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Statistical Methods for CMC Applications

  • Richard K. Burdick
  • David J. LeBlond
  • Lori B. Pfahler
  • Jorge Quiroz
  • Leslie Sidor
  • Kimberly Vukovinsky
  • Lanju Zhang
Chapter
Part of the Statistics for Biology and Health book series (SBH)

Abstract

In this chapter, we provide statistical methods that are useful in CMC applications. Our goal is to provide a description of these methods without delving deeply into the theoretical aspects. References are provided for the reader who desires a more in depth understanding of the material.

Keywords

Analysis of variance Bayesian analysis Confidence intervals Data reporting Data rounding Data transformations Dependent measures Equivalence testing Hypothesis testing Interaction effects LOQ values Mixed models Multiple regression Nonlinear models Non-normal data Prediction intervals Quadratic effects Regression analysis Residual analysis Statistical consulting Statistical intervals Tolerance intervals Visualization of data 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Richard K. Burdick
    • 1
  • David J. LeBlond
    • 2
  • Lori B. Pfahler
    • 3
  • Jorge Quiroz
    • 4
  • Leslie Sidor
    • 5
  • Kimberly Vukovinsky
    • 6
  • Lanju Zhang
    • 7
  1. 1.Elion LabsLouisvilleUSA
  2. 2.CMC StatisticsWadsworthUSA
  3. 3.Merck & Co., Inc.TelfordUSA
  4. 4.Merck & Co., Inc.KenilworthUSA
  5. 5.BiogenCambridgeUSA
  6. 6.PfizerOld SaybrookUSA
  7. 7.Nonclinical Statistics, Abbvie Inc.North ChicagoUSA

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