R for Basic Biostatistics in Medical Research - Tapa dura

Acerca del autor

Dr. Anand Srinivasan did his MD in Pharmacology and DM in Clinical Pharmacology at the Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India. He is currently working as an Additional Professor in the Department of Pharmacology at All India Institute of Medical Sciences (AIIMS), Bhubaneswar. He likes working in R because of its flexibility and its unlimited potential in data science. Moreover, R is also an open-source tool. He uses R for statistical analysis of data generated from various clinical studies and meta-analyses for the past ten years. He also uses it for simulating various situations to answer a myriad of clinical and research questions, and for analyzing the data from population pharmacokinetic-pharmacodynamic studies. The editor has developed artificial intelligence models on healthcare data using artificial neural networks utilizing R. He has been instrumental in organizing R workshops at AIIMS, Bhubaneswar, for healthcare professionals every year since 2017.

Dr. Archana Mishra is a Senior Resident in the Department of Pharmacology at All India Institute of Medical Sciences (AIIMS), Bhubaneswar. She completed her MD from the same organization and her DM in Clinical Pharmacology from AIIMS, New Delhi. Her journey with R started in 2017 when she participated in the first National Workshop on R for Basic Biostatistics conducted at AIIMS, Bhubaneswar. She was gradually drawn to R's powerful yet intuitive statistical capabilities. Since then, she has been regularly using R for biostatistical analysis of healthcare data. She is also using R to conduct meta-analysis, network meta-analysis, pharmacokinetic modeling, clinical trial simulations, and to develop machine learning models. As she gained experience in R, she is now a part of conducting the annual workshop at AIIMS, Bhubaneswar, in R as a resource person. She explains the journey from all red errors after every line of R code in the initial days to publishing with R, an ever-rewarding and satisfying one.

Dr. Praveen Kumar pursued his MD (Pharmacology) and DM (Clinical Pharmacology) at the Postgraduate Institute of Medical Education and Research (PGIMER). His interest in R began on the day when he realized the versatility, elegance, and impression the R provides for statistical analysis. During his tenure at PGIMER, his work with R, R shiny, and machine learning models has been published in reputed medical journals. His interest vests in designing graphical charts with R and in developing interactive web interfaces with R Shiny Studio. In addition to R, he is comfortable with Python, SQL, and MATLAB. In his current position as the Head of Clinical Sciences at Nference, Bengaluru, he is proficient in solving multiple real-world data (RWD) based research questions for pharmaceutical industries using large electronic medical records (EMRs) of top academic medical centers (AMCs) like Mayo and Duke. He has been actively involved in the annual R workshop for healthcare professionals since its inception (2017) at AIIMS, Bhubaneswar.

De la contraportada

The scientific community at the global level is fast becoming aware of the rising use of open-source tools such as R and Python for data analysis. Unfortunately, in spite of the awareness, the conversion of the intrigue to the practical knowledge in utilization of the open-source tools for routine day-to-day data analysis is seriously lacking both among physicians and medical scientists. This book enables physician-scientists to understand the complexity of explaining a programming/ data-analytic language to a healthcare professional and medical scientist. It simplifies and explains how R can be used in medical projects and routine office works. It also talks about the methodologies to convert the knowledge to practice. The book starts with the introduction to the structure of R programming language in the initial chapters, followed with explanations of utilizing R in the basics of data analysis like data importing and exporting, operations on a data frame, parametric and non-parametric tests, regression, sample size calculation, survival analysis, receiver operator characteristic analysis (ROC) and techniques of randomization. Each chapter provides a brief introduction to the involved statistics, for example, dataset, working codes, and a section explaining the codes. In addition to it, a chapter has been dedicated to describing the ways to generate plots using R. This book primarily targets health care professionals and medical/life-science researchers in general.