New Heart Failure drug showed superior trial efficacy vs the Standard of Care drug. Yet poor effectiveness was observed in real-world with higher mortality than in average jeopardizing the medical impact and the exepected revenues.
Drugs effectiveness is driven by so-called “drivers of effectiveness” including drug related, patient related and healthcare system related factors. Modeling of disease progression in real-world treated patients allows to compare different switching times and treatment sequences strategies to guide medical practice, maximize drugs value and support the design of outcomes-based pricing.
To optimize switching time so to maximize drug effectiveness and de-risk outcomes-based pricing
To identify and characterize drivers of disease progression and new drug’s effectiveness vs standard of care.
To predict the effectiveness in real-world with help of longitudinal dynamic model with time-dependent drivers of effectiveness (DoEs) and prognostic factors.
To simulate real-world outcomes over time from diagnosis onwards: survival and HF/CV related hospitalization.
To bridge effectiveness model based on the evaluation of both exposure- and patients-related drivers of effectiveness.
Methods & solution
Modeling, simulating and comparing treatments sequences to optimize switching strategies
Modeling patient pathway and disease progression from RCT and Real-World data using a Bayesian multi-state model to reflect on the sequence of worsening events over time.
Simulating and comparing various sequences of treatments, switching strategies in various sub-groups.
Early switch maximizes medical value and care efficiency
Early switch was shown to significantly reduced probability of 1st HF hospitalization.
Early switch was also demonstrated to significantly increase overall survival.
Medical value demonstration and economic value generation
- Results were used to design outcomes-based pricing and to guide medical practices.