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Summary Method for Pool Objects

Source: R/pool_methods.R
summary.pool.Rd

Provides a detailed visit-level breakdown of pooled analysis results with significance flags. Shows treatment comparisons and least squares means grouped by visit.

Usage

# S3 method for class 'pool'
summary(object, alpha = 0.05, ...)

Arguments

object

An object of class pool, typically obtained from rbmi::pool().

alpha

Numeric. Significance threshold for flagging p-values. Default is 0.05. Flags are: * for p < alpha, ** for p < 0.01, *** for p < 0.001.

...

Additional arguments (currently unused).

Value

Invisibly returns a list with:

n_parameters

Number of parameters in the pool object

visits

Character vector of unique visit names

method

Pooling method used

n_imputations

Number of imputations combined

conf.level

Confidence level

tidy_df

The full tidy tibble from tidy_pool_obj()

Details

The summary output groups results by visit, showing treatment comparisons with significance flags and least squares means. This provides a quick overview of which visits have statistically significant treatment effects.

Significance flags:

  • * p < alpha (default 0.05)

  • ** p < 0.01

  • *** p < 0.001

See also

Examples

# \donttest{
library(rbmi)
library(rbmiUtils)
data("ADMI")

ADMI$TRT <- factor(ADMI$TRT, levels = c("Placebo", "Drug A"))
ADMI$USUBJID <- factor(ADMI$USUBJID)
ADMI$AVISIT <- factor(ADMI$AVISIT)

vars <- set_vars(
  subjid = "USUBJID", visit = "AVISIT", group = "TRT",
  outcome = "CHG", covariates = c("BASE", "STRATA", "REGION")
)
method <- method_bayes(n_samples = 20, control = control_bayes(warmup = 20))

ana_obj <- analyse_mi_data(ADMI, vars, method, fun = ancova)
#> Warning: Data contains 100 imputations but method expects 20. Using first 20
#> imputations.
pool_obj <- pool(ana_obj)
summary(pool_obj)
#> 
#> ── Pool Object Summary ─────────────────────────────────────────────────────────
#> Method: rubin
#> N imputations: 20
#> Confidence: 95%
#> Alternative: two.sided
#> 6 parameters across 2 visits
#> ────────────────────────────────────────────────────────────────────────────────
#> 
#> ── Week 24 ──
#> 
#> Treatment Comparisons
#> Treatment Comparison: -2.18 (-2.54, -1.82) p=< 0.001 ***
#> Least Squares Means
#> Least Squares Mean for Reference at Week 24: 0.09 (-0.17, 0.34)
#> Least Squares Mean for Alternative at Week 24: -2.1 (-2.34, -1.85)
#> 
#> ── Week 48 ──
#> 
#> Treatment Comparisons
#> Treatment Comparison: -3.79 (-4.3, -3.29) p=< 0.001 ***
#> Least Squares Means
#> Least Squares Mean for Reference at Week 48: 0.04 (-0.33, 0.4)
#> Least Squares Mean for Alternative at Week 48: -3.76 (-4.11, -3.41)
# }