LAGOtrials - Learn-as-You-Go Adaptive Trial Optimization

Optimizes interventions for adaptive "Learn-As-you-GO" (LAGO) trials, in which the intervention package is updated between stages using accumulating data. Given data from completed stages, lago_optimization() fits the outcome model (via 'stats' generalized linear models or, for clustered designs, cluster-robust variance) and computes the cost-optimal recommended intervention for the next stage that is expected to reach a target mean outcome and/or a desired statistical power. get_confidence_set() constructs confidence sets for the optimal intervention, and visualize_cost() and lago_report() summarize the cost surface and results. Methods are described in Nevo, Lok and Spiegelman (2021) <doi:10.1214/20-aos1978> and Bing, Spiegelman, Nevo and Lok (2025) <doi:10.1093/biomtc/ujaf061>.