clinical-trial-design-patterns

# Clinical Trial Design Patterns ## When to Use This Skill - Selecting appropriate trial design for clinical objectives - Implementing multi-arm or multi-endpoint trials - Designing stratified trials - Planning adaptive designs - Understanding design trade-offs ## Two-Arm Parallel Design ### Standard Design The most common design: randomize patients to treatment or control. ```r # simtrial implementation sim_pw_surv( n = 400, block = c(rep("control", 1), rep("experimental", 1)), # 1:1 enroll_rate = data.frame(rate = 20, duration = 12), fail_rate = fail_rate ) ``` ```r # Mediana implementation DataModel() + OutcomeDist(outcome.dist = "NormalDist") + SampleSize(200) + # Per arm Sample(id = "Control", outcome.par = parameters(mean = 0, sd = 1)) + Sample(id = "Treatment", outcome.par = parameters(mean = 0.5, sd = 1)) ``` ### Unequal Randomization **When to Use:** - Increase exposure to experimental treatment - Ethical considerations - Resource optimization ```r # 2:1 randomization (experimental:control) sim_pw_surv( n = 300, block = c("control", rep("experimental", 2)) ) # Mediana with unequal allocation DataModel() + Sample(id = "Control", sample.size = 100, ...) + Sample(id = "Treatment", sample.size = 200, ...) ``` **Trade-off:** Unequal allocation reduces power for same total N. ## Multi-Arm Designs ### Dose-Finding (Multiple Doses vs Placebo) ```r # Three doses + placebo DataModel() + OutcomeDist(outcome.dist = "NormalDist") + SampleSize(75) + # Per arm Sample(id = "Placebo", outcome.par = parameters(mean = 0, sd = 1)) + Sample(id = "Low Dose", outcome.par = parameters(mean = 0.3, sd = 1)) + Sample(id = "Mid Dose", outcome.par = parameters(mean = 0.5, sd = 1)) + Sample(id = "High Dose", outcome.par = parameters(mean = 0.7, sd = 1)) # Analysis with Dunnett-type comparison AnalysisModel() + Test(id = "Low vs Placebo", samples = samples("Placebo", "Low Dose"), method = "TTest") + Test(id = "Mid vs Placebo", samples = samples("Placebo", "Mid Dose"), method = "TTest") + Test(id = "High vs Placebo", samples = samples("Placebo", "High Dose"), method = "TTest") + MultAdjProc(proc = "HolmAdj") ``` ### Active Comparator Design ```r # Treatment vs Active Control DataModel() + Sample(id = "Active Control", outcome.par = parameters(mean = 0.4, sd = 1)) + Sample(id = "New Treatment", outcome.par = parameters(mean = 0.6, sd = 1)) ``` ## Multi-Endpoint Designs ### Co-Primary Endpoints Both endpoints must be significant for trial success. ```r # Correlated endpoints corr.matrix <- matrix(c(1.0, 0.5, 0.5, 1.0), 2, 2) DataModel() + OutcomeDist(outcome.dist = "MVNormalDist") + SampleSize(100) + Sample(id = list("Control E1", "Control E2"), outcome.par = parameters( parameters(par = parameters( parameters(mean = 0, sd = 1), parameters(mean = 0, sd = 1) ), corr = corr.matrix))) + Sample(id = list("Treatment E1", "Treatment E2"), outcome.par = parameters( parameters(par = parameters( parameters(mean = 0.4, sd = 1), parameters(mean = 0.3, sd = 1) ), corr = corr.matrix))) # Evaluation: Conjunctive power (both must be significant) EvaluationModel() + Criterion(id = "Co-primary", method = "ConjunctivePower", tests = tests("E1 Test", "E2 Test"), par = parameters(alpha = 0.025)) ``` ### Hierarchical Endpoints Primary must succeed before secondary is tested. ```r # Primary → Key Secondary → Other Secondary AnalysisModel() + Test(id = "Primary", ...) + Test(id = "Key Secondary", ...) + Test(id = "Other Secondary", ...) + MultAdjProc(proc = "FixedSeqAdj") ``` ### Multiple Primary with Gatekeeping ```r # Two primary, two secondary MultAdjProc( proc = "ParallelGatekeepingAdj", par = parameters( family = families(family1 = c(1, 2), family2 = c(3, 4)), proc = families(family1 = "HolmAdj", family2 = "HolmAdj"), gamma = families(family1 = 0.8, family2 = 1) ) ) ``` ## Stratified Designs ### Single Stratification Factor ```r # simtrial stratification sim_pw_surv( n = 400, stratum = data.frame( stratum = c("Low Risk", "High Risk"), p = c(0.4, 0.6) # Prevalence ), fail_rate = data.frame( stratum = rep(c("Low Risk", "High Risk"), each = 2), period = rep(1, 4), treatment = rep(c("control", "experimental"), 2), duration = rep(100, 4), rate = c(0.03, 0.02, 0.06, 0.04) # Different by