Randomized Controlled Trial Design AI Prompt

Design a randomized controlled trial (RCT) to answer this research question.

Research question: {{research_question}}
Intervention: {{intervention}}
Primary outcome: {{primary_outcome}}
Population: {{population}}
Practical constraints: {{constraints}} (budget, timeline, ethical restrictions)

1. Randomization design:

   Simple randomization:
   - Each participant independently assigned with probability p = 0.5
   - Works well for large n (> 200); may produce imbalanced groups in small trials

   Block randomization:
   - Participants randomized in blocks of fixed size (e.g. blocks of 4 or 6)
   - Guarantees approximately equal group sizes throughout the trial
   - Use when enrollment is sequential and interim analyses are planned

   Stratified randomization:
   - Randomize separately within strata of key prognostic variables (age group, site, disease severity)
   - Prevents chance imbalance on important covariates
   - Combine with block randomization within strata

   Cluster randomization:
   - Randomize groups (clinics, schools, communities) rather than individuals
   - Use when individual randomization causes contamination
   - Requires larger sample size (inflate by design effect = 1 + (m-1) x ICC)

2. Blinding:
   - Open-label: neither participants nor assessors are blinded (highest risk of bias)
   - Single-blind: participants are blinded to treatment assignment
   - Double-blind: both participants and outcome assessors are blinded (gold standard for efficacy)
   - Triple-blind: includes the data analysts
   - Is blinding feasible for this intervention? If not: use blinded outcome assessment at minimum

3. Sample size and allocation:
   - Calculate required n based on primary outcome (see power analysis prompt)
   - Equal allocation (50/50) is most efficient when costs per participant are equal
   - Unequal allocation: use if one arm is more costly or to expose fewer to the control

4. Analysis plan (pre-specified):
   - Primary analysis: intention-to-treat (ITT) — analyze participants as randomized, regardless of adherence
   - Per-protocol analysis: sensitivity analysis for those who completed the protocol
   - Handling missing data: specify imputation method in advance
   - Pre-register the primary outcome and analysis plan (ClinicalTrials.gov, OSF)

5. Validity threats:
   - Selection bias: only randomization fully controls this
   - Attrition: track dropout rate by arm; > 20% differential dropout threatens validity
   - Contamination: control group receives elements of the intervention
   - CONSORT checklist: use for reporting

Return: randomization design recommendation, blinding plan, sample size, ITT analysis plan, and validity threat assessment.