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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>.
Getting started
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Package details |
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| Author | Ante Bing [aut, cre], Minh Bui [aut], Jingyu Cui [aut], Mike Bostock [cph] (Author of the bundled D3 v7 library, inst/js/d3.v7.min.js) |
| Maintainer | Ante Bing <abing@bu.edu> |
| License | GPL-3 |
| Version | 1.1.1 |
| URL | https://correspondmerchant.github.io/LAGO-R-Package/ https://github.com/correspondMerchant/LAGO-R-Package |
| Package repository | View on CRAN |
| Installation |
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