Market Access / HEOR & Pricing

Predicting effectiveness from efficacy for an asthma drug to ascertain medical value towards regulators and payers

Building a predictive real-world disease model of asthma by integrating trial efficacy and drivers of efectiveness to predict the effectiveness of a new anti-asthma drug.
Despite being a known chronic disease, there are still important unmet needs in the treatment of asthma due to high patient heterogeneity and the real-world conditions of care. While different drugs may show similar efficacy in trials, their comparative effectiveness can be widely different in the real-world due to the conditions of care and compliance patterns defining the drivers of effectivenes.


Bridge the efficacy to effectiveness gap, for optimal pragmatic trial design

Identify and characterize drivers of effectiveness.

Project efficacy endpoints to efficacy outcomes.

Optimize negotiations with payers and study design accordingly.

Methods & solution

Building a predictive disease model in real-world of asthma accounting for the drivers of effectivness of drugs in development and from the standard of care

Using a long term cohort studies on asthma patients.

Definition of prescriptions, 3-level GINA control scores and exacerbations adjusted on the basis of hospital admissions.
Drug possession ratios, used as an indicator of compliance.

Identification and quantification of effectiveness factors such as adherence.

Development of a Bayesian dynamic inhomogeneous Markov model at the patient level to jointly describe prescriptions and outcomes over time.


Adherence and related factors significantly improve long-term effectiveness

Association of treatment changes with disease severity while adherence was significantly improved when patients had treatment changes.

The risk of exacerbation depended on the control score and season.

Control was significantly improved by better adherence.


Succesful pragmatic trials and postmarketing studies guided by such real-world simulations

  • Adherence and related factors were shown to be highly interacting with long-term efficacy (eg on relapse rate).

Reference: A Bayesian Dynamic Model of Asthma in the Real Life –