eBook[PDF]

Biostatistical Methods and Applications in Health Research: A Case Study Approach By Vivek Verma, Hafiz Khan, Dilip Nath, Kenneth Land

$30.00

✓ Secure checkout

✓ Instant digital download

✓ PDF document

Document details

Pages: 335
File size: 7.72 MB
Format: Digital PDF
Course: Medicine
Category: eBook[PDF]

About this ebook

The textbook Biostatistical Methods and Applications in Health Research: A Case Study Approach (published by Routledge) by Vivek Verma, Hafiz T.A. Khan, Dilip C. Nath, and Kenneth C. Land focuses on practical, data-driven applications in clinical trials, epidemiology, and public health. [1, 2]

The book is structured into sequential parts ranging from foundational math to advanced digital health modeling: [1, 2]

Part I: Basic Statistics, Probability, and Statistical Hypothesis Testing

Introduction to Biostatistics: Overview of biological data types and the critical role of data collection in medicine. [1, 2]
Descriptive Statistics: Techniques and methods utilized for summarizing health-related datasets. [1, 2]
Exploratory Data Analysis (EDA): Measures for discovering hidden trends and visual summaries in medical research. [1, 2]
Introduction to Probability: Fundamentals of mathematical probability, conditional probability, and risk estimation. [1, 2]
Probability Distributions: Deep dive into how specific datasets fit standard biological distributions (e.g., Binomial, Poisson, and Normal distributions). [1, 2]
Statistical Hypothesis Testing: Parametric and non-parametric testing frameworks specifically adapted for health science investigations. [1, 2]

Part II: Core Epidemiological & Regression Modeling

Regression Analysis: Core mathematical relationships modeled between exposures, health habits, and clinical outcomes.
Survival Analysis: Specialized techniques for evaluating time-to-event outcomes, such as clinical mortality or disease relapse.
Systematic Reviews & Meta-Analysis: Advanced statistical pooling models (including the Mantel-Haenszel and Peto odds ratio methods) designed for categorical outcomes and rare events. [1, 2]

Part III: Clinical Trials & Design Methodology

Clinical Trial Design: Practical selection of study designs, spanning observational frameworks (cohort, case-control) to interventional settings. [1, 2]
Advanced Trial Frameworks: Comparative mechanics behind crossover, superiority, non-inferiority, and equivalence trials. [1]
Sample Size & Power Calculations: Formulaic and software-assisted determination of exact target sample sizes needed for valid biomedical studies. [1]

Part IV: Advanced & Computational Methods

Diagnostic Testing & DTA: Statistical metrics to evaluate diagnostic test accuracy (DTA) alongside clinical study criteria like STARD and QUADAS-2.
Bayesian Biostatistics: Applying prior knowledge and probability distributions to improve diagnostic testing and clinical decision-making.
Machine Learning in Health Research: Integrating modern algorithmic modeling with standard biostatistical applications to predict health outcomes. [1, 2, 3]