IPW (Inverse Probability Weighting)

Use the synthetic data setup first; it defines data and the columns used below. These are advanced direct R examples.

Use propensity-based weights to adjust for measured confounders. Inspect overlap, weight extremes and effective sample size before interpreting the estimates.

ipw_result <- causal_ipw(data, "Immunotherapy", "Response",
                        confounders = c("Age", "Sex", "Stage", "ECOG"),
                        outcome_type = "binary",
                        stabilize = TRUE)  # Use stabilized weights

Read the result

The wrapper parameter is stabilize. Inspect positivity, raw and truncated weights and effective sample size. A binary-exposure survival HR is a limited weighted Cox summary; continuous-exposure primary survival estimation is not implemented for this method.

See effect scales and reporting, diagnostics and the current support reference.