Health Economics & Outcomes Research

Solving complex evidence gaps
for regulatory and HTA decisions

Credible, decision-relevant comparative effectiveness analyses for regulatory submissions, HTA evaluations, and Joint Clinical Assessments.

Our Services Get in Touch
How We Help

Supporting your evidence needs

From analytical advice to full execution — we provide expert support at every stage of the evidence generation and synthesis process.

🔍

Evidence Identification & Feasibility

Structure the evidence base aligned to your research question from the outset.

  • Identify and structure evidence
  • Assess feasibility upfront
  • Define key assumptions
  • AI-assisted systematic search
📈

Analytical Advice & Execution

Tailored analytical support given your data constraints and decision context.

  • Advise on appropriate approaches
  • Execute ITC and external control analyses
  • Crossover adjustment
  • Surrogate analyses
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Regulatory & HTA Support

Analyses and documentation aligned with submission requirements across agencies.

  • NICE, JCA, and global HTA submissions
  • Technical reports and responses
  • Regulatory-aligned methods
  • Reviewer question support
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Strategic Methods & Training

Input on study design, estimands, and evidence strategy — plus team training.

  • Estimand and trial design input
  • Evidence strategy development
  • Training on comparative effectiveness methods
  • Best practices workshops
The challenge

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.

Biased or non-credible treatment effect estimates
→
Inability to claim comparative benefit
→
Regulatory or HTA rejection risk
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
Our Approach

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 defendable linkages between the sources.

Filling the gap
Comprehensive, decision-relevant evidence
Robust methods for unbiased estimates
Transparent, defensible assumptions
Research Framework
1

Define target population

Establish who the analysis is designed to inform

2

Define the causal estimand

Clarify what treatment effect is being estimated

3

Define the target trial

Specify the ideal study design for the question

4

Identify or collect evidence

Systematic search across trial and real-world data

5

Assess feasibility

Evaluate identifiability given assumptions and available adjustments

6

Validate clinical assumptions & methods

Ensure analytical approach is clinically defensible

7

Define statistical estimand

Align statistical target with causal question

8

Perform analyses

Execute with appropriate methods and sensitivity analyses

9

Communicate results

Clear, credible outputs ready for submission and review

Analytical approaches

Disease-level analyses define baseline risk and outcome relationships, within-trial analyses improves internal validity or relevance and extend trial insights, and cross-trial 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
Thought Leadership

Publications & research contributions

Peer-reviewed publications spanning methods development and applied analyses across network meta-analysis, population adjustment, survival modelling, and HTA.

Google Scholar Profile
48
Network meta-analysis & meta-regression publications
24
Indirect population-adjusted comparison publications
13
Advanced survival & endpoint modelling publications
8
Economic modelling for HTA publications

Recent ISPOR Presentations

Case Studies

Applied HTA experience

Selected examples of analyses that have shaped HTA decisions and advanced the methods field — from regulatory submissions to guideline-endorsed methodological frameworks.

Recommended
Cancer Drugs Fund → Routinely Recommended
NICE Single Technology Appraisal · TA592

Cemiplimab

Metastatic / locally advanced cutaneous squamous cell carcinoma
Regeneron / Sanofi able to demonstrate comparative effectiveness and value of cemiplimab versus existing options
  • Systematic literature review
  • External control: MAIC and STC methods
  • Cost-effectiveness model: partitioned survival and mapped utilities
  • Survival extrapolation: standard parametric and fractional polynomials
  • Expert elicitation SHELF methods for long term survival
  • Technical report and responses to questions supporting submission
Konidaris et al. 2021 — Value of Cemiplimab · Value in Health
Keeping et al. 2021 — Comparative efficacy of cemiplimab · Future Oncology
View TA592 on NICE →
KEY STUDIES RECOGNIZED — NICE DSU 26 (2025)
Also cited in NICE DSU 21: Flexible methods for survival analysis
Expert Elicitation · Guideline Endorsed

Structured expert elicitation

Long-term survival extrapolation for HTA in oncology
Enables defensible extrapolation where trial follow-up is immature, supporting HTA decision-making under uncertainty
  • SHELF-based elicitation across CAR-T case studies
  • 6–9 clinical experts per study
  • Structured individual elicitation with consensus calibration
  • Behavioral aggregation using Rational Impartial Observer (RIO)
  • Formal quantitative integration into survival models
Cope et al. 2019 — CAR-T in ALL · BMC Med Res Methodol
Ayers & Cope et al. 2022 — CAR-T in RRMM · BMC Med Res Methodol
View NICE DSU 26 Report →
About

Shannon Cope

Founder, Unpuzzle HEOR · Vancouver, Canada

LinkedIn Profile
Shannon Cope

Shannon Cope is a leader in evidence generation and synthesis for the comparative effectiveness of new treatments required for regulatory and health technology assessments.

She specializes in advanced comparative effectiveness methods, including network meta-analysis, population-adjusted and external control analyses, and trial- and patient-level surrogacy. Her work focuses on complex evidence settings, particularly in oncology, where indirect comparisons often require data integration for time-to-event outcomes.

She has led analyses supporting regulatory submissions, HTA evaluations, and Joint Clinical Assessments in Europe, and has contributed to numerous peer-reviewed publications on methods development and application.

Her recent work includes the application of AI to evidence synthesis workflows.

  • MSc Health Administration, University of Toronto
  • BSc Health Sciences, McMaster University
Get In Touch

Ready to solve your evidence challenge?

Whether you need analytical advice, execution support, or strategic methods input — we'd love to hear from you.

shannon.cope@unpuzzleheor.ca
Based in Vancouver, Canada · Available globally
Send a Message