What We Do

We work with clients and academic experts to problem-solve complex questions around drug value and access.

Our work spans a wide range of therapeutic areas including:

  • Rare disease
  • Gene therapy
  • Oncology
  • Cardiovascular
  • Neurology
  • Neuromuscular
  • Dermatology
  • Infectious disease
  • Respiratory

Our Capabilities

With data and evidence at the heart of our work, we combine sophisticated algorithms with intuitive design to:

Health Economics

Leveraging clinical, epidemiological and economic data to create math-based intuitive models

Health Economics

Our approach to developing HEOR models begins by collaborating with clients to design and implement the evidence generation strategy to fill the gaps needed for a customized model.

Our work includes

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Pharmacoeconomic and budget impact models for HTA submission (NICE, SMC, CADTH) and peer-review publication

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Adaptation of global/core models for local submissions

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Risk-benefit and multi-criteria decision models to identify and characterize intended and unintended medication effects

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Burden of illness models that synthesize multiple data sources

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Early-stage models to guide Phase I/II product priorities

Evidence Synthesis

Assembling relevant pieces of information from multiple sources to address important research questions

We conduct rigorous systematic literature reviews to inform quantitative or network meta-analysis, as well as more descriptive reviews to bring the proof of scientific literature to research areas.

Of course, we can also provide targeted literature reviews where a more efficient strategy addresses a client’s needs.

Reviews are conducted and reported in accordance with recommended guidelines (i.e., Cochrane Collaborative, PRISMA).

Following the review process, our team designs and implements state-of-the-art evidence syntheses, including indirect treatment comparisons.

We look at complex networks, such as those informed by:

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Single-arm trials

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Observational studies

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Individual patient data for a subset of comparators

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Systematic reviews of burden of illness data, or natural history evidence to inform economic models for publication

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Bringing a trained eye to look at the clinical landscape and burden of disease for a drug

Observational studies

We design and conduct observational studies across a range of epidemiological territories, including estimating treatment effectiveness or safety, characterizing treatment patterns or the burden of disease, and providing parameter inputs for economic models.

Our competencies include standard and specialized methods of study design, and of course, consideration of the most appropriate ways to account for bias.

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Retrospective chart review studies

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Analysis of existing databases with secure processes for data storage and management

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Quantitative/qualitative surveys of patients, healthcare practitioners and/or general public

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Prospective observational studies of clinic- or community-based samples to collect select epidemiological, economic and clinician- and patient-reported outcomes (ClinROs and PROs)

We have extensive knowledge of and access to real world data and evidence.

All observational studies include a study design component where we translate a client’s objectives into a feasible study.

We also offer “stand-alone” study design and feasibility including:

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Contributing guidance to large multinational studies including disease registries

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Contributing guidance to large multinational studies including disease registries

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Study protocol to provide comprehensive assessments of timelines, budget, strengths and limitations before a study is initiated

Data Visualization

Data Visualization

Translating complex information into simple interactive dashboards that engage users and communicate clear messages

We design strategic visual explanations of sophisticated data that tell a deeper story than possible with one-dimensional number tables, or sets of number-based comparisons.

Every solution is customized

Our goal is to allow users to understand multi-layered concepts and findings at a glance.

Our data visualization work is a way to communicate the evidence we generate – not just an output of that evidence. Every project is different, so every solution is customized.
We don’t rely on a proprietary or off-the-shelf tool.

Many of our visualizations are predictive models that enable users to explore different scenarios by changing 1 or more variables to illustrate the impact those changes have on other factors.
These include:

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Value and cost of HCP or drug interventions over time

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Budget or pricing impact of drugs across populations and geographies

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Financial consequences of adopting a new therapy

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Impact of variable factors such as drug adherence against disease trajectories

How can we help you?