# Advanced Adaptive Trials > Adaptive trial designs in R, including platform, basket, MAMS, response-adaptive, and interim decision methods. - **Type:** Skill - **Install:** `agentstack add skill-choxos-biostatagent-advanced-adaptive-trials` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [choxos](https://agentstack.voostack.com/s/choxos) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [choxos](https://github.com/choxos) - **Source:** https://github.com/choxos/BiostatAgent/tree/main/plugins/r-tidy-modeling/skills/advanced-adaptive-trials ## Install ```sh agentstack add skill-choxos-biostatagent-advanced-adaptive-trials ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Advanced Adaptive Trial Designs in R ## Overview Advanced adaptive clinical trial designs including platform trials, basket and umbrella trials, response-adaptive randomization, multi-arm multi-stage designs, Bayesian adaptive methods, and sample size re-estimation techniques. ## Platform Trials ### Using adaptr Package ```r library(adaptr) # Define a platform trial with multiple arms setup setup_trial_binom( highest_is_best = TRUE, soften_power = 0.5 # Softening for allocation ) # Add arm dropping rules setup add_arm( arm = "Arm_C", true_y = 0.50, start_look = 3 # Add at third interim ) # Simulate sims_platform p0 fit_mem$post_prob # Cluster map (which baskets share information) plot(fit_mem, type = "cluster") ``` ### Hierarchical Model for Baskets ```r library(basket) # Full Bayesian hierarchical model fit_hier p0) > 0.975 qc = 0.10, # Futility: P(rate > p0) < 0.10 lower.tail = FALSE ) # Operating characteristics oc <- oc2S( prior_treatment = robust_prior, prior_control = robust_prior, n1_treatment = 30, # Stage 1 treatment n1_control = 30, # Stage 1 control n2_treatment = 30, # Stage 2 treatment n2_control = 30, # Stage 2 control decision = decision ) # Plot OC curves plot(oc) # Type I error and power summary(oc) ``` ## Group Sequential Designs with rpact ### O'Brien-Fleming Design ```r library(rpact) # O'Brien-Fleming group sequential design design_of <- getDesignGroupSequential( kMax = 3, # Number of stages alpha = 0.025, # One-sided alpha beta = 0.20, # Type II error sided = 1, typeOfDesign = "OF", informationRates = c(0.33, 0.67, 1.0) ) summary(design_of) plot(design_of) # Boundaries design_of$criticalValues # Z-score boundaries design_of$alphaSpent # Cumulative alpha spent ``` ### Sample Size Calculation ```r library(rpact) # Sample size for survival endpoint sample_size <- getSampleSizeSurvival( design = design_of, lambda1 = log(2) / 24, # Control median = 24 months lambda2 = log(2) / 36, # Treatment median = 36 months (HR = 0.67) accrualTime = 24, # Accrual period followUpTime = 12, # Additional follow-up dropoutRate1 = 0.05, # Control dropout dropoutRate2 = 0.05, # Treatment dropout allocationRatioPlanned = 1 ) summary(sample_size) # Events and sample size at each look sample_size$eventsPerStage sample_size$numberOfSubjects ``` ### Interim Analysis ```r library(rpact) # Perform interim analysis interim <- getAnalysisResults( design_of, dataInput = getDataset( n = c(100, 100), # Cumulative n by stage events = c(40, 80), # Cumulative events by stage logRanks = c(2.1, 2.8) # Log-rank Z statistics ) ) summary(interim) # Can the trial stop? interim$finalStage # Final stage reached? interim$futilityStop # Stopped for futility? interim$rejectAtFinalStage # Rejected at final analysis? ``` ## Sample Size Re-Estimation ### Blinded SSR ```r library(rpact) # Sample size re-estimation based on interim data ssr <- getSampleSizeReestimation( design_of, stageResults = interim, conditionalPower = 0.80 # Target conditional power ) summary(ssr) ssr$sampleSizeNew # New sample size recommendation ``` ### Unblinded SSR ```r library(rpact) # Conditional power at interim cp <- getConditionalPower( design = design_of, stage = 2, stageResults = interim, nPlanned = c(50, 50), # Planned future n per arm assumedEffect = 0.67 # Assumed treatment effect (HR) ) summary(cp) # If CP too low, calculate required sample size if (cp$conditionalPower < 0.50) { # Increase sample size new_n <- getSampleSizeReestimation(design_of, interim, conditionalPower = 0.80) } ``` ## Graphical Multiplicity with gMCP ```r library(gMCPLite) # Define hypothesis graph # H1, H2: Primary endpoints; H3, H4: Secondary endpoints m <- matrix( c(0, 0.5, 0.5, 0, 0.5, 0, 0, 0.5, 0.5, 0, 0, 0.5, 0, 0.5, 0.5, 0), nrow = 4, byrow = TRUE ) weights <- c(0.5, 0.5, 0, 0) # Initial alpha allocation # Create graph graph <- gMCP::matrix2graph(m, weights) nodeNames(graph) <- c("H1_Primary", "H2_Primary", "H3_Secondary", "H4_Secondary") # Plot plot(graph) # Test with p-values pvalues <- c(0.01, 0.03, 0.02, 0.04) result <- gMCP::gMCP(graph, pvalues, alpha = 0.025) print(result) # Which hypotheses rejected? result@rejected ``` ## Key Packages Summary | Package | Purpose | |---------|---------| | adaptr | Platform trial simulation | | basket | Basket trial analysis | | MAMS | Multi-arm multi-stage designs | | rpact | Group sequential and adaptive designs | | RBesT | Bayesian evidence synthesis | | gMCPLite | Graphical multiplicity procedures | | gsDesign | Group sequential designs | | gsDesign2 | Enhanced group sequential | | Mediana | Clinical trial simulations | ## Best Practices 1. **Pre-specification**: Define adaptation rules before trial starts 2. **Type I error**: Ensure strong control under all adaptations 3. **Operating characteristics**: Simulate extensively under various scenarios 4. **Blinding**: Maintain blinding where possible during adaptations 5. **Documentation**: Document all decision rules in protocol 6. **Regulatory**: Engage regulators early for complex adaptive designs 7. **Implementation**: Plan for operational complexity of adaptations 8. **Analysis**: Plan for potential biases from adaptations ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [choxos](https://github.com/choxos) - **Source:** [choxos/BiostatAgent](https://github.com/choxos/BiostatAgent) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-choxos-biostatagent-advanced-adaptive-trials - Seller: https://agentstack.voostack.com/s/choxos - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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