SHE Consulting

Economic Modelling

Decoding the Value of Healthcare Interventions

In a landscape where resources are finite, but the demand for new and improved health care interventions is high, the efficient allocation of resources is essential. Health economic modelling serves as an important tool for the evaluation of health and cost outcomes associated with healthcare interventions.

Communicating the value of an intervention through robust economic evaluation empowers stakeholders to make well-informed decisions: payers can compare a new treatment with the standard of care and decide whether it is eligible for reimbursement.

SHE employee explaining the data collection process
SHE employees discussing the economic modelling process

At SHE,

We specialize in developing tailored, precise, and flexible health economic models. Our approach is multifaceted, involving several key phases dedicated to ensuring the model aligns with its intended purpose and meets the specific needs of our clients. Depending on the model and available sources, our process includes comprehensive steps to identify important  outcomes, risk factors and model inputs.

Although approaches may vary across models, our general approach to developing a health economic model stems from international guidelines and the conceptual modelling framework by Squires et al. (2016)¹.

1 Squires H, Chilcott J, Akehurst R, Burr J, Kelly MP. A Framework for Developing the Structure of Public Health Economic Models. Value in Health. 2016;19(5):588-601. doi:10.1016/J.JVAL.2016.02.011

Our Model Development Process

Strategic Alignment

We work in close collaboration with stakeholders to establish what needs to be done, which resources are available, and what the timeline is.

Stakeholder Involvement

We ensure that all relevant stakeholders are included, and all angles are covered. This will enhance the relevance of the model later on.

Problem Conception

Defining the problem is essential to solving it. This is done by formulating a clear research question and may involve mapping out the problem and hypothesized causal relationships in a conceptual mode.

Model Development

Our protocol development phase is a key step in our model development. The design phase involves several key steps: 

  1. Reviewing existing health economic models and current guideline documents
  2. Defining the intervention and comparators
  3. Determining the model scope and level of detail
  4. Choosing the most appropriate model framework
Data Collection and Evidence Retrieval

In this phase, model inputs are gathered from a variety of sources, using the following steps.

  1. Identify data sources and parameters
  2. Systematic literature review (targeted or comprehensive)
  3. Use stakeholder input for further collection of evidence
Expressing Structural Uncertainty

The model will be designed to acknowledge and explore possible limitations in evidence and modelling uncertainty. This might include alternative data sources, modification of key assumptions or the inclusion/exclusion of variables. The overall aim is to capture the joint parameter uncertainty and structural uncertainty present in the model and understand the implications for decision-makers.

Validation and Reporting

In this phase, the model is communicated to the shareholders, who are able to give feedback and suggestions for revision. The goal is to ensure that the final model is of the highest standards and meets final client requirements.

Publication

Finally, the model may be published via abstracts and posters for scientific conferences, manuscripts for peer-reviewed journals, or via other channels.

Other Services

Statistical Analysis
Turn your clinical data into actionable insights with our extensive analysis techniques that include survival analysis, missing data analysis and longitudinal analysis
Literature Review
Receive a comprehensive analysis of studies relevant to your project by enlisting our experts for a literature review
Data Collection
Leverage our experience in RWE and RCT data collections to build a robust dataset that will provide you with the answers to your research questions

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