The data | Recommending an intervention | Visualizing the confidence set | Adding a power goal | How sensitive is the recommendation?
Package: LAGOtrials 1.1.0
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>.
Authors:
LAGOtrials_1.1.0.tar.gz
LAGOtrials_1.1.0.zip(r-4.7-x86_64)LAGOtrials_1.1.0.zip(r-4.7-arm64)LAGOtrials_1.1.0.zip(r-4.6-x86_64)LAGOtrials_1.1.0.zip(r-4.6-arm64)LAGOtrials_1.1.0.zip(r-4.5-x86_64)
LAGOtrials_1.1.0.tgz(r-4.6-x86_64)LAGOtrials_1.1.0.tgz(r-4.6-arm64)LAGOtrials_1.1.0.tgz(r-4.5-x86_64)LAGOtrials_1.1.0.tgz(r-4.5-arm64)
LAGOtrials_1.1.0.tar.gz(r-4.7-arm64)LAGOtrials_1.1.0.tar.gz(r-4.7-x86_64)LAGOtrials_1.1.0.tar.gz(r-4.6-arm64)LAGOtrials_1.1.0.tar.gz(r-4.6-x86_64)
LAGOtrials_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
LAGOtrials/json (API)
| # Install 'LAGOtrials' in R: |
| install.packages('LAGOtrials', repos = c('https://correspondmerchant.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/correspondmerchant/lago-r-package/issues
Pkgdown/docs site:https://correspondmerchant.github.io
Datasets:
- BB_data - BetterBirth Data
- BB_proportions - BetterBirth Data (for continuous outcome analysis, using EBP proportions as a continuous outcome).
- main_pulesa_data - PULESA hypertension-care data
- new_pulesa_data - PULESA hypertension-care data
adaptive-designbiostatisticscausal-inferenceclinical-trialsimplementation-trialsintervention-studylagostatisticscpp
Last updated from:d88eb3b841. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 241 | ||
| linux-devel-x86_64 | OK | 322 | ||
| source / vignettes | OK | 218 | ||
| linux-release-arm64 | OK | 232 | ||
| linux-release-x86_64 | OK | 278 | ||
| macos-release-arm64 | OK | 334 | ||
| macos-release-x86_64 | OK | 475 | ||
| macos-oldrel-arm64 | OK | 354 | ||
| macos-oldrel-x86_64 | OK | 550 | ||
| windows-devel-arm64 | OK | 514 | ||
| windows-devel-x86_64 | OK | 303 | ||
| windows-release-arm64 | OK | 491 | ||
| windows-release-x86_64 | OK | 285 | ||
| windows-oldrel-x86_64 | OK | 209 | ||
| wasm-release | OK | 100 |
Exports:get_confidence_setlago_budgetlago_optimizationlago_reportlago_sensitivityvisualize_cost
Dependencies:base64encbslibcachemclicommonmarkcpp11digestfarverfastmapfontawesomefsggplot2gluegtablehtmltoolshttpuvisobandjquerylibjsonlitelabelinglaterlifecyclemagrittrMASSmemoisemimeNlcOptimotelpromisesquadprogR6rappdirsRColorBrewerRcpprjerlangS7sassscalesshinyshinyjssourcetoolsvctrsviridisLitewithrxtable
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| BetterBirth Data (for binary outcome analysis) | BB_data |
| BetterBirth Data (for continuous outcome analysis, using EBP proportions as a continuous outcome). | BB_proportions |
| Budget-constrained LAGO optimization | lago_budget |
| Run a LAGO optimization | lago_optimization |
| Generate an HTML report for a LAGO optimization result | lago_report |
| Sensitivity analysis for a LAGO recommendation | lago_sensitivity |
| PULESA hypertension-care data (main model) | main_pulesa_data |
| PULESA hypertension-care data (new model) | new_pulesa_data |
| Plot a LAGO optimization result | plot.lago |
| Plot a budget-constrained LAGO result | plot.lago_budget |
| Plot a LAGO sensitivity analysis | plot.lago_sensitivity |
| Print a LAGO optimization result | print.lago |
| Print a budget-constrained LAGO result | print.lago_budget |
| Print a LAGO sensitivity analysis | print.lago_sensitivity |
| Summarize a LAGO optimization result | summary.lago |
| Visualize and choose intervention cost functions | visualize_cost |