Statistical Methods in Ayurveda - Presentation part of AyurData Seminar

1. CONFIDENTIAL © 2019 AyurData
Balance
Calmness
Serenity
Statistical Methodology as
applied to Ayurveda Research
Kadiroo Jayaraman
Chief Biostatistics Consultant
AyurData

2. © 2019 AyurData /CONFIDENTIAL 2
PRESENTATION OUTLINE
A brief review of the statistical methodology as applied to Ayurveda
research will be made.
Shall also be making some comments on the current state of affairs
in respect of the following investigation modes.
1. Experimentation
2. Surveys
3. Case-control studies
4. Meta-analysis
5. Survival studies
6. Time series analysis
7. Pharmacological studies
8. Epidemiological studies
9. Ayurgenomics

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SOME GENERAL REMARKS
• After a long period of stagnancy since its original inception, Ayurveda research
has caught up speed in the recent times. The research methodology in general
got modernized both in terms of data capturing methods and the inferential
process.
• Thereby, we are witnessing more and more sophisticated study designs being
employed and more of allopathic parameters being measured in investigations
undertaken in Ayurveda.
• The statistical process as applied to research is part of the broader scientific
approach to knowledge discovery.
• Creativity, objectivity, repeatability, pattern recognition and modelling are the
hallmarks of modern knowledge discovery process.
• Historically, the paradigm shift mainly occurred in terms of availability of big data
and computationally intensive methods although the core principles of data
collection and inference remained the same.

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• Phase I trials seem to be rarely undertaken in Ayurveda as most of the
formulations have an old history.
• Reverse pharmacology is practiced most of the times because of the huge
effort and time involved in the conventional drug discovery approach of
screening thousands of molecules and their biological targets.
• The sample size is kept around 100-200 which can at best serve as a Phase
II trial.
• Large Phase III Randomized Clinical Trials (RCTs) of 300-3000 patients are
not that popular in Ayurveda. One exception found is the one published by
CCRAS by name Drug Development for Select Diseases.
• Post marketing studies (Phase IV) also do not seem to be systematically
enforced. However, pharmacovigilance is practiced by many institutions
although the safety concerns are not very high with Ayurvedic formulations.
EXPERIMENTATION

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EXPERIMENTATION
• One dominant feature that is missing in Ayurveda trials is the use of stratification
required in Ayurveda experiments.
• Ayurveda by its very principles recommends biotype (Prakriti)-specific treatments
which is emerging only now in modern medicine. However, we do not see such
stratification widely practiced in many Ayurveda trials.
• The analysis of experimental data generally proceed through Generalized Linear
Models (GLM) wherein a wide variety of distributions for the dependent are
covered. In the case of ordinal variables, nonparametric methods would be
applicable.
• Bayesian designs like adaptive designs are not in practice either due to deficiency
in expertise or the sample size not being over critical in Ayurveda trials.
• Similarly, less emphasis is given to creation of SDTM/ADaM datasets as there is no
insistence on these standards by the approving authorities. The use of crossover
designs become relevant for reducing the sample size but such designs are rarely
adopted in Ayurveda.

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TYPES OF HYPOTHESES
In the newer set up of experimentation, it is important to recognize the
following types of trials/hypotheses.
The new drug is better than the standard drug.
The new drug is equivalent to the standard drug.
The new drug is at least as good as the standard drug.
Superiority hypothesis
H0: ∆ = 0
H1: ∆ ≠ 0, or (∆ > 0, or ∆ <0 for one-tailed tests)
Bio-equivalence hypothesis
H0: ∆ > ∆E or ∆ < -∆E
H1: -∆E ≤ ∆ ≤ ∆E where ∆E is a clinically relevant equivalence margin
(usually 10%).
Non-inferiority hypothesis
H0: ∆ ≤-∆NI
H1: ∆ >-∆NI where ∆NI is a clinically relevant non-inferiority margin
(usually 10%).

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SURVEYS
• Surveys should ordinarily form a convenient mode of investigation as it can
generate information of value quickly and are applicable to large populations.
• Other than stratification, the basic sampling methods are simple random
sampling, multistage sampling and systematic sampling. On the other hand,
multiphase sampling can effectively be used for measuring multiple characters
some of which are difficult to measure and also for studying time trends.
• Surveys are popular in Ayurveda and stratified multistage sampling is a very
useful option. Stratification improves precision whereas multistage sampling
reduces cost. For a change assessment, at least a two-phase sampling will work
out effective.
• The conventional sampling uses sampling frames which are lists of all sampling
units in the population like list of individuals, households, schools, villages or
other convenient units. In instances where such frames are not possible to be
formed, area frames are utilizable.

