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Create Regulatory-Style Efficacy Summary Table

Source: R/efficacy_table.R
efficacy_table.Rd

Takes an rbmi pool object and produces a publication-ready gt table in the style of CDISC/ICH Table 14.2.x. The table displays least squares means by treatment arm, treatment differences, confidence intervals, and p-values, organized by visit row groups.

Usage

efficacy_table(
  pool_obj,
  title = NULL,
  subtitle = NULL,
  digits = 2,
  ci_level = NULL,
  arm_labels = NULL,
  pval_digits = 3,
  pval_threshold = 0.001,
  font_family = NULL,
  font_size = NULL,
  row_padding = NULL,
  ...
)

Arguments

pool_obj

A pooled analysis object of class "pool", typically obtained from rbmi::pool() after calling analyse_mi_data().

title

Optional character string for the table title.

subtitle

Optional character string for the table subtitle.

digits

Integer. Number of decimal places for estimates and standard errors. Default is 2.

ci_level

Numeric. Confidence level for CI column labeling. If NULL (the default), extracted from pool_obj$conf.level. Falls back to 0.95 if neither is available.

arm_labels

Named character vector with elements "ref" and "alt" providing custom labels for the reference and treatment arms. If NULL (the default), uses "Reference" and "Treatment".

pval_digits

Integer. Number of decimal places for p-values. Default is 3.

pval_threshold

Numeric. P-values below this threshold are displayed as "< threshold". Default is 0.001.

font_family

Optional character string specifying the font family for the table. When NULL (default), uses gt's default font. Applied via gt::opt_table_font().

font_size

Optional numeric value specifying the table font size in pixels. When NULL (default), uses gt's default size. Applied via gt::tab_options().

row_padding

Optional numeric value specifying the vertical padding for data rows in pixels. When NULL (default), uses gt's default padding. Smaller values (e.g., 2-3) create compact regulatory-style tables.

...

Additional arguments passed to gt::gt().

Value

A gt table object of class gt_tbl.

Details

This function assumes a single-parameter-per-visit pool object (the standard output from an rbmi ANCOVA or MMRM pipeline). It internally calls tidy_pool_obj() to parse the pool object, then constructs the gt table.

Arm labels: Use the arm_labels parameter to customize arm names in the table. For example, arm_labels = c(ref = "Placebo", alt = "Drug A") will display "LS Mean (Placebo)" and "LS Mean (Drug A)" instead of the defaults.

Customization: The returned gt object can be further customized using standard gt piping, e.g., efficacy_table(pool_obj) |> gt::tab_options(...).

Example output:

See also

Examples

# \donttest{
if (requireNamespace("gt", quietly = TRUE)) {
  library(rbmi)
  data("ADMI", package = "rbmiUtils")
  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, thin = 1)
  )

  ana_obj <- analyse_mi_data(ADMI, vars, method, fun = ancova)
  pool_obj <- pool(ana_obj)

  # Basic table
  tbl <- efficacy_table(pool_obj)

  # Publication-styled table
  efficacy_table(
    pool_obj,
    title = "Table 14.2.1: ANCOVA of Change from Baseline",
    subtitle = "Mixed Model for Repeated Measures",
    arm_labels = c(ref = "Placebo", alt = "Drug A"),
    font_size = 12,
    row_padding = 4
  )
}
#> Warning: Data contains 100 imputations but method expects 20. Using first 20
#> imputations.



  
Table 14.2.1: ANCOVA of Change from Baseline
    
Mixed Model for Repeated Measures
    

      Estimate
      Std. Error
      95% CI
      P-value
    
Week 24
    
LS Mean (Placebo)
0.09
0.13
(-0.17, 0.34)
0.514
LS Mean (Drug A)
−2.10
0.13
(-2.34, -1.85)
< 0.001
Treatment Difference
−2.18
0