pairwise_rmst_test()

Pairwise RMST tests for all group pairs with multiple-comparison correction.

Usage

pairwise_rmst_test(
    surv,
    tau,
    group,
    *,
    estimand="difference",
    strata=None,
    correction="holm",
    conf_level=0.95,
    format=None
)

Compares RMST between all pairs of groups, with optional multiple-comparison adjustment. This answers the question: “Which pairs of groups have significantly different RMST?” when you have more than two groups.

Parameters

surv: Surv: A right-censored Surv response (time-to-event data).
tau: float: The restriction time for RMST calculation.
group: Any: Group labels, one per observation. Can be array-like or categorical variable. Must have at least 2 unique levels to create pairs.
estimand: str = "difference": Type of estimand: "difference" (default), "ratio", or "percentage_difference".
strata: Any | None = None: (Optional) Stratification variable. Each pairwise test is stratified by this factor.
correction: str = "holm": Multiple-comparison adjustment: "holm" (default), "bh", "bonferroni", or "none".
conf_level: float = 0.95: Confidence level for intervals (the default is 0.95).
format: str | None = None: Output format: None (auto-detect), "pandas", "polars", or "pyarrow".

Returns

DataFrame: One row per pair of groups with columns group1, group2, rmst1, rmst2, estimate, se, lower_ci, upper_ci, statistic, p_value, and p_adjusted.

Details

All \binom{k}{2} pairwise comparisons are performed, where k is the number of unique group levels. The raw p-values are then adjusted for multiplicity using the chosen correction method. Holm’s step-down procedure (the default) controls the family-wise error rate without assuming independence between tests.

Examples

Compare one-year RMST across sex groups in the lung cancer dataset with Holm correction:

import greenwood as gw

# Load data and build a right-censored response
lung = gw.load_dataset("lung", backend="polars")
y = gw.Surv.right(lung["time"], event=(lung["status"] == 2))

# Run pairwise RMST comparisons with Holm-adjusted p-values
gw.pairwise_rmst_test(y, tau=365, group=lung["sex"], format="polars")

shape: (1, 11)

group1	group2	rmst1	rmst2	estimate	se	lower_ci	upper_ci	statistic	p_value	p_adjusted
i64	i64	f64	f64	f64	f64	f64	f64	f64	f64	f64
1	2	241.495085	297.46541	-55.970324	14.958126	-85.287712	-26.652936	-3.741801	0.000183	0.000183