LAGOtrials playground

Design a LAGO optimization interactively, in your browser, with no install. Pick a bundled dataset or upload your own CSV, choose the outcome and intervention components, set the bounds, per-unit costs and outcome goal, then press Run. The recommendation is drawn with the package's own interactive webR-powered charts.

Starting webR…

First load downloads R and the package (tens of MB) once and can take up to a minute; after that, runs are quick.

1. Data

Bundled dataset

… or upload a CSV

Drop a .csv here, or click to choose

Comma-separated with a header row; numeric columns become the outcome and component choices.

2. Model

Outcome column

Outcome type

Outcome goal: 0.85

Direction

Intervention components

Interaction terms (optional — model the product of two components)

Center characteristics (optional — optimize for a center holding these at set values)

Additional covariates (optional — extra columns to adjust the outcome model for)

Fixed center effects (optional — adjust the outcome model for each center; replaces center characteristics)

Power goal (optional — also plan for the next stage’s statistical power; binary outcomes only)

R code for this configuration

Paste this into R (after library(LAGOtrials)) to reproduce the run.

# choose an outcome and at least one intervention component

Result

Sensitivity sweep

vary from to steps

Re-runs the recommendation across a range of one input to show how it moves. Each step is a full optimization, and the confidence set is skipped for speed.

Budget: best outcome for a cost

budget

The reverse of an outcome goal: given a fixed cost budget, finds the best outcome you can reach and the intervention that reaches it. Runs several optimizations, so it takes a few seconds.

webR runs a WebAssembly build of R (4.6.0) entirely client-side; nothing you load or type leaves your browser. The confidence-set plot is drawn for one- or two-component interventions (three or more still get the cost curves and the full console summary). This playground exposes the common options; for the rest — a custom GLM family/link, custom center weights, and more — call lago_optimization() in R (copy the snippet above as a starting point). To shape the per-component cost functions themselves (including non-linear ones), use the interactive cost designer. See the package documentation for the complete API.