Biostatistics Explored Through R Software: An Overview

By Vinaitheerthan Renganathan

The term Statistics and Data is now familiar to most of us even though we are related to different fields such as Clinical, Biology, Business & Management, Engineering and Physical Sciences.  Statistical methods are being applied in different fields including the emerging fields such as Machine Learning, Natural Language Processing and Artificial Intelligence because of the fact that each field deals with data in one form or the other.

 

Statistical methods help decision makers to arrive at evidence based decisions by collecting, analyzing and interpreting the data. Biostatistics is a branch of statistics which is applied in the biomedical domain and it includes clinical and biological fields. 

 

Biostatistical methods include descriptive, inferential and Statistical modeling tools. Descriptive statistical tools help us to draw information and summary about the data in terms of measures of central tendency, measures of dispersion, measure of skewness and underlying probability distributions. Inferential Statistical tools help us to estimate, test the hypothesis and draw inferences about the parameters of the data. Statistical modeling tools help us to study and estimate the relationship between the variables.   

 

The book starts with the introduction of concepts in biostatistics and moves on to explore descriptive and inferential statistical tools using R Open Source Software. It then explores statistical modeling tools such as regression analysis and survival analysis.

 

The book also covers the role of Biostatistics in the clinical trials process. Clinical trials are scientific experiments which are conducted in different phases. It involves testing of drugs and clinical procedures for treating different diseases and conditions among a sample of participants before rolling out in the market. Biostatistics tools help clinical trial researchers in deciding about the study design, sample size and analytical methods.

 

The data set used in the book can be downloaded from Author's Website.

 

Vinaitheerthan Renganathan

• Language: English
• Publisher: Vinaitheerthan Renganathan
• Release date: Sep 10, 2021
• ISBN: 9798201776169

Vinaitheerthan Renganathan

Statistician and Data Scientist with 26 years of experience in the field of Clincal,Manufacturing, Quality Assurance and Marketing Research.   

Book preview

Biostatistics explored through R software: An overview

Title: Biostatistics explored through R software: An overview

Author: Vinaitheerthan Renganathan

Publisher: Self Published

Edition: 1st Edition

Copyright: © 2021 Vinaitheerthan Renganathan 

Biostatistics explored through R software: An overview

Preface

The term Statistics and Data is now familiar to most of us even though we are related to different fields such as Clinical, Biology, Business & Management, Engineering and Physical Sciences.  Statistical methods are being applied in different fields including the emerging fields such as Machine Learning, Natural Language Processing and Artificial Intelligence because of the fact that each field deals with data in one form or the other.

Statistical methods help decision makers to arrive at evidence based decisions by collecting, analyzing and interpreting the data. Biostatistics is a branch of statistics which is applied in the biomedical domain and it includes clinical and biological fields. 

Biostatistical methods include descriptive, inferential and Statistical modeling tools. Descriptive statistical tools help us to draw information and summary about the data in terms of measures of central tendency, measures of dispersion, measure of skewness and underlying probability distributions. Inferential Statistical tools help us to estimate, test the hypothesis and draw inferences about the parameters of the data. Statistical modeling tools help us to study and estimate the relationship between the variables.   

The book starts with the introduction of concepts in biostatistics and moves on to explore descriptive and inferential statistical tools using R Open Source Software. It then explores statistical modeling tools such as regression analysis and survival analysis.

The book also covers the role of Biostatistics in the clinical trials process. Clinical trials are scientific experiments which are conducted in different phases. It involves testing of drugs and clinical procedures for treating different diseases and conditions among a sample of participants before rolling out in the market. Biostatistics tools help clinical trial researchers in deciding about the study design, sample size and analytical methods.

The data set used in the book can be downloaded from Author’s Website.

Vinaitheerthan Renganathan

Contents

Chapter 1:  Introduction

Chapter 2:  R Software

Chapter 3:  Descriptive Statistics

Chapter 4:  Probability and Probability Distributions

Chapter 5:  Sampling Methodology and sampling distributions

Chapter 6:  Estimation and Statistical Inference

Chapter 7:  Correlation and Regression analysis

Chapter 8:  Survival Analysis

Chapter 9:  Biostatistics in Randomized Clinical Trials (RCT)

Chapter 10:  Meta-Analysis and Systematic reviews

Chapter 1:  Introduction

Statistics starts with numbers (data) and transforms it to useful and actionable information.  The basic definition of Statistics includes collection, compilation, summarization, analysis and interpretation of data.

Statistical tools are mainly useful when the outcome of an event or experiment is random in nature. The real time examples where the statistical tools are used are finding patient survival rate, comparing efficacy of drugs, forecasting the stock price movement. Probability and Statistical Distributions plays an important role in dealing with the situations which involve uncertainty. 

Now with the introduction of Big Data, the need for using appropriate tools to analyze and make conclusions out of the large stream of data is becoming essential part of any modern systems. Statistical methods guides us in choosing the appropriate tools in analyzing this large amount of Big Data (3 V’s.) which is generated in high Volume, Veracity (speed) and Variety (types) using different open source and commercial software.

Biostatistical tools help clinicians and researchers in arriving at decisions related to patient diagnosis, prognosis, surgical procedures, laboratory and radiological investigations based on the data. 

Biostatistical tools also play an important role in clinical trials where in efficacy of treatment and procedures are tested on human subjects. Biostatistical tools are used from the planning stage to analytical and report preparation stage in clinical trials.

Important concepts

Let us start with some important concepts in Biostatistics.

Data

Data is the basic form or tool in any biostatistics application. Generally data can include different types such as numbers, texts, audio and video forms. Most of the Biostatistical tools deal with numerical form of data and but now with the help of Machine learning and Deep Learning methods we can handle text, audio and video form data types. 

Variable

Variable holds information about particular entity or feature such as name, age, gender and it can take any value with respect to the type its belongs. Continuous variables can any takes values between two intervals wherein discrete variables take only integer values.

Constant

Constant is an entity whose value remains same and example for constant is the mathematical entity π.

Independent Variables

Independent Variables are those variables whose