Design Of Experiments And Advanced Statistical Techniques In Clinical Research
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Design of Experiments and Advanced Statistical Techniques in Clinical Research
Author: Basavarajaiah D. M.
language: en
Publisher: Springer Nature
Release Date: 2020-11-05
Recent Statistical techniques are one of the basal evidence for clinical research, a pivotal in handling new clinical research and in evaluating and applying prior research. This book explores various choices of statistical tools and mechanisms, analyses of the associations among different clinical attributes. It uses advanced statistical methods to describe real clinical data sets, when the clinical processes being examined are still in the process. This book also discusses distinct methods for building predictive and probability distribution models in clinical situations and ways to assess the stability of these models and other quantitative conclusions drawn by realistic experimental data sets. Design of experiments and recent posthoc tests have been used in comparing treatment effects and precision of the experimentation. This book also facilitates clinicians towards understanding statistics and enabling them to follow and evaluate the real empirical studies (formulation of randomized control trial) that pledge insight evidence base for clinical practices. This book will be a useful resource for clinicians, postgraduates scholars in medicines, clinical research beginners and academicians to nurture high-level statistical tools with extensive scope.
Advanced Statistical Methods in Life Science
This book introduces the principles and foundations of advanced statistical methods for designing experiments and testing hypotheses in life sciences. Advanced statistical methods, such as testing of hypotheses, recent methods of sample size determination/imputation, estimation techniques, probability distributions, and univariate analysis demonstrated with real data, and their integration into life sciences are included in this book. Advanced topics are presented with sufficient conceptual depth and examples to explain the use of recent statistical techniques and to demonstrate what conclusions can be drawn at the right time using modeling in life science research. Key features: Explains the derivation of statistical models to prove disease transmission using massive real-world datasets to explore practical applicability Incorporates the application of innovative advanced statistical and epidemiological models and demonstrates the possible solutions for public health policy intervention Helps to understand the process of hypothesis testing in small or larger observations by using weighted parameters Presents suitable examples and real-life research datasets, and all models can easily be followed in formulating statistical and mathematical derivations and key points Includes machine learning (ML), statistical methods for meta-analysis, testing of hypotheses, methods of imputation, estimation techniques, probability distributions, univariate analysis, and recent nonparametric methods, all illustrated through actual data This textbook is for students and scholars of various courses in life sciences, medicine, mathematics, and statistical science. It will also help academicians and researchers to understand the foundation of this topic.
Introduction to Statistical Methods, Design of Experiments and Statistical Quality Control
This book provides an accessible presentation of concepts from probability theory, statistical methods, the design of experiments and statistical quality control. It is shaped by the experience of the two teachers teaching statistical methods and concepts to engineering students, over a decade. Practical examples and end-of-chapter exercises are the highlights of the text as they are purposely selected from different fields. Statistical principles discussed in the book have great relevance in several disciplines like economics, commerce, engineering, medicine, health-care, agriculture, biochemistry, and textiles to mention a few. A large number of students with varied disciplinary backgrounds need a course in basics of statistics, the design of experiments and statistical quality control at an introductory level to pursue their discipline of interest. No previous knowledge of probability or statistics is assumed, but an understanding of calculus is a prerequisite. The whole book serves as a master level introductory course in all the three topics, as required in textile engineering or industrial engineering. Organised into 10 chapters, the book discusses three different courses namely statistics, the design of experiments and quality control. Chapter 1 is the introductory chapter which describes the importance of statistical methods, the design of experiments and statistical quality control. Chapters 2–6 deal with statistical methods including basic concepts of probability theory, descriptive statistics, statistical inference, statistical test of hypothesis and analysis of correlation and regression. Chapters 7–9 deal with the design of experiments including factorial designs and response surface methodology, and Chap. 10 deals with statistical quality control.