Bayesian Applications in Pharmaceutical Development

533 Pages·2019·14.455 MB·\533

by  Mani Lakshminarayanan (Editor), Fanni Natanegara (Editor)

Edited by Mani Lakshminarayanan and Fanni Natanegara
Bayesian Applications in
Pharmaceutical
Development
Chapman & Hall/CRC Biostatistics Series
Shein-Chung Chow
Duke University School of Medicine
Byron Jones
Novartis Pharma AG
Jen-pei Liu
National Taiwan University
Karl E. Peace
Georgia Southern University
Bruce W. Turnbull
Cornell University
Analysis of Incidence Rates
Peter Cummings
Cancer Clinical Trials: Current and Controversial Issues in Design and
Analysis
Stephen L. George, Xiaofei Wang, Herbert Pang
Data and Safety Monitoring Committees in Clinical Trials 2nd Edition
Jay Herson
Clinical Trial Optimization Using R
Alex Dmitrienko, Erik Pulkstenis
Mixture Modelling for Medical and Health Sciences
Shu-Kay Ng, Liming Xiang, Kelvin Kai Wing Yau
Economic Evaluation of Cancer Drugs: Using Clinical Trial and Real-World
Data
Iftekhar Khan, Ralph Crott, Zahid Bashir
Bayesian Analysis with R for Biopharmaceuticals: Concepts, Algorithms,
and Case Studies
Harry Yang and Steven J. Novick
Mathematical and Statistical Skills in the Biopharmaceutical Industry: A
Pragmatic Approach
Arkadiy Pitman, Oleksandr Sverdlov, L. Bruce Pearce
Bayesian Applications in Pharmaceutical Development
Mani Lakshminarayanan, Fanni Natanegara
For more information about this series, please visit:
https://www.crcpress.com/go/biostats
Edited by Mani Lakshminarayanan and Fanni Natanegara
Bayesian Applications in
Pharmaceutical
Development
CRCPress
Taylor&FrancisGroup
6000BrokenSoundParkwayNW,Suite300
BocaRaton,FL33487-2742
(cid:13)c 2020byTaylor&FrancisGroup,LLC
CRCPressisanimprintofTaylor&FrancisGroup,anInformabusiness
NoclaimtooriginalU.S.Governmentworks
Printedonacid-freepaper
InternationalStandardBookNumber-13:978-1-1382-9676-3(Hardback)
This book contains information obtained from authentic and highly regarded sources.
Reasonableeffortshavebeenmadetopublishreliabledataandinformation,buttheauthor
and publisher cannot assume responsibility for the validity of all materials or the conse-
quences of their use. The authors and publishers have attempted to trace the copyright
holdersofallmaterialreproducedinthispublicationandapologizetocopyrightholdersif
permissiontopublishinthisformhasnotbeenobtained.Ifanycopyrightmaterialhasnot
beenacknowledged,pleasewriteandletusknowsowemayrectifyinanyfuturereprint.
Except as permitted under U.S. Copyright Law, no part of this book may be reprinted,
reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other
means,nowknownorhereafterinvented,includingphotocopying,microfilming,andrecord-
ing,orinanyinformationstorageorretrievalsystem,withoutwrittenpermissionfromthe
publishers.
For permission to photocopy or use material electronically from this work, please access
www.copyright.com (http://www.copyright.com/) or contact the Copyright Clearance
Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a
not-for-profitorganizationthatprovideslicensesandregistrationforavarietyofusers.For
organizations that have been granted a photocopy license by the CCC, a separate system
ofpaymenthasbeenarranged.
Trademark Notice: Product or corporate names may be trademarks or registered
trademarks,andareusedonlyforidentificationandexplanationwithoutintenttoinfringe.
