Design of Experiments and Advanced Statistical Techniques in Clinical Research

380 Pages·2020·17.602 MB·English

by  Basavarajaiah D. M., Bhamidipati Narasimha Murthy

Design of Experiments
and Advanced
Statistical Techniques
in Clinical Research
Basavarajaiah D. M.
Bhamidipati Narasimha Murthy
123
Design of Experiments and Advanced
Statistical Techniques in Clinical
Research
Basavarajaiah D. M.
Bhamidipati Narasimha Murthy
Design of Experiments
and Advanced Statistical
Techniques in Clinical
Research
Basavarajaiah D. M. Bhamidipati Narasimha Murthy
Karnataka Veterinary, Animal and National Health Mission, Govt. of India
Fisheries Sciences University National Institute of Epidemiology
Karnataka Chennai
India India
ISBN 978-981-15-8209-7 ISBN 978-981-15-8210-3 (eBook)
https://doi.org/10.1007/978-981-15-8210-3
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Preface
Statistics is the grammar of science and unique among the academic disci-
plines, and statistical thought is needed at every stages of  research
study almost all research investigations including planning, selecting the
sample, managing the massive research data sets, and interpreting the resulted
findings. Statistics is the science of learning from data and of measuring,
controlling, and communicating uncertainty, providing a scientific naviga-
tional tools essential for taking appropriate clinical decisions at crucial times.
Both clinical and statistical reasonings are very important to progress in med-
ical research. The clinical research must infer from a few to many and com-
bine evidence with several theory. Scientific discipline and study of empirical
knowledge is generated from practical observations and ramification
with research interest. Medical and statistical theories are based on estab-
lished and existing hypotheses radically derived from mathematical interven-
tion and real probability. Always Medical research  requires both a theoretical
basis in science and statistical support for testing real hypothetical state-
ments about any kind of research based on the observed data sets, the theo-
retical and applied statistical methods layout is formally accounting for
known sources of controlling random state of clinical variables and vari-
ous attributes, (even so we shall estimate patients response rate to treatment
and efficacy). In addition, the use of statistics in clinical trial allows the clini-
cal researcher to form reasonable inference at sample and larger popula-
tion level. Infact, the banquet of new statistical approach can be used to
delineate various patterns present in the medical science, indicate various
simulation methods absolutely report for checking randomness and uncer-
tainties in the varied research setup, and as such used to draw the effective
inference about the research being studied. The pragmatic knowledge of sta-
tistics will be derived necessary tools and overall conceptual analytical foun-
dation for qualitative and quantitative reasoning, to extract the true research
evidence from experimental area (infer new findings from the massive data
sets of clinical and medical researches); this is often supported for testing the
null hypothesis (H ) effectively. Practically now, we have observed that health
0
is an important quotient for individual species, especially the days when
health domains show negative trend in developing and developed countries,
for example, the recent spikes of COVID19 in 215 countries, and the remain-
ing countries are also not free from this deadly disease. As far as human life
is concerned, all governments and private agencies seek prevention and con-
trol measures such as treatment with existing drugs and development of
vii
viii Preface
vaccine or new drugs at population level. In this overall research frame,
advanced statistical methods are needed to understand the complex phenom-
enon of COVID 19 with real-life data sets on patient selection, early screen-
ing of infected patients from machine learning tools, evaluation of cases
based on clinical perspectives, etc. Still, for prophylaxis, development of vac-
cine is very essential for prevention of COVID 19. The FDA and WHO have
already initiated steps in this direction, and clinical trials in different epi-
demic sites by using quality-by-design approach are in progress. Advanced
design of experiments and statistical methods are needed to develop new vac-
cines and understand the entire process of drug or vaccine development (until
the approval of FDA) for the health benefits of human beings. More advance-
ment for developing a drug or vaccine through trials especially for COVID
19, one has to consider genetic complexity in the population and its interac-
tion with environmental factors. For this purpose, advanced statistical and
genetic models are to be developed and tested with real-life data sets.
Similarly, pharmacokinetic models to study drug metabolism, its duration,
and its effectiveness are also needed to be considered. In the present classic
book, we are inspired and motivated to explore various new analytical choices
to escalate advanced statistical methods and its mechanisms, new methods
that help us to know the relation between different clinical attributes, to
describe real data sets and formulation of necessary design of experiments
(DOE) for conducting drug or new vaccine trials at global level. Eventually,
advanced statistical methods and design of experiments (DOE) tools are in a
close association between the researcher to understand and anticipate new
clinical findings and to know the relationship between causative factors and
its associated variables; in turn these variables will be tested for credibility
and real probabilities. Many statistical techniques developed earlier by scien-
tists/researchers so far used for clinical or drug trials in various kinds of dis-
eases are fundamental and derived from frequentistic approach for handling
new clinical and medical research datum for evaluating and applying prior
research interventions. With these postulates, the present book discusses dis-
tinct methods for building predictive and probability distribution models in
clinical situations and recent ways to assess the stability of these models to
draw qualitative conclusion using real-life experimental data sets. Post hoc
tests are used for comparing treatment effects and precision of the experimen-
tation at greater accuracy. Initially, the book starts with basic ingredients for
design of experiments in clinical and medical research and statistical methods
for analysis. Then, step-by-step various complex design of experiments and
necessary advanced statistical methods or models are developed using intel-
lectual conceptualization of clinical existence, natural histories, clinical
response with likelihood and illustrated with real-life data sets. Selection of
suitable or appropriate designs, sample selection for clinical trials, use of
neural networks, adoption of genetic and environmental factors, pharmacoki-
