Journal of Indian System of Medicine

EDITORIAL
Year
: 2021  |  Volume : 9  |  Issue : 4  |  Page : 213--215

Coherence of variables in clinical trials of Ayurveda


Srihari Sheshagiri 
 Department of Kaumarabhritya, Mahatma Gandhi Ayurved Medical College, Hospital and Research Centre, Datta Meghe Institute of Medical Sciences (DU), Wardha, Maharashtra, India

Correspondence Address:
Dr. Srihari Sheshagiri
Department of Kaumarabhritya, Mahatma Gandhi Ayurved Medical College, Hospital and Research Centre, Datta Meghe Institute of Medical Sciences (DU), Wardha, Maharashtra.
India




How to cite this article:
Sheshagiri S. Coherence of variables in clinical trials of Ayurveda.J Indian Sys Medicine 2021;9:213-215


How to cite this URL:
Sheshagiri S. Coherence of variables in clinical trials of Ayurveda. J Indian Sys Medicine [serial online] 2021 [cited 2022 Jan 24 ];9:213-215
Available from: https://www.joinsysmed.com/text.asp?2021/9/4/213/334264


Full Text



Ayurveda, the age-old medical wisdom of India practised for almost 5000 years, is still relevant in the current era due to its holistic and individualistic approach to treating patients.[1] Systemic research incorporating the modern tools of research methodology in Ayurveda started approximately 83 years ago.[2] The contretemps on the methodology to understand how Ayurveda therapeutics work has coexisted since then and evolved time again.[3] Due to the lack of basic statistical benchmarks of Ayurveda concepts (variables), applying current research tools which advocate “one model fits all” is challenging.

Variables influence the design, analysis, and outcome of every research work. It is, in fact, a type of data that is to be measured based on its dispersion from the mean value (standardized by taking large representative values from the population).[4] Moreover, the main aim of statistical analysis in any scientific research work is to assess the variation in the values from the mean and understand the reason as well as the meaning expressed in numerical values. The reason may be natural or due to the intervention used in the scientific work. To have precise, valid, and reliable study results, thorough knowledge about managing the variables is necessary, which helps avoid biases.[5]

Similar to data, variables are also qualitative and quantitative. Furthermore, the qualitative variable includes nominal and ordinal, and the quantitative variable includes discrete and continuous varieties.[6] Analytically, variables are said to be of two types: independent and dependent variables.[7] Another type of variable is called a confounder, which influences the relationship between independent and dependent variables.[7] Clinically, these variables are grouped into baseline and response variables.[8]

On review of various lexicons of Ayurveda, the standard variables of Ayurveda may be clubbed as shown in [Table 1].{Table 1}

Baseline variables (BV) include recording patients’ demographic and medical characteristics before treatment or research.

The classification of BVs in Ayurveda is as follows.

Based upon the inherent qualities Prakriti, Sara, Samahanana, Sattva, Satyma, and Bala are graded as Pravara (superior), Madhyama (medium), and Avara (inferior).[9]

Desha includes Bhumi (regional) and Athura Desha (human body).[10]Bhumi Desha is said to be of three types. Vata and Kaphadosha dominance is observed in Janghala (dry region) and Anupa Desha (marshy) correspondingly. At the same time, Sadharana Desha (temperate region) has an equilibrium of all three Dosha.[11]Athura Desha includes three components, viz., examination of life span, the intensity of morbid Dosha, and an individual’s Bala.[12]

Bheshaja (medicine) in Ayurveda is selected based on Kala, which includes Kshana (measurement of time) and Vyadhi Avastha (stages of the diseases).[11]

By logical designing of research studies, the BVs of Ayurveda are to be taken care of at the beginning of the trial itself. When put in a homologous group, it helps in avoiding allocation bias as samples have similar BVs. By this process, the interpretation of the data will be unbiased as the “like is compared with like.”[8]

Response variables (RVs) include recording changes in health characters before, during, and after treatment administration. These may include clinical signs elicited by the researcher, symptoms exhibited by the subject, physiological and biochemical test results, or the results obtained in a clinical trial.[8]

The classification of RVs in Ayurveda is as follows:

The level of Dosha, Dhatu, and Mala determines its physiological or pathological state and is classified into Sama (balanced levels), Vriddhi (increased level), or Kshaya (decreased level).

Agni, Kostha, and Aharashakthi are three interrelated components to be assessed together. Teekshna (quick or fast digestive fire), Vishama (irregular state of digestive fire, either fast, slow, or regular), Manda (low digestive fire), and Sama (normal digestive fire) are the three types of Agni.

Mridu (soft bowel movements), Madhyama (regular bowel movements), and Krura (hard bowel movements) are the types of Kostha.[13]

Aharashakthi is assessed based on individuals Abhyavarana Shakti (ingestion capacity) and Jarana Shakti (digestive capacity) and is classified into Pravara (superior), Madhyama (medium), and Avara (inferior).[14]

Vyayamashakthi is indirectly related to Aharashakthi and is based on individual’s capacity (Pravara, Madhyama, and Avara) to perform any physical activities.[15]

Measurement and analysis of RVs of Ayurveda are challenging as no two individuals have the same levels of these variables in their bodies. Hence, analysis of the data obtained in any clinical trial of Ayurveda should consider variations in these RVs of every enrolled subject.

The critical appraisal of the above facts reveals that the statistical basis of classifying these variables is similar to the measurement of central tendencies, viz., mean, median, and mode.[16]

Mean is the average of an entity in a population.[17] The best examples of Ayurveda variables would be Bala, Ahara Shakti, and Vyayama Shakthi, which are classified based on the average capacity of the individual.

Mode is the most repeated entity in a population.[18] The best example is Bhumi Desha, wherein the most similar or commonly occurring features are predominant (in Jangala Desha, Vatadosha is predominant).

Median is a central entity (midpoint) when data are sequential.[19] The best example is the Pravara, Madhyama, and Avara classification of the subjects based on the ubiquity of variables in the population.

Apart from the aforementioned variables, entities such as Vaya (age), Pramana (measurement), Dravya (medicine), and its Matra (dosage) also require proper attention of the researcher during the planning and analysis of the study.[20]

Assessing standard variables such as age, gender, and height requires employing principles of variation such as mean, proportion, range, standard deviation (SD), and standard error (SE).[21] However, most of the variables in Ayurveda are so subjective that interpreting their extent or degree of variations is very difficult. This data analysis process requires a very nuanced understanding of the concepts of RVs mentioned in Ayurveda classical texts.[22],[23] The benefits of doing so are error-free assessments of clinical trial data and understanding the natural effect of any interventions to generalize the results to a larger disadvantaged population.

Error-free and unbiased scientific studies are the key to implementing new therapeutical entities into clinical practice. Suppose the evidence generated by the research works are without properly addressing variables. In such case, the generalization of the research findings will not yield desired results. Therefore, a diligent coherence toward the development of statistical benchmark for the standard variables of Ayurveda in all clinical trials is warranted.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

References

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