Visit the Taylor & Francis Web site at
http://www.taylorandfrancis.com
and the CRC Press Web site at
http://www.crcpress.com
Contents
Preface vii
Editors xi
Contributors xiii
1 Introduction 1
Mani Lakshminarayanan and Fanni Natanegara
2 Incorporation of Historical Control Data in Analysis 17
of Clinical Trials
G. Frank Liu, Wen Li, and A. Lawrence Gould
3 Practical Considerations for Building Priors for 49
Confirmatory Studies
Guochen Song, John Zhong, Stacy Lindborg, and Baoguang Han
4 The Practice of Prior Elicitation 61
Timothy H. Montague, Karen L. Price, and John W. Seaman, Jr.
5 Bayesian Examples in Preclinical In Vivo Research 93
John Sherington, Phil Stanley, and Ros Walley
6 Planning a Model-Based Bayesian Dose Response Study 125
Neal Thomas
7 Novel Designs for Early Phase Drug-Combination Trials 141
Ying Yuan and Ruitao Lin
8 Executing and Reporting Clinical Trial Simulations: 169
Practical Recommendations for Best Practices
Cory R. Heilmann, Fanni Natanegara, Maria J. Costa,
Matilde Sanchez-Kam, and John W. Seaman, Jr.
9 Reporting of Bayesian Analyses in Clinical Research: 187
Some Recommendations
Melvin Munsaka and Mani Lakshminarayanan
10 Handling Missing Data in Clinical Trials with Bayesian 225
and Frequentist Approaches
Xin Zhao, Baoguang Han, John Zhong, Stacy Lindborg,
Neal Thomas, and G. Frank Liu
v
vi Contents
11 Bayesian Probability of Success for Go/No-Go 247
Decision Making
Qi Tang
12 Simulation for Bayesian Adaptive Designs—Step-by-Step 267
Guide for Developing the Necessary R Code
J. Kyle Wathen
13 Power Priors for Sample Size Determination 287
in the Process Validation Life Cycle
Paul Faya, John W. Seaman, Jr., and James D. Stamey
14 Bayesian Approaches in the Regulation of Medical 307
Products
Telba Irony and Lei Huang
15 Computational Tools 329
David Kahle and Michael Sonksen
16 Software for Bayesian Computation—An Overview 375
of Some Currently Available Tools
Melvin Munsaka and Mani Lakshminarayanan
17 Considerations and Bayesian Applications in 409
Pharmaceutical Development for Rare Diseases
Freda Cooner
18 Extrapolation Process in Pediatric Drug Development 427
and Corresponding Bayesian Implementation for
Validating Clinical Efficacy
Margaret Gamalo-Siebers and Simin Baygani
19 A Brief Guide to Bayesian Model Checking 483
John W. Seaman, Jr., James D. Stamey, David J. Kahle, and
Somer Blair
Index 503
Preface
Drug discovery and drug development have a long history with primitive
stages dating back to the early days of human civilization. The long jour-
ney of the pharmacological treatment of disease began long ago with the
use of herbs in Chinese medicine, Japanese traditional medicine, Ayurvedic
medicine(traditionalIndianmedicine),andseveralotherformsbeforeventur-
ing into synthetic drugs in the 1900s. The introduction of synthetic drugs led
tothefoundingofpharmaceuticalindustrieswhichcontinuetoinvestmillions
of dollars in the research and development of innovative treatment for both
common and rare diseases. Key discoveries of the 1920s and 1930s, such as
insulin and penicillin, were game changers during wartime in treating infec-
tions and resulted in mass manufacturing and distribution. Screening natural
products and isolating active ingredients for treating diseases became part of
drugdiscoveryintheearly1930s.Theactiveingredientsarenormallythesyn-
thetic versions of the natural products. These synthetic versions, called New
Chemical Entities (NCEs), have to go through many iterations and tests to
ensure they are safe, potent, and effective.
