Page 78 - Htain Manual

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in the cancer cases continues to happen as late as 60 or 70 years or even later . And it may
not be feasible to have resources to follow-up a trial cohort for a lifetime. Hence, RCTs may

not offer the medium to generate data for EE.

Fourthly, a trial is generally conducted to evaluate a few alternative options for

treatment or addressing a particular health problem. However, decision making in the field

of policy is full of possible scenarios which need to be evaluated. For example, a single

question of which is the most appropriate method to screen women for cervical cancer can

be further stratified into several scenarios based on which method should be used (pap
smear, visual inspection with acetic acid or HPV DNA), which population should be screened

(30-65 years, 40-65 years, 50-65 years), how frequently (annual, 3 yearly, 5 yearly, 10 yearly,

once in a lifetime). Together these can constitute 16 possible scenarios. However, it may be
difficult to have a single RCT with 16 arms to evaluate all possible scenarios. In view of this

limitation again, RCT alone cannot be used to generate evidence for EE.

Bridging the limitations of RCT: Role for Decision Modelling

A solution to bridge the limitations of RCT is to either undertake decision modelling

alone, or use decision model alongside the evidence generated in RCT. A decision model used
for EE is a biologically plausible sequence of occurrence of health consequences as a result of

the decision of undertaking an intervention. The model so prepared, does not only shows

relationships, but also mathematically quantifies the probability of occurrence of such a
health consequence or outcome as a result of an intervention. In the mathematical

parameterization of a decision model, the researcher can use pragmatic data on effectiveness

from a real-world study rather than a RCT. Alternatively, an assumption which justifies the

constraints of program implementation or treatment administration in real-world could be

incorporated to generate an output which is more acceptable. For example, one may consider
findings of a national evaluation which shows that the coverage of routine immunization is

not likely to be more than 90% in the best possible scenario, and hence the efficacy of

treatment derived from RCT could be modelled on only 90% of the intervention cohort to
generate the health consequences.

Secondly, the evidence from a 1-year trial of anti-hypertensive drug on reduction in

blood pressure could then be used along with evidence from other studies for effect of

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