Clinical Research Designs Suitable for AYURVEDA

Dr. Sreejith Sreekumar, BAMS,PGDCR(Canada)

Chief Executive Officer

Email: drsreejith@clinfound.com; URL: www.clinound.com

Mob: +91-9995342978

“Tadeva yuktaṃ bhaiṣajyam yadārogyāya kalpate” 

 "Who (if anyone) benefits?" v/s

"Does the overall group benefit?"

Major Questions in Designing for Studies in Ayurveda

Which aspect of the science is being studied? Can it be studied following the patterns of modern

science protocols? Does already exist a conventional treatment safe

and effective? Is ethically correct to study that type of remedy?

DESIGN PONDERINGS….

Basis

Diagnosis/Prognosis/Patient-Outcome

Treatment Principles Preventive/

Epidemiological/ Community based

Methods Observational Quasi experimental Experimental

What makes them complex Number of and interactions between components

within the experimental and control Selection of Control/Placebo Number and difficulty of behaviours required by

those delivering or receiving the intervention(Yogya) Number of groups or organisational levels targeted

by the intervention(Possible clustering based on factors like doṣa,prakruti)

Number and variability of outcomes(Samyak lakshanas)

Degree of flexibility or tailoring of the intervention permitted

Some Guiding Lights....

WHO Guidelines for Research in Traditional medicine Developing and evaluating complex interventions:

new guidance-MRC(UK)

Single Case Research Designs

Observational Quasi experimental Experimental

“Puruṣoayam lok sammitah”

Single-case design/ N of 1 trials

Randomized controlled trials with only one participant are called "n-of-one trials" or "individual patient trials.“

These are within-patient randomized, double-blind, crossover trials, in which patients act as their own controls, and provide the most rigorous information available for any individual patient

 In the n-of-1 trial, the unit of randomization is the treatment sequence for an individual patient, and a single n-of-1 treatment cycle includes an exposure to each therapy

 The question answered by an n-of-1 trial is ‘which therapy is better for this patient?’

The randomised n-of-1 trial is now considered Level 1 evidence for treatment decision purposes by the Oxford Centre for Evidence-Based Medicine

Single-case research designs have a long and distinguished history in psychological science.

N = 1 Trials

Trials can be undertaken with just one participant. If the condition is a chronic relapsing problem the unit of randomisation can be the ‘episode’ of treatment.

For example, n = 1 trials have been used among people with rheumatoid arthritis to ‘try out’ different pain relieving drugs.

Its not fail safe…. Not all single-case designs have high methodological

quality.  We have to identify experimental versus non-

experimental single-case designs Experimental designs are those which use

variants of the withdrawal/reversal design (e.g. ABA), multiple baseline designs, alternating treatment designs and changing criterion designs.

Non-experimental designs include those that use a single phase or are bi-phasic only (eg. A-B), contain pre/post data only and case descriptions.

In Ayurveda

Single-case designs have the advantage of being adaptable to the clinical needs of the patient and the therapeutic approach of the practitioner, but have limitations due to their lack of generalization to other patients.

Such designs are appropriate for the development of research hypotheses, testing those hypotheses in daily clinical practice and refining clinical techniques.

Single-case designs using a common protocol-if the protocol can be systematically followed-should be advocated for collaborative research among practitioners from different backgrounds.

For example, single-case designs can evaluate the effectiveness of various specialized treatment methods in patients with a variety of individual differences.

In a single-case design, the patient is his or her own control. Treatment can be randomized for a patient, rather than the patient being

randomized for a treatment.

Uses

Evaluate the impact of interventions of proven effectiveness on individuals.

Understanding heterogeneity: Testing theory: Causal determinants of change Informing trial design: most likely to respond, estimate sample

size, optimal dose, effect time of a drug

Types of N of 1 designs

Reversal Designs (ABAB designs) Multiple-Baseline Design Cross-over designs Balanced designs Block designs

Black Box Design The study of traditional medicine can also be undertaken in a

“black-box” manner. This means that the treatment and all of its components are

delivered as they would be in the usual clinical situation. In this type of study, no component of the treatment “package” is

isolated and studied independently. This allows the effectiveness of traditional medicine to be

determined either within its own theoretical framework or within that of conventional medicine.

Conclusions & InterpretationSubject Baseline

Treatment

Opening the Black Box

What is happening? Is it what is expected or desired? Why is it happening as it is? What are the program’s key components? What does each component do? To what extent is the program amenable to implementation

elsewhere?

Pragmatic studies

The primary question of interest relates to intervention effectiveness: whether an intervention works under real-life conditions. 

