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E-RESOURCE

Title Statistical applications for chemistry, manufacturing and controls (CMC) in the pharmaceutical industry / Richard K. Burdick [and others].

Published Cham : Springer, 2017.

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Location Call No. Status
 UniM INTERNET resource    AVAILABLE
Physical description 1 online resource (383 pages).
Series Statistics for biology and health
Statistics for biology and health.
Springer Mathematics and Statistics eBooks 2017 English+International
Contents Chapter 1: Introduction; 1.1 Objectives; 1.2 Regulatory Guidance for CMC Applications; 1.3 Use of Statistical Tools in Pharmaceutical Development and Manufacturing; 1.4 Differences Between Clinical and CMC Statisticians; 1.5 How to Use This Book; References; Chapter 2: Statistical Methods for CMC Applications; 2.1 Introduction; 2.2 Statistical Analysis in a CMC Environment; 2.2.1 Initial Client Meeting; 2.2.2 Planning of Statistical Analysis; 2.2.2.1 Statement of the Study Objective; 2.2.2.2 Data Acquisition; 2.2.2.3 Selection of a Statistical Tool; 2.2.3 Data Analysis.
2.2.3.1 Obtain the Data2.2.3.2 Plot the Data; 2.2.3.3 Estimate the Unknown Quantities of Interest; 2.2.3.4 Quantify the Uncertainty in the Point Estimates Using a Statistical Interval; 2.2.4 Communication of Results to Client; 2.3 Data Rounding and Reporting of Results; 2.4 Use of Tables and Graphs; 2.5 Statistical Intervals; 2.5.1 Confidence Intervals; 2.5.2 Prediction Intervals; 2.5.3 Tolerance Intervals; 2.5.4 Individual Versus Mean; 2.5.5 Formula Notation; 2.6 Intervals for One Population (Independent Measurements); 2.6.1 Confidence Interval for Mean.
2.6.2 Confidence Interval for Variance2.6.3 Confidence Interval on the Standard Deviation; 2.6.4 Confidence Interval on the Percent Relative Standard Deviation; 2.6.5 Confidence Interval for Proportion Out of Specification; 2.6.6 Prediction Interval for the Next Observed Process Value; 2.6.7 Tolerance Interval for all Future Process Values; 2.6.8 Comparison of Statistical Intervals; 2.6.9 Data Sets with LOQ Values; 2.6.10 Non-Normal Data; 2.7 Intervals for One Population (Dependent Measurements); 2.7.1 Confidence Interval for Mean.
2.7.2 Confidence Intervals for Individual Variances, the Sum of the Variances, and the Ratio2.7.3 Prediction Interval for the Next Observed Process Value; 2.7.4 Tolerance Interval for All Future Process Values; 2.7.5 Modifications for Unbalanced Designs; 2.8 Comparing Two Populations (Independent Measurements); 2.8.1 Confidence Interval for Difference in Means; 2.8.2 Confidence Interval for the Effect Size; 2.8.3 Confidence Interval for the Ratio of Two Variances; 2.9 Confidence Interval for Difference of Means (Dependent Measurements); 2.10 Basics of Hypothesis Testing.
2.10.1 Statement of Hypotheses2.10.2 Testing Errors and Power; 2.10.3 Using Confidence Intervals to Conduct Statistical Tests; 2.10.4 Using p-Values to Conduct a Statistical Test; 2.11 Equivalence Testing; 2.12 Regression Analysis; 2.12.1 Linear Regression with One Predictor Variable; 2.12.2 Checking Regression Assumptions with Residual Plots; 2.12.3 Multiple Regression Analysis; 2.12.3.1 Model Definitions; 2.12.3.2 Regression Calculations; 2.12.4 Incorporating Interaction and Quadratic Effects; 2.12.5 Incorporating Qualitative Predictor Variables.
Notes 2.12.6 Nonlinear Models Using Variable Transformation.
Bibliography Includes bibliographical references and index.
Summary This book examines statistical techniques that are critically important to Chemistry, Manufacturing, and Control (CMC) activities. Statistical methods are presented with a focus on applications unique to the CMC in the pharmaceutical industry. The target audience consists of statisticians and other scientists who are responsible for performing statistical analyses within a CMC environment. Basic statistical concepts are addressed in Chapter 2 followed by applications to specific topics related to development and manufacturing. The mathematical level assumes an elementary understanding of statistical methods. The ability to use Excel or statistical packages such as Minitab, JMP, SAS, or R will provide more value to the reader. The motivation for this book came from an American Association of Pharmaceutical Scientists (AAPS) short course on statistical methods applied to CMC applications presented by four of the authors. One of the course participants asked us for a good reference book, and the only book recommended was written over 20 years ago by Chow and Liu (1995). We agreed that a more recent book would serve a need in our industry. Since we began this project, an edited book has been published on the same topic by Zhang (2016). The chapters in Zhang discuss statistical methods for CMC as well as drug discovery and nonclinical development. We believe our book complements Zhang by providing more detailed statistical analyses and examples.
Other author Burdick, Richard K.
LeBlond, David J.
Pfahler, Lori B.
Quiroz, Jorge.
Sidor, Leslie.
Vukovinsky, Kimberly.
Zhang, Lanju.
SpringerLink issuing body.
Subject Pharmacology -- Statistical methods.
Electronic books.
ISBN 9783319501864 (electronic bk.)
3319501860 (electronic bk.)
9783319501840 (print)
3319501844
Standard Number 10.1007/978-3-319-50186-4