlights the considerations of natural history and patient registry, diagnostic criteria determination, identification of clinically meaningful endpoints, and global regulatory guidance. The authors detail the nuances of clinical trial design as specific to small population disease, drawing from real examples.</abstract>
  <tableOfContents>1. Introduction -- 2. The Case Study of NCI-COG Pediatric Match Trials -- 3. The Case Study of Non-oncology (Lonafarnib) -- 4. Natural History and Patient Registry -- 5. Diagnostic Criteria and Endpoints -- 6. Enrichment Designs -- 7. Pediatric Extrapolation -- 8. Cross-over Designs -- 9. Adaptive Designs -- 10. Master Protocols -- 11. Other Designs -- 12. Modeling and Simulations -- 13. Frequentist Approaches -- 14. Bayesian Approaches -- 15. Operation and Other Considerations -- 16. Case Studies.</tableOfContents>
  <note type="statement of responsibility">by Jingjing Ye, Lei Nie.</note>
  <subject authority="lcsh">
    <topic>Biometry</topic>
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  <subject authority="lcsh">
    <topic>Clinical medicine</topic>
    <topic>Research</topic>
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  <subject authority="lcsh">
    <topic>Statistics </topic>
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    <topic>Artificial intelligence</topic>
    <topic>Data processing</topic>
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  <subject authority="lcsh">
    <topic>Machine learning</topic>
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  <subject>
    <topic>Biostatistics</topic>
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  <subject>
    <topic>Clinical Research</topic>
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  <subject>
    <topic>Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences</topic>
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  <subject>
    <topic>Applied Statistics</topic>
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  <subject>
    <topic>Data Science</topic>
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  <subject>
    <topic>Machine Learning</topic>
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      <title>ICSA Book Series in Statistics</title>
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  <identifier type="isbn">9783031608438</identifier>
  <identifier type="uri">https://doi.org/10.1007/978-3-031-60843-8</identifier>
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