Strategic clinical trial design feasibility assessment. Analyzes 6 dimensions (endpoint, population, comparator, effect size, duration, regulatory pathway) using precedent trials and FDA guidance. Produces enrollment projections, endpoint recommendations, and approval-pathway analysis. Use for trial-protocol design, power/sample-size estimation, comparator selection, and FDA submission strategy. Driven by precedent-based reasoning rather than first-principles math.
Strategic clinical trial design feasibility assessment. Analyzes 6 dimensions (endpoint, population, comparator, effect size, duration, regulatory pathway) using precedent trials and FDA guidance. Produces enrollment projections, endpoint recommendations, and approval-pathway analysis. Use for trial-protocol design, power/sample-size estimation, comparator selection, and FDA submission strategy. Driven by precedent-based reasoning rather than first-principles math.
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Clinical Trial Design Feasibility Assessment
Systematically assess clinical trial feasibility by analyzing 6 research dimensions. Produces comprehensive feasibility reports with quantitative enrollment projections, endpoint recommendations, and regulatory pathway analysis.
IMPORTANT: Always use English terms in tool calls (drug names, disease names, biomarker names), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language.
Reasoning Before Searching
Trial design starts with the question, not the methods. Answer these four questions before running any tools — they determine everything else:
What is the primary endpoint? Is it overall survival (gold standard but slow), PFS (faster but surrogate), ORR (single-arm friendly but not always accepted), or a biomarker (needs validation as surrogate first)? The endpoint determines FDA pathway, statistical design, and duration.
Who is the population? Broad unselected vs. biomarker-enriched. Enriched populations have higher response rates, allowing smaller trials — but require a validated companion diagnostic and reduce the eligible patient pool.
Placebo (only if no standard of care exists), active control (requires non-inferiority or superiority framing), or single-arm with historical control (acceptable for rare diseases or breakthrough designations, but FDA scrutiny is high).
What is the comparator?
Is the effect size realistic given the mechanism? A 20% improvement in ORR over SOC requires ~100 patients per arm. A 50% improvement requires ~30. If the mechanism only justifies a 10% improvement, the trial may be underpowered regardless of design. Check precedent effect sizes in similar trials before committing to an endpoint.
These four answers determine sample size, duration, and trial design. Look them up from precedent trials and FDA guidance — do not derive them from first principles.
LOOK UP DON'T GUESS: Never assume what the standard of care is for an indication — look it up with DrugBank and FDA tools. Never assume an endpoint is FDA-accepted — verify with search_clinical_trials precedents and OpenFDA_get_approval_history. Never estimate prevalence from memory — use OpenTargets, gnomAD, or COSMIC.
Core Principles
1. Report-First Approach (MANDATORY)
DO NOT show tool outputs to user. Instead:
Create [INDICATION]_trial_feasibility_report.md FIRST
Initialize with all section headers
Progressively update as data arrives
Present only the final report
2. Evidence Grading System
Grade
Symbol
Criteria
Examples
A
3-star
Regulatory acceptance, multiple precedents
FDA-approved endpoint in same indication
B
2-star
Clinical validation, single precedent
Phase 3 trial in related indication
C
1-star
Preclinical or exploratory
Phase 1 use, biomarker validation ongoing
D
0-star
Proposed, no validation
Novel endpoint, no precedent
3. Feasibility Score (0-100)
Weighted composite score:
Patient Availability (30%): Population size x biomarker prevalence x geography
