Experimental Design AI Prompts

Design a factorial or adaptive experimental design for this study. Research question: {{research_question}} Factors: {{factors}} (each factor and its levels) Adaptive elements needed: {{adaptive_needs}} (interim analysis, arm dropping, response-adaptive randomization) 1. Factorial designs: Full factorial: - All combinations of factor levels are tested - 2^k design: k factors each at 2 levels → 2^k treatment combinations - Advantages: tests main effects AND interactions simultaneously - Sample size: same n needed per cell as a one-factor design, but tests many more questions - Key output: interaction plot — does the effect of factor A depend on the level of factor B? Fractional factorial: - Test only a fraction of all 2^k combinations (e.g. 2^(k-p) design) - Aliasing: main effects are confounded with high-order interactions - Use when: k is large and high-order interactions are assumed negligible - Resolution III: main effects aliased with 2-way interactions (minimum for screening) - Resolution V: main effects and 2-way interactions estimable (preferred for confirmatory) 2. Adaptive designs: Group sequential design: - Pre-planned interim analyses at specified information fractions (e.g. at 50% and 100% of n) - Spending functions control Type I error across looks: - O'Brien-Fleming: strict early stopping, liberal late (good when early stopping is rare) - Pocock: equal thresholds at each look (more liberal early) - Stopping rules: stop for efficacy (p < boundary), futility (conditional power < 20%), or safety Response-adaptive randomization: - Allocation probabilities update based on accumulating outcome data - More participants assigned to the arm showing better performance - Pros: ethical (fewer participants in inferior arm) - Cons: increases bias risk, complicates inference; FDA skepticism in confirmatory trials Platform trials: - Multiple interventions tested simultaneously on a shared control arm - Arms can enter and exit the platform based on interim results - Efficient for rapid testing of many treatments (COVID-19 trials used this) 3. Analysis for adaptive designs: - Naive p-values from adaptive designs are invalid (inflation of Type I error) - Use: conditional power, stagewise p-values (combination function), or Bayesian posterior probabilities - Closed testing principle: preserves familywise error rate when multiple hypotheses are tested Return: factorial design specification (factors, combinations, sample size), interaction test plan, adaptive design choice with stopping boundaries, and analysis approach.

Design an observational study and plan the appropriate analysis to control for confounding. Exposure of interest: {{exposure}} Outcome of interest: {{outcome}} Available data: {{data_description}} Study type: {{study_type}} (cross-sectional, case-control, cohort) 1. Study design selection: Cross-sectional: - Exposure and outcome measured at the same time - Pros: fast, cheap, good for prevalence estimation - Cons: cannot establish temporality; prone to reverse causation - Best for: estimating associations and generating hypotheses Case-control: - Sample based on outcome (cases vs controls), then measure past exposure - Pros: efficient for rare outcomes - Cons: recall bias; selection of controls is critical - Analysis: conditional or unconditional logistic regression; effect measure = odds ratio Prospective cohort: - Sample based on exposure status, follow forward to measure outcomes - Pros: can measure incidence, multiple outcomes, avoids recall bias - Cons: expensive, slow; loss to follow-up threatens validity - Analysis: survival analysis (Cox model), incidence rate ratio; effect measure = hazard ratio, RR Retrospective cohort: - Historical data used to construct a cohort; follow forward in time using existing records - Faster than prospective; subject to data quality of historical records 2. Confounding control methods: Design-stage: - Restriction: limit study to a homogeneous subgroup (removes confounding from that variable) - Matching: match cases and controls (or exposed and unexposed) on potential confounders - Advantage: guaranteed balance; disadvantage: cannot study matched variables as exposures Analysis-stage: - Multivariable regression: include confounders as covariates - Propensity score methods (see propensity score prompt) - Stratification: estimate effect within strata of the confounder, then pool with Mantel-Haenszel 3. Bias assessment: - Selection bias: is the study sample representative of the target population? - Information bias: are exposure and outcome measured accurately? - Confounding: have all major confounders been measured and controlled? - Use a directed acyclic graph (DAG) to identify the minimal sufficient adjustment set 4. Directed Acyclic Graph (DAG): - Draw the causal diagram: nodes = variables, arrows = direct causal effects - Identify confounders: common causes of exposure and outcome - Identify colliders: common effects of two variables (do NOT adjust for colliders — this opens a non-causal path) - Use the backdoor criterion to identify the adjustment set Return: study design recommendation, confounding control plan, DAG specification, and bias assessment.

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.