Use of randomized clinical trials and rigorous statistical principles for
establishing the safety and effectiveness of NCEs was first investigated in
the 1948 evaluation of streptomycin for the treatment of tuberculosis by the
British Medical Research Council under the direction of Sir Austin Brad-
ford Hill, an epidemiologist and statistician. The concept of a double-blind
placebo-controlled trial was introduced in the field trial of the Salk polio vac-
cine in 1954. Before World War II, no formal scientific rationale was required
for conducting clinical trials before marketing a drug. In the 1960s, evidence
from controlled clinical trials became necessary to get an approval for a new
drugfromtheFoodandDrugAdministration(FDA).Thisscientificrationale
was further enhanced through the efforts of the International Conference on
Harmonization (ICH) in standardizing processes for drug approvals.
Statistical principles for clinical trials described in the ICH E-9 guideline
(https://www.ich.org/fileadmin/Public Web Site/ICH Products/Guidelines/
Efficacy/E9/Step4/E9 Guideline.pdf, 1998) focused on giving directions to
researchers in the design, conduct, analysis, and evaluation of trials using fre-
quentist approach. The FDA’s Critical Path Initiative in 2004 transformed
the way FDA-regulated medical products are developed, evaluated, and man-
ufactured. The Initiative’s report recognized that there is a widening gap
between scientific discoveries and their translation into medical treatments.
It also positioned the Bayesian approach as a tool to create innovative and
vii
viii Preface
efficientclinicaltrials.In2010,theCenterforDevicesandRadiologicalHealth
of the FDA issued an elaborate guidance for the use of Bayesian statistics in
clinical trials involving medical devices.
This book presents a plethora of topics behind Bayesian applications in
pharmaceuticaldevelopment.Itisanextensionofsomeofthegreateffortsput
forth by most of the authors in this book who participated in the Bayesian
Scientific Working Group of the Drug Information Association. This work-
ing group, started in 2011, states its mission as follows: “to ensure that
Bayesian methods are well-understood and broadly utilized for design and
analysisthroughoutthemedicalproductdevelopmentprocessandtoimprove
industrial, regulatory and economic decision making”. The group is com-
prised of individuals from academia, industry, and regulatory authorities
(http://www.bayesianscientific.org/).Authorswhohavecontributedchapters
to this book also participated as members of various subteams that were
formed to deal with topics such as non-inferiority, safety, missing data, and
others, most of which have resulted in publications.
Our intention is not only to provide a platform that will cover the entire
spectrum of development of medical products, from pre-clinical to post-
marketing, but also to take advantage of the great efforts put forth by these
subteam members who have contributed to different topics in the use of
Bayesian applications. It is our intention that this book would serve as a
“go-to source” for the topics on the use of Bayesian methods in biopharma-
ceutical development. The principal audience for this book are statisticians,
clinicians, pharmacologists, scientists, engineers, and epidemiologists who are
typical members of any medical product development team. We believe that
these members can use this book as a single source to find out much more
about different topics than is currently available in multiple sources in the
public domain. Besides providing theoretical development, the authors have
also attempted to provide computer codes if and when required under each
topic,inadditiontocasestudiesasabasisfordescribingsomeoftheBayesian
solutions.Wewerealsoveryconsciousinensuringthattheprincipalemphasis
of the book is to describe the problems that occur and the solutions that are
used “in practice” rather than in theory.
This book has been designed to show many of the problems and solu-
tions associated with applying Bayesian methods appropriately in the life of
a medical product. The chapters in this book are written with the personal
experience of the authors in developing and getting regulatory approval of
such products. Several of our colleagues who have collaborated in the DIA
Bayesian Scientific Working Group have also contributed tirelessly to vari-
ous chapters in this book. First and foremost, we wish to thank all of the
contributors for their efforts and patience as we strove toward completion of
this undertaking. We hope that this book will pave way to the understanding
and correct usage of Bayesian approaches and, as a result, will provide yet
another statistical tool for strengthening regulatory applications. We are also
thankful for the wonderful support of our family members. Finally, we take
Preface ix
this opportunity to thank John Kimmel of Taylor and Francis for his support
and patience throughout the development of this book.
Mani Lakshminarayanan
Statistical Consultant
Fanni Natanegara
Eli Lilly and Company, Indianapolis, IN