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sbwadjust

Covariate adjustment for randomized trials using stable balancing weights (SBW), from “Simple Covariate Adjustment for Many Estimands Using Stable Balancing Weights” (Irish, Zubizarreta, Luedtke; arXiv:2609.01638).

library(sbwadjust)

## 1. Design stage -- before unblinding. Outcome-blind, prespecifiable.
sbw <- sbw_weights(
  balance   = ~ age + sex + bmi + region,   # covariates to balance
  data      = trial_baseline,
  treatment = arm
)
summary(sbw)          # balance table + effective-sample-size diagnostics

## 2. Analysis stage -- after unblinding. `trial_outcomes` has one row per
##    participant, in the same order as `trial_baseline`.
sbw_estimate(sbw, Y ~ 1, estimand = "RR", data = trial_outcomes)      # relative risk
sbw_estimate(sbw, Surv(time, status) ~ 1, data = trial_outcomes,
             estimand = "survival_ratio", horizon = 52)

## An estimand we don't cover? Take the weights, use your own estimator.
w <- weights(sbw)

sbw_weights() fits exact-balance SBW (imbalance tolerance fixed at zero, matching the paper) via a closed-form solve with a nonnegative quadratic-programming fallback. sbw_estimate() computes one of a closed menu of estimands from the fitted weights, with a bootstrap confidence interval: average treatment effect ("ATE"), relative risk ("RR"), survival ratio ("survival_ratio", needs a horizon argument), Mann-Whitney win probability ("mann_whitney", finite/uncensored outcomes), and quantile contrasts ("quantile_diff" / "quantile_ratio", with a probs argument). No user-supplied functional is accepted — an uncovered estimand means: take weights(sbw) and run (and bootstrap) your own estimator.

The simulation studies and data application that build on these routines live in sbw-covariate-adjustment-code.

Installation

# install.packages("remotes")
remotes::install_github("kaylairish/sbwadjust")

What’s in it

File	Functions
`R/sbw_weights.R`	`sbw_weights` — formula/data/treatment front end to `get_sbws_for_study`, returning an `sbw_fit` object with `print`, `summary`, `plot`, and `weights` methods.
`R/sbw_estimate.R`	`sbw_estimate` — treatment-effect estimation from an `sbw_fit`: ATE, RR, survival ratio, Mann-Whitney, and quantile contrasts, each with a bootstrap CI.
`R/weights.R`	`get_weights_for_group_neg`, `get_weights_for_group_nonneg`, `get_sbws_for_study` — fit SBW for one arm or a full two-arm study: a closed-form solve first, falling back to a nonnegative quadratic program when the closed-form weights go negative.
`R/km_ratio.R`	`km_ratio_greenwood`, `boot_km_ratio` — weighted Kaplan–Meier survival-ratio point estimates and bootstrap CIs (Wald or percentile); `sbw_estimate(..., estimand = "survival_ratio")` wraps `boot_km_ratio`.

Estimand coverage note. ATE, RR, and survival ratio have full worked estimators and simulations in the paper. mann_whitney implements only the finite/uncensored case given in the JASA supplement (right-censored outcomes are future work). Quantile contrasts use the natural SBW-weighted empirical-quantile plug-in (a generalized-inverse weighted quantile, arm-specific difference/ratio, bootstrap CI); this recipe isn’t spelled out verbatim in the paper, unlike the other estimands. RMST is not included in this release.

Tests

# from a local clone
devtools::test()

Each exported function has a regression test pinned to a fixed seed / toy input (tests/testthat/).

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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