Exploring Medical Statistics: Biostatistics, Clinical Trials, and Epidemiology

ISBN-13: 9798369366424 / Angielski / Miękka / 2024 / 363 str.

Geeta Arora, Sarabjit Singh, Homan Emadifar

Opis

In today's data-driven world, understanding and interpreting statistical information is more critical than ever, especially in medicine, where statistical methods are used to design and analyze clinical trials, study the distribution of disease in populations, and develop new treatments. In the era of evidence-based medicine, Exploring Medical Statistics: Biostatistics, Clinical Trials, and Epidemiology addresses the critical need for a grasp of statistical concepts. This book delves into biostatistics, clinical trials, and epidemiology, offering a robust foundation for understanding and interpreting statistical information in medicine. It explores biostatistics, elucidating fundamental elements such as probability, sampling, and hypothesis testing. The section on clinical trials covers the entire spectrum from trial design to ethical considerations, providing an invaluable resource for researchers navigating the complexities of medical research. Epidemiology, a cornerstone of public health, is examined in the book, offering insights into the distribution and determinants of diseases in populations. The application-focused section further extends the utility of medical statistics, encompassing public health, healthcare policy, and drug development. The target audience is diverse, including medicine and public health students, medical field researchers, and healthcare practitioners. The book's accessibility, clear explanations, and relevant examples accommodate varying levels of statistical knowledge. The book also appeals to a broader audience interested in the intersection of statistics and healthcare, presenting a clear overview of principles and methods without overwhelming technical jargon. Its relevance extends to those intrigued by the future trajectory of medical statistics, touching on topics such as survival analysis, Bayesian statistics, and the integration of machine learning and artificial intelligence.