netic mechanism, advanced imputation methods for usually occurring miss-
ing observations, and above all ethical considerations are some of the
challenges that are faced while writing this book. The present book covers 12
chapters, all chapters briefly describe new intervention of design of experi-
ments and advanced statistical methods. Chapter 1 deals with the design of
Preface ix
clinical research and its practical approach; Chap. 2 briefly describes
advanced design of experiment approach to clinical and medical research;
Chap. 3 brings newer techniques of random forest and concept of decision
tree model association with clinical and medical research and how to con-
struct different trees based on the observed data sets; Chap. 4 demonstrates
different applications of machine learning in medical research; Chaps. 5 and
6 briefly describe advanced statistical genetics and its application in drug trail
and theoretical implications for estimation of genetic traits in human vaccine
trials; Chap. 7 focuses on foundation of statistical implications and its practi-
cal approach to research methodology; Chaps. 8 and 9 deal with various sta-
tistical models in relation with life-threatening diseases demonstrated with
real-life data sets and meta-analysis; Chap. 10 describes in depth pharmaco-
kinetic and statistical modeling; Chaps. 11 and 12 describe advanced tools of
imputation methods for deriving missing observations and ethical perspective
approach to clinical and medical research, etc. All chapters are derived with
theoretical formulations and their applications and necessary conclusions
with eye catching illustrations and fashionable diagrams. The reader should
easily understand all the concepts of analysis with practical intuition. Though,
we conceived the idea of writing this book in calendar year 2016, the actual
process started 2 years ago, and the true impulse is happened after the COVID
19 lockdown period. On a more applied level, clinicians and researchers need
basic understanding and good apprehension toward statistics well enough to
follow and evaluate the real empirical studies (e.g., formulation of random-
ized control trail) that provide insight and evidence base for clinical practices.
We hope that this book will be very useful for clinical trial investigators or
researchers, applied statisticians, planners, and policymakers, especially on
health and environment, research scholars, and academicians for furthering
their skills on design of experiments and variou recent applications of
advanced statistical methods.
Karnataka, India Basavarajaiah D. M.
New Delhi, India Bhamidipati Narasimha Murthy
Acknowledgments
This book is based on real-life research data sets collected from different
health institutes and academic universities. We are grateful to the Karnataka
Veterinary Animal and Fisheries Sciences University authority, Bidar,
National Institute of Epidemiology of Indian Council of Medical Research
and National Health Mission, Ministry of Health and Family Welfare,
Government of India, for giving permission to publish this book. Firstly, we
would like to extend our sincere thanks to Prof. H.D. Narayanaswamy,
Hon’ble Vice Chancellor, KVAFSU (B), Director, National Health Mission
(NHM) and Secretary (DHR) and Director General, ICMR, for their moral
support and guidance, and also we extend our whole hearted thanks to all the
key officers and teachers of KVAFSU(B), Prof. H.M. Jayaprakasha, Dean,
Dairy Science College, Hebbal, Bengaluru; Prof. KC Veeranna, Registrar;
Prof. NA Patil, Director of Extension; Prof Sri BV Shiva Prakash, Director of
Research; Prof. Manik Kishan Tandle, Director of Instruction, Postgraduation
Studies; Prof. Narayan Bhat, Dean Veterinary College, Bengaluru; Prof.
Mohamed Nadeem Fairoze, Former PG Dean, KVAFSU(B); Prof.
H.N. Narasimhamurthy, Former Dean, VCH (B); Prof. Jayanaik; and Prof.
Dr. Mrs. Bharathi (Sociology), Former Dean, Dr. M D Surangi PPMC
head for their constant encouragement. We would like to acknowledge Mrs.
Netra Rajpurohit for her technical support on proof reading. Finally our sin-
cere thanks and highest gratitude to our beloved family members for their
constant encouragement and support toward completion of the entire work.
This book would not have been possible to be brought out without them. We
feel immensely proud for extending our heartiest thanks to the various health
institutes for providing research data for embedding this beautiful book.
xi
Contents
1 Designs of Clinical Research and Its Practical Approach . . . . . . 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Identification of Research Problem . . . . . . . . . . . . . . . 3
1.1.2 Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1.3 Formulating the Research Question . . . . . . . . . . . . . . . 4
1.1.4 Research Proposal Writing. . . . . . . . . . . . . . . . . . . . . . 4
1.1.5 Institutional Review Board (IRB) . . . . . . . . . . . . . . . . 4
1.1.6 Data Collection and Compilation. . . . . . . . . . . . . . . . . 4
1.1.7 Dissemination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Practical Implication of Study Design in Clinical Research . . . 5
1.2.1 Objectives of the Book . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Statistical Historical Perspectives of Clinical Trail . . . . . . . . . 6
1.4 Global Milestone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4.1 Indian Milestone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.5 Clinical Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.5.1 Intervention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.5.2 Nonintervention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6 Types of Clinical Research . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.6.1 Patient-Oriented Research . . . . . . . . . . . . . . . . . . . . . . 10
1.6.2 Epidemiological and Behavioral Studies . . . . . . . . . . . 10
1.6.3 Outcome and Health-Related Services Research . . . . . 10
1.7 Risk in Clinical Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.8 Clinical Trial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.8.1 Treatment Trial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.8.2 Prevention Trial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.8.3 Quality of Life Trial . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.8.4 Diagnostic Trial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.9 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.10 Brief Concept of Study Design . . . . . . . . . . . . . . . . . . . . . . . . 14
1.11 Experimental Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.11.1 Randomized Controlled Trail (RCT) . . . . . . . . . . . . . . 15
1.11.2 Non-randomized Controlled Trail (RCT) . . . . . . . . . . 15
1.12 Randomization Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.12.1 Salient Properties of Randomization . . . . . . . . . . . . . . 18
1.12.2 Elimination of Selection Effects . . . . . . . . . . . . . . . . . 18
xiii