How to cite:
Mahajan, V.; Sanker MK, V.; Mathur, A. Clinical Research in Ayurveda - Can Statistics as A Scientific Discipline Help the Cause?. Preprints2022, 2022060101. https://doi.org/10.20944/preprints202206.0101.v1
Mahajan, V.; Sanker MK, V.; Mathur, A. Clinical Research in Ayurveda - Can Statistics as A Scientific Discipline Help the Cause?. Preprints 2022, 2022060101. https://doi.org/10.20944/preprints202206.0101.v1
Mahajan, V.; Sanker MK, V.; Mathur, A. Clinical Research in Ayurveda - Can Statistics as A Scientific Discipline Help the Cause?. Preprints2022, 2022060101. https://doi.org/10.20944/preprints202206.0101.v1
APA Style
Mahajan, V., Sanker MK, V., & Mathur, A. (2022). Clinical Research in Ayurveda - Can Statistics as A Scientific Discipline Help the Cause?. Preprints. https://doi.org/10.20944/preprints202206.0101.v1
Chicago/Turabian Style
Mahajan, V., Vivek Sanker MK and Aswhini Mathur. 2022 "Clinical Research in Ayurveda - Can Statistics as A Scientific Discipline Help the Cause?" Preprints. https://doi.org/10.20944/preprints202206.0101.v1
Abstract
Ayurveda as a healthcare system has survived for thousands of years but continues to be dogged by reported lack of efficacy of the treatments in clinical trials. The reported lack of efficacy could be due to a real lack of efficacy (which then contradicts the survival of Ayurveda as a functional medical system enjoying considerable public patronage) or could be attributed to inadequacies in the efforts towards evidence generation or in a larger context the overall scientific conduct of research in Ayurveda. In an effort towards better evidence generation, there is an immediate need for standardizing the design, conduct and reporting of clinical trials of Ayurveda but it is a daunting task. For this effort to benefit the scientific endeavors of Ayurveda researchers, it should allow the researchers to be able to apply Ayurveda’s multi-component, individualized and inherently holistic approach. Statistical principles can benefit this effort. Statistical hypothesis testing (SHT) is central to these statistical principles and also aligns well with conventional scientific principles of evidence generation. Although there are challenges with SHT, good practitioners engaged in it do much more than just apply the mathematical theory behind it. As a particular example, lot of time in clinical trial designing is spent in addressing biases and designing trials prudently by minimizing the effect of such biases. SHT can benefit such an effort objectively. There is a need for Ayurveda researchers to engage deeply and mindfully about biases in study design in order to gain scientific validity and acceptability. The article highlights issues that arise in Ayurveda research, and discusses few ways of dealing with these issues using statistical principles.
Keywords
Ayurveda research; good research; hypothesis generation; hypothesis testing; managing bias and sample size
Subject
Medicine and Pharmacology, Clinical Medicine
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
