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Decision Modelling

Demonstrating clinical and economic value of health technologies through user-friendly decision models

What is decision modelling?

Decision models are generalized view of real-world scenarios which enable decision making. In the context of healthcare, decision modelling is the art of  structuring the decision problem, extrapolating short-term outcomes beyond clinical trial periods, synthesizing evidence from multiple studies and capturing decision uncertainty. Decision models help to predict the impact of healthcare interventions on individuals, patient groups, healthcare and society.

 

How do we make a difference?

We employ cutting-edge methods to ensure models to be scientifically robust and user-friendly, and aligned with our client’s product strategy and timelines. We have rigorous validation processes to ensure development of high-quality models. We also provide critical review of external models to ensure that they are of high HTA-submission standards’ before submitting them for healthcare decision-making 

Everything you may need

Decision Model Development
  • Cost-effectiveness models (Global and Country (or HTA) specific)
  • Early decision models
  • Budget impact models (Global and Country-specific)
  • Epidemiological/ disease transmission dynamic models
  • Microsimulation models
  • Public health models
Pre SUbmission REview ("SURE")
  • Clinical validation
  • Model face validation on assumptions and structure
  • Model verification (coding and calculation checks)
  • Report review vis-a-vis model
  • Strategic recommendations
User Interface/ Application Development
  • Web based Applications
  • Excel or R shiny-based Applications or dashboards
  • Mobile and iPad based Applications
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Case Study

Cost-Effectiveness Modelling for Anti-Cancer Treatment: A Case of Immature Overall Survival Data

The Problem

An anticancer drug from a large pharmaceutical company showed promising results in the phase III clinical trial. The drug demonstrated substantial progression-free survival (PFS) gain compared to standard care. However, the overall survival (OS) was immature at the trial data cut and was inconclusive (<10% event). The company wanted to develop a cost-effectiveness model for demonstrating the value of the drug to HTA agencies

The Solution

  • Developed a user-friendly global cost-effectiveness model with the UK as base case in MS Excel and VBA
  • Model structure considered both partitioned-survival and state-transition Markov approach (with model flexibility of choosing between the structure)
  • Followed the NICE-accepted method guidance and sought clinical expert advice throughout the modelling process
  • Conducted statistical (survival and utility data) analysis of patient-level trial data using R and R Knitr
  • Assessed uncertainty around OS extrapolation by generating more than 120 scenarios in the model.
  • Performed full assessment of model uncertainty using techniques such as deterministic sensitivity analysis, probabilistic sensitivity analysis and scenario analysis
  • Validated the model by seeking external clinical expert advice, comparing observed data with model prediction, double programming the model in R and rigorous technical checks

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