Explanatory trials are designed to find out whether a treatment has any efficacy, almost always compared with placebo under ideal conditions.

PT answers questions about the overall effectiveness of an intervention, and cannot study the contributions of its different components.

The main advantage of PT is that they can deliver evidence of effectiveness directly in clinical practice

Limitations of PT The lack of placebo and blindness Increased costs The need of several therapists More complexity and lack of clarification about the

mechanism of action PT should be seen not as an alternative to explanatory

studies, but as a mandatory complement that define and improve evidence primarily coming from explanatory trials, the only one that can reliably confirm efficacy.

Pilot Studies

A trial study carried out before a research design is finalized to assist in defining the research question or to test the feasibility, reliability and validity of the proposed study design

The results of the pilot are used to improve the program or evaluation procedure being piloted before it is used on a larger scale.

Proof-of-Concept Studies & Adaptive Designs A proof-of-concept (PoC) study is defined as a clinical trial

carried out to determine if a treatment (drug) is biologically active or inactive 

PoC studies usually use surrogate markers as endpoints. In general, they are phase I/II studies - which,investigate the safety profile, dose level and response to new drugs.

An adaptive trial design refers to a design that allows modifications to be made to a trial's design or statistical procedures during its conduct, with the purpose of efficiently identifying clinical benefits/risks of new drugs or to increase the probability of success of clinical development

EQUIVALENCE TRIALS In conventional superiority drug trial design, the null hypothesis

(H0) states that both the treatments have no difference, whereas the alternate hypothesis (H1) states that they are not equal.

An equivalence trial is designed to confirm the absence of a meaningful difference between treatments.

Though the absolute equivalence can never be demonstrated, it is possible to assert that the true difference is unlikely to be outside the “equivalence” range.

In this case, it is more informative to conduct the analysis by computing the CI of the difference between the two treatments although there are closely related methods, using significance test procedures.

A margin of clinical equivalence is chosen by defining the largest difference that is clinically acceptable, so that a difference bigger than this would matter in practice.

Sequential trials

A sequential trial is a study with parallel design in which the number of participants is not specified by the investigators beforehand.

Instead, the investigators continue recruiting participants until a clear benefit of one of the interventions is observed or until they become convinced that there are no important differences between the interventions.

This element applies to the comparison of some diagnostic interventions and some procedures in interventional radiology.

Strict rules govern when trials can be stopped on the basis of cumulative results, and important statistical considerations come into play.

Patient Preference Trial

Patients often have a preference for treatment. Patients with strong preferences for ‘usual care’ often do not get

into a trial because randomisation does not guarantee that they will get what they want.

One approach to the issue of preferences is to undertake a ‘patient preference trial’.

Only patients ‘indifferent’ to which treatment they receive are randomised.

Trial also known as ‘Brewin-Bradley or Comprehensive Cohort Design’.

Patient Flow in Preference Trial

A sse ssO u tco m es

R e ce ive A

P re fe r A

A sse ssO u tco m es

R e ce ive A

A sse ssO u tco m es

R e ce ive B

In d iffe re n tR a nd o m ised

A sse ssO u tco m es

R e ce ive B

P re fe r B

Preference Example

A trial of two methods of abortion – medical termination (mifepristone) vs surgical aspiration.

Some women had strong preferences and therefore were allowed their treatment choice.

Abortion Trial

0102030405060708090

100

Prefer Med Prefer Vac Medical Vacuum

Heshaw BMJ 1993;307:714-7.

Backpain Preference Trial

F o llo w -up-3 .10

P re fe r E xe rc iseG o t E xe rc ise

F o llo w -up-1 .93

P re fe r E xe rc iseG o t G P C a re

F o llo w -up-3 .15

In d iffe re n tG o t E xe rc ise

F o llo w -up-1 .18

In d iffe re n tG o t G P C a re

Klaber Moffett et al. BMJ 1999;319:279-83.

Observational design

Observational studies collect findings on a therapeutic or prophylactic treatment under routine conditions.

The special feature of these studies is that they seek, as far as possible, not to influence the individual doctor-patient relationship with respect to indications, and the selection of and carrying out the treatment.

These studies may be conducted with or without a control group. The specific details of the study (e.g. the time and extent of examination for each

individual patient, the number of patients involved) and the envisaged methods (e.g. data recording and evaluation) must be adapted to the question investigated in the study (e.g. safety or appropriate posology).

Observational studies have specific advantages in studying aspects of clinical safety.

The use of such studies to prove efficacy is limited because bias in patient selection may occur.

Nevertheless, the level of evidence on efficacy of traditional medicine can be significantly increased by well-designed observational studies.

