A brief introduction of application of new data mining techniques in Ayurveda Research

1. CONFIDENTIAL © 2019 AyurData
Balance
Calmness
Serenity
Introducing
Advanced Statistical Manual
for Ayurveda Research
Kadiroo Jayaraman
Praveen Venugopal
AyurData

2. © 2019 AyurData /CONFIDENTIAL 2
The basic manual
covered the syllabus
specified for M.D.
students on Medical
Statistics, by the Central
Council of Indian
Medicine.
Earlier Release

3. © 2019 AyurData /CONFIDENTIAL 3
In the advanced manual, we
have covered more
advanced statistical
applications including that in
data science.
The mode of presentation is
that the concept is
introduced first, followed by
illustration and the use in a
real context.
Some mathematics will be
involved but well explained
in the text.
Current Release

4. © 2019 AyurData /CONFIDENTIAL 4
TOPICS COVERED
1.Analysis of Repeated Measures
2.Multiple Linear Regression
3.Superiority, Bioequivalence and Non-inferiority trials
4.Logistic Regression
5.Decision Trees
6.Random Forest
7.Support Vector Machines
8.Naïve Bayes Classifier
9.Neural Networks
10.K-Nearest Neighbour Technique
11.Principal Component Analysis
12.Cluster Analysis
13.Stratified Multistage Sampling
14.Analysis of Time Series Data
15.Analysis of Time-to-event Data

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ANALYSIS OF REPEATED MEASURES
Repeated measurements is a common case in Ayurveda experiments.
Repeated measurements on the same individual would be correlated and
so require special analysis.

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ANALYSIS OF REPEATED MEASURES
ANOVA table for two-way with repeated measures for one factor
For instance, Factor A could be treatment and B could be age group provided
the patients have been stratified based on age group. In case age was a taken
as a baseline variable that is continuous, it can be included as a covariate in the
model. The computations and interpretations are well-explained in the manual.

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MULTIPLE LINEAR REGRESSION
The model we are covering is,
which in the matrix form would be,
The model fitting, testing and residual analysis are illustrated using
a real-life dataset.

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Superiority, Bioequivalence and Non-inferiority trials
In many practical situations, we encounter the following types of
comparisons:
• 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.
The hypothesis and test criterion to be employed in each of the above
cases have to be different. For instance,
Superiority hypothesis
H0: ∆ = 0
H1: ∆ ≠ 0, or (∆ > 0, or ∆ <0 for one-tailed tests)

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Superiority, Bioequivalence and Non-inferiority trials
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%).
The tests involved and the interpretation are illustrated using
examples from Ayurveda.

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