rbmiUtils (version 0.3.0)

summary.pool: Summary Method for Pool Objects

Description

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 pool
summary(object, alpha = 0.05, ...)

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()

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).

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

• print.pool() for compact tabular output

• tidy_pool_obj() for full tidy tibble output

• rbmi::pool() to create pool objects

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)
pool_obj <- pool(ana_obj)
summary(pool_obj)
# }