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SURVEYS
• Sometimes, choice of a domain (subpopulation based on region or other
attributes) becomes relevant. A suitable choice of a domain coupled with small
area estimation is an efficient way of conducting surveys but the estimation
methods are quite complex and not practical for small scale surveys.
• Survey data can be used to study relationship between different attributes but the
major disadvantage of using such data is the inability to attribute causation for
observed correlations unless a corresponding justification can be worked out
based on technical arguments.
• A host of regression and associated techniques are available to investigate the
relationships between variables and to develop prediction models. They invariably
use a training set and thus are classified as supervised learning techniques.
• In contrast, quite many techniques belong to unsupervised learning which have
more of descriptive value. In this respect, many multivariate analyses like
principal component analysis and clustering become useful.
• .

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CASE CONTROL STUDIES
• By definition, a case-control study is always retrospective because it starts with
an outcome and then traces back to investigate exposures.
• Although controls must be like the cases in many ways, it is possible to over-
match. Over-matching can make it difficult to find enough controls. Also, once a
matching variable has been selected, it is not possible to analyse it as a risk
factor.
• Case control studies help us identify the major exposure factors associated with
occurrence/non-occurrence of a condition. It is possible to calculate the odds
ratios as well through statistical analysis.
• Logistic regression has been identified as the most useful technique to be
adopted in such studies with its variants such as ordinal logistic and multinomial
logistic regression.
• Many more classifiers are available to be used in such situations like decision
tree, random forest, k-nearest neighbour techniques, neural network and support
vector machines.

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LOGISTIC REGRESSION AND DECISION TREE
Predictor Regression
coefficient
SE (Regression
coefficient)
P
value
Odds
ratio
Intercept -3.3848 0.5415 < 0.001 0.03
Age group ( > 60 years) 1.1095 0.4107 0.0069 3.03
Gender (Male) -1.0809 0.5354 0.0435 0.34
Socio-economic status
(Upper)
1.3554 0.4855 0.0052 3.88
Family history (Present) 1.2050 0.7178 0.0932 3.34
Medication (Yes) 1.3617 0.4124 0.0010 3.90
Palpitation (Yes) 2.1603 0.5644 0.0001 8.67
Sleep (Disturbed) 1.2021 0.5941 0.0430 3.32
Tobacco (Yes) 1.8300 0.5791 0.0016 6.23
Bowel suppression (>13.6) 0.8337 0.3992 0.0368 2.30
Tear suppression (>33) 1.4983 0.4195 0.0003 4.47
Decision tree

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META ANALYSIS
• Meta-analysis is a potential means of synthesising already available information
from multiple sources and is worth pursuing from a more practical angle. Meta-
analysis aims to assess the relative effectiveness of several interventions and
synthesize evidence across a network of randomized and/or non-randomized
clinical trials or other relevant sources of information.
• With a network meta-analysis, the relative effectiveness of two treatments can be
estimated even if no studies directly compare them (indirect comparisons).
• Both fixed and random/mixed effects models are employed to analyze the data
from meta-analytical studies. Also, the models work both under frequentist and
Bayesian framework. Bayesian analysis will require specification of priors, i.e.,
information available on the status of parameters of our model.
• Real world evidence (RWE) is also being increasingly used for synthesizing
information from nonclinical sources with information from regular randomized
clinical trials (RCT). Real world evidence (RWE) can include non-randomized
studies, electronic health records, disease registries, and claims data but are not
limited to these.

