Diagnosis before method
We begin by clarifying the decision, target population, treatment effect of interest and available evidence. We then assess feasibility and assumptions before recommending methods that are scientifically credible, transparent and fit for purpose.
The evidence gap
Regulatory trials are designed to demonstrate efficacy, not always to answer the comparative questions that matter for HTA decisions. Differences in population, comparator, endpoint and follow-up create evidence gaps that must be understood before they can be addressed.
| Study dimension | Ideal study for the decision | Example gap in actual study |
|---|---|---|
| Population | Target decision population | Differences in effect modifiers |
| Treatment conditions | Aligned comparator | Missing or misaligned comparator |
| Endpoint | Patient-relevant outcome | Surrogate or not patient-relevant |
| Intercurrent events | Applicable handling | Subsequent therapy not applicable |
| Summary measure | Mature, estimable | Immature survival |
Filling the evidence gap
New treatments are rarely studied in the exact populations and conditions that matter for decision-making. We bridge that gap using the right combination of external evidence and analytical methods, establishing defensible linkages between the sources.
Research framework
- Define target populationEstablish who the analysis is designed to inform
- Define the causal estimandClarify what treatment effect is being estimated
- Define the target trialSpecify the ideal study design for the question
- Identify or collect evidenceSystematic search across trial and real-world data
- Assess feasibilityEvaluate identifiability given assumptions and available adjustments
- Validate clinical assumptions & methodsEnsure the analytical approach is clinically defensible
- Define statistical estimandAlign the statistical target with the causal question
- Perform analysesExecute with appropriate methods and sensitivity analyses
- Communicate resultsClear, credible outputs ready for submission and review
Analytical approaches
Disease-level analyses define baseline risk and outcome relationships, study-level analyses improve internal validity or relevance and extend trial insights, and cross-study analyses enable decision-relevant comparative effectiveness.
Disease-level
- Natural history analysis
- Prognostic factor synthesis
- Biomarker–outcome relationships
Study-level
- Subgroup analyses
- Crossover adjustment
- Extrapolation
- Population adjustment (transportability)
- Individual-level surrogacy
Cross-study-level
- Indirect treatment comparisons (NMA, MAIC, STC)
- Population-adjusted comparisons (transportability across trials)
- External control & target trial emulation
- Trial-level surrogacy & validation
Ready to solve your evidence challenge?
Tell us about your evidence question. Based in Canada, working globally.