Transforming Data into Informed Decisions across Clinical and Non-Clinical Domains

This book sheds new light on metrics like Minimal Clinically Important Difference (MCID) or Minimal Important Difference (MID) by discussing their application beyond traditional medical fields into non-clinical domains such as education, environmental studies, business analytics, finance, linguistics, economics, and biology. It addresses a significant research gap by demonstrating the utility of MCID/MID in enhancing decision-making processes across various scientific fields.
The chapters cover topics such as the theoretical foundations of MCID/MID, methodological approaches for determining these metrics, and their application in diverse contexts. Readers will learn about the importance of MCID in assessing meaningful changes in speech therapy, biology, ecological restoration projects, and more. The book also explores the complexities of Health Technology Assessment (HTA), highlighting methodological diversities and the tension between universal and context-specific thresholds.
Researchers in fields ranging from clinical medicine to social sciences will find this book invaluable. It offers insights into integrating MCID/MID metrics into telemedicine and remote healthcare while addressing underexplored areas in non-clinical research. This volume is a must-read for anyone interested in enhancing data-driven decision-making through meaningful outcomes.

Prof. Dr. István Fekete is Professor of Research Methods and Statistics at the Amity University, Kolkata, India. For almost two decades, he has been teaching statistics and working with medical claims data, and leading analysis of clinical studies in both academic and industrial environments. He is the author of textbooks and numerous scientific articles. This book is born out of his interdisciplinary career that spans statistics, psychology, linguistics and Health Technology Assessment (HTA). By integrating his experiences in HTA and research, this book aims to empower readers from various sectors to critically evaluate statistically significant differences through a lens that values meaningful outcomes.