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META ANALYSIS
Indirect treatment effect
Forest plot showing the ranking among the treatment effects
Network of studies

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• Time-to-event (TTE) data are unique because the outcome of interest is not only
whether or not an event occurred, but also ‘when’ that event occurred. Traditional
regression methods are not equipped to handle censoring when subjects do not
experience the event of interest during the follow-up time.
• Analysis of time-to-event data provides important information on survival time and
hazard ratios. There are three main approaches to analysing TTE data: non-
parametric, semi-parametric and parametric approaches.
• The most common non-parametric approach in the literature is the Kaplan-Meier
(or product limit) estimator.
• As a case of semi-parametric approach, the Cox proportional hazards model is
the most commonly used multivariable approach for analysing survival data in
medical research.
• Accelerated Failure Time (AFT) models are a class of parametric survival models
that can be linearized by taking the natural log of the survival time model.
SURVIVAL STUDIES

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SURVIVAL CURVES BASED ON KM ESTIMATES
Kaplan Meier curve for two treatment groups

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TIME SERIES ANALYSIS
Time series data occur frequently in clinical domain. A time series is a sequence of
observations recorded at a succession of time intervals. It could be an output from
an ECG or EEG, serial recording of pulse rate or recordings of gait or tremor
through digital devises from patients suffering from Parkinson’s disease.
Such data have become more abundant these times with the availability of
wearables like smart watches and other electronic devices.
Methods for time series analysis may be divided into two classes: frequency-
domain methods and time-domain methods. The former includes spectral
analysis and wavelet analysis; the latter includes auto-correlation and cross-
correlation analysis.
Spectral analysis is one class of procedure which has immense potential in
Ayurveda because serial measurements at small intervals like ECG are
abundantly used in Ayurveda clinical trials. Also, diagnosis through pulse is a
fundamental aspect of Ayurveda.
Analysis in the time-domain help us understand the trend, seasonality and cyclical
pattern in a time series.

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SPECTRAL ANALYSIS
Fourier transform Power spectrum

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Ayurveda requires more of pharmacological investigations.
PHARMACOLOGICAL STUDIES
• Pharmacokinetics investigates what the body does to a drug in terms of its
absorption, distribution, metabolism and excretion. Pharmacodynamics is the
study of how a drug affects an organism,
• Statistical moments, exponential modelling and physiologically based kinetic
modelling are used for this purpose. The figure shows the major PK parameters.

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EPIDEMIOLOGICAL STUDIES
• Epidemiological investigations have assumed importance in these days in view
of the pandemic. Mathematical modelling, which can predict disease progress is
an increasingly important option in modern epidemiology.
• One basic model in epidemiology is the SIR (Susceptible-Infectious-Recovered )
model. The outputs from an SIR model is shown below.
• Modern epidemiology utilizes Geographic Information System (GIS) and spatial
modelling.

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AYURGENOMICS
• Genomics has ushered in an era of predictive, preventive and personalized
medicine based on the genetic makeup of each individual. Several approaches are
being attempted in the recent times to identify genetic variations that are
responsible for susceptibility to diseases and differential response to drugs.
• Ayurveda however had documented and practiced personalized approach towards
management of health and disease from a quite long time, based on Prakriti.
Ayurgenomics is an attempt to integrate Ayurveda and genomics.
• Exploratory studies have provided evidence that healthy individuals of contrasting
Prakriti types i.e. Vata, Pitta and Kapha identified on the basis of Ayurveda exhibit
striking differences at the biochemical and genome-wide gene expression level.
These differences were meaningful since these could be linked to genetic markers.
• Genomic studies yield high throughput data of mega size. The data could be at the
genomic, genic or nucleotide level. The generation and analysis of such data
involve the many regular design and analytical principles. The high volume of data
has called for the use of data science concepts as well in the analysis of such data.

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GENE STRUCTURE
Nucleotide sequence of a gene
(C,G,A,T - cytosine, guanine, and adenine, thymine)

21. CONCLUSIONS
Ayurveda research is on the verge of a great stride in terms of its relevance
and methodology.
It is important to adopt modern research tools to make it more effective
and acceptable.
The traditional experimentation, surveys and case-control studies form the
basis of most of the investigations.
Meta-analysis is to be utilized in terms of its great potential to synthesize
information that is widely scattered in the literature.
Analysis of time-to-event data and time series data become relevant in the
context of chronic diseases and improved technological advances.
More of pharmacokinetic, epidemiological and aurgenomic investigations
are to be undertaken to advance our knowledge in these realms.

22. THE TAKEAWAY MESSAGE
Ayurveda needs a revitalization and this can
be achieved through two means:
Better research in Ayurveda
Popularization of Ayurveda
We need dedicated people to accomplish
this.
In short, we need a modern time Charaka to
achieve this!!!

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THANK YOU
© 2019 AyurData /CONFIDENTIAL 23
AyurData Team
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