STEP WEDGE DESIGNS

One individual/cluster receives the intervention in each time period

Order of intervention determined at random All individuals/clusters get the intervention

by the end of the process Data collected in each time period Eventually, the whole population receives

the intervention, but with randomisation built into the phasing of implementation.

If CRT, then individuals at each time can be same (cohort) or different (cross-sectional)

5

4

3

2

11 2 3 4 5 6

Shaded cells represent intervention periodsBlank cells represent control periodsEach cell represents a data collection point

Time periods

Parti

cipan

ts/C

lust

ers

ADVANTAGES Efficiency: Units act as their own control, so fewer units needed (same as cross-

over design) Logistical or financial - cannot introduce the intervention in all units at once Evaluate the community effectiveness of an intervention previously shown to be

efficacious in an individually randomized trial or in a different setting; systematically evaluate new program

To study the effect of time on intervention effectiveness (i.e.seasonality, time since introduction)

Introduction of HBV vaccination in infants in The Gambia (The Gambia Hepatitis Study Group, 1987)

• Cluster randomized (Health districts)

• 18 health districts, but program could not be Implemented in all districts at the same time

• Immediate outcome: HBV antibody titre

• Long-term outcome: Hepatocellular cancer and other Liver disease

Wait-List Design Variant of Stepped Wedge A stepped-wedge wait-list design is a version of the

cluster based stepped-wedge approach described above in which individuals (rather than clusters like clinics or community centers) are randomized from wait list to intervention over a series of time stages.

Adapted to contexts in which there is a large registry of patients who are eligible for participation in an intervention (such as disease-specific registries or health plan membership rosters)

When an RCT is not possible When it is not feasible to assign all patients to

receive the intervention at the same time.

Natural experiments

Natural experiments are observational studies which can be undertaken to assess the outcomes and impacts of policy interventions.

Unlike ‘experiments’ such as randomised controlled trials (RCTS), or quasi‐experimental design studies,  researchers do not have the ability to assign participants to ‘treatment’ and ‘control’ groups. 

Rather, divergences in law, policy or practice can offer the opportunity to analyse populations like they had been part of an experiment

NIYATHATHANKA PARYAYA HETU & ANIYATHATANKA PARYAYA HETU in Janapadodhwamsa?Achara Rasayana becoming a social policy

Natural Experiments

Advantages Pragmatic, cost‐effective research

design if data are already be available for analysis in national data sources.  

Provide an opportunity to answer research questions that it may not be possible to address in any other way (particularly given the ethical and practical constraints of ‘randomisation’).

May identify effective interventions to improve social, educational and health equalities and provide a useful tool for policy evaluation

Disadvantages It is difficult to draw clear casual

inferences. The process by which subjects are

assigned to the “treatment” or “control” groups is rarely truly random and many extraneous factors may influence the selection.

There may be baseline differences in health, social or other prognostic factors between the two groups

The” boundaries” of geographical and social populations may be difficult to define and may change and overlap in unpredictable way

Examples

Example 1: The effects of daylight saving on road traffic accidents in the United States

Example 2: The effectiveness of needle‐exchange programmes for prevention of HIV infection

Ethnographic design

Ethnographic research is one of the most in-depth research methods possible

An ethnographer sees what people are doing as well as what they say they are doing

It provides researchers with rich insights into the human, social and cultural aspects of organizations

Ethnographic design

Ethnographic studies document the social and cultural context in which a traditional practice emanates may be appropriate in situations where there is no available scientific literature or other documentation.

These and other qualitative studies can provide baseline information from which hypotheses may be generated, and can lead to further research.

Diagnostic Research Design

In the era of evidence-based medicine, diagnostic procedures also need to undergo critical evaluations.

Mathematical models to estimate the true (added) diagnostic accuracy of a test

Practitioners’ judgment to assess diagnostic accuracy

The value of test results in terms of patient outcome

Methods

Appropriateness Reliability Validity Responsiveness Precision Interpretability Acceptability Feasibility

Ayurveda Diagnostics

Diagnostic Criteria for different diseases Prakruti Nadi Pareeksha Moothra Pareeksha Thaila bindu pareekasha …… and many more

Conclusion The current scientific evidence on Ayurvedic therapies, and

Ayurveda as a whole system of medicine, often does not reflect the indigenous theories of Ayurveda and utilizes primarily western-conventional oriented outcome measures of benefit.

Ongoing challenges associated with Ayurvedic research include:

- methodological issues,

- limited funding

- a lack of multidisciplinary and international research initiatives

- a heavy bias in favour of preclinical research and single drug interventions,

- a lack of focus on real life clinical situations.

Thank you