stratum ) ) ``` ### Biomarker-Defined Subgroups ```r # Marker-positive and marker-negative populations DataModel() + OutcomeDist(outcome.dist = "NormalDist") + SampleSize(100) + Sample(id = "Control M+", outcome.par = parameters(mean = 0, sd = 1)) + Sample(id = "Control M-", outcome.par = parameters(mean = 0, sd = 1)) + Sample(id = "Treatment M+", outcome.par = parameters(mean = 0.6, sd = 1)) + Sample(id = "Treatment M-", outcome.par = parameters(mean = 0.2, sd = 1)) # Pooled analysis (Overall Population) AnalysisModel() + Test(id = "Overall", samples = samples(c("Control M+", "Control M-"), c("Treatment M+", "Treatment M-")), method = "TTest") # Subgroup analysis AnalysisModel() + Test(id = "M+ Subgroup", samples = samples("Control M+", "Treatment M+"), method = "TTest") + Test(id = "M- Subgroup", samples = samples("Control M-", "Treatment M-"), method = "TTest") ``` ## Event-Driven Designs ### Time-to-Event with Fixed Events ```r # Mediana event-driven DataModel() + OutcomeDist(outcome.dist = "ExpoDist", outcome.type = "event") + Event(n.events = c(300, 350, 400), rando.ratio = c(1, 1)) + Design( enroll.period = 24, study.duration = 48, enroll.dist = "UniformDist", dropout.dist = "ExpoDist", dropout.dist.par = parameters(rate = 0.01) ) + Sample(id = "Control", outcome.par = parameters(rate = log(2)/12)) + Sample(id = "Treatment", outcome.par = parameters(rate = log(2)/18)) ``` ### PFS/OS Correlated Endpoints ```r # Correlated survival endpoints DataModel() + OutcomeDist(outcome.dist = "MVExpoPFSOSDist", outcome.type = c("event", "event")) + Event(n.events = 350, rando.ratio = c(1, 1)) + Sample(id = list("Control PFS", "Control OS"), outcome.par = parameters( parameters( par = parameters( parameters(rate = log(2)/6), # PFS parameters(rate = log(2)/15) # OS ), corr = matrix(c(1, 0.3, 0.3, 1), 2, 2) ))) + Sample(id = list("Treatment PFS", "Treatment OS"), outcome.par = parameters( parameters( par = parameters( parameters(rate = log(2)/9), parameters(rate = log(2)/20) ), corr = matrix(c(1, 0.3, 0.3, 1), 2, 2) ))) ``` ## Adaptive Designs ### Sample Size Re-Estimation Concept: Adjust sample size at interim based on observed effect size. ```r # Simulation framework for adaptive design # 1. Generate interim data # 2. Estimate effect size # 3. Re-calculate sample size # 4. Complete enrollment # 5. Perform final analysis simulate_adaptive <- function(initial_n, interim_frac, target_power) { # Stage 1: Interim n_interim <- round(initial_n * interim_frac) interim_data <- generate_data(n_interim) effect_estimate <- estimate_effect(interim_data) # Re-estimate sample size new_n <- calculate_sample_size(effect_estimate, target_power) new_n <- max(new_n, initial_n) # Cannot decrease # Stage 2: Continue to new_n final_data <- generate_data(new_n) return(final_data) } ``` ### Response-Adaptive Randomization Concept: Adjust randomization ratio based on interim results. **Note:** More common in Bayesian settings; simtrial/Mediana focus on fixed designs. ## Design Selection Flowchart ``` START │ ├─ How many treatment arms? │ ├─ 2 → Two-arm parallel │ └─ 3+ → Multi-arm design │ ├─ How many primary endpoints? │ ├─ 1 → Single primary │ ├─ 2 (both required) → Co-primary │ └─ 2+ (any success) → Multiple primary with multiplicity │ ├─ Are there secondary endpoints? │ ├─ Yes, hierarchical → Fixed-sequence or gatekeeping │ └─ Yes, equal priority → Holm/Hochberg │ ├─ Is stratification needed? │ ├─ Yes → Stratified randomization │ └─ No → Simple randomization │ ├─ Endpoint type? │ ├─ Continuous → Normal-based tests │ ├─ Binary → Proportion tests │ ├─ Time-to-event → Logrank/survival methods │ └─ Count → Poisson/NegBinom tests │ └─ Interim analyses needed? ├─ Yes → Group sequential design └─ No → Fixed design END ``` ## Best Practices 1. **Match Design to Objectives**: Choose design that directly addresses primary question 2. **Consider Multiplicity Early**: Plan adjustment strategy during design phase 3. **Stratify When Important**: Use stratification for known prognostic factors 4. **Pre-specify Everything**: Document design choices before data collection 5. **Simulate Extensively**: Validate operating characteristics via simulation 6. **Consider Regulatory Path**: Align design with agency expectations