% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ild_msm_bootstrap.R
\name{ild_msm_inference}
\alias{ild_msm_inference}
\title{MSM and IPW inference for weighted mixed models}
\description{
After [ild_ipw_refit()] (or any \code{lmer(..., weights =)} with \code{attr(fit, "ild_data")}),
uncertainty for MSM-style **estimands** should not rely on default \code{lmer} standard errors.
tidyILD supports:
\itemize{
  \item \strong{Cluster bootstrap} — [ild_msm_bootstrap()] resamples persons (clusters),
    refits the weighted model, and collects fixed effects; use
    \code{weight_policy = "reestimate_weights"} and a \code{weights_fn} to re-fit propensity
    weights on each replicate when that matches your estimand (computationally heavier;
    preferred for many MSM workflows). \code{weight_policy = "fixed_weights"} keeps the
    weights from the resampled rows (fast approximation; ignores first-stage uncertainty).
  \item \strong{Cluster-robust sandwich Wald} — [ild_robust_se()] / [tidy_ild_model(..., se = "robust")]
    apply \pkg{clubSandwich} at the **person** level on the **weighted outcome model only**; they
    do **not** account for estimation error in IPW/IPCW weights unless you treat weights as fixed.
  \item \strong{Bayesian} — Full joint Bayesian MSM is not built in. See [ild_brms()] for
    hierarchical outcome models; combining external IPW row weights with Stan is delicate;
    prefer explicit joint/sensitivity designs and specialized causal packages for Bayes MSM.
}
}
\seealso{
[ild_msm_bootstrap()], [tidy_ild_msm_bootstrap()], [ild_ipw_refit()],
  [ild_robust_se()], [tidy_ild_model()], [ild_brms()], [ild_msm_estimand()],
  [ild_msm_fit()]
}
\keyword{internal}
