README.md
In sbwadjust: Stable Balancing Weights for Covariate Adjustment in Trials

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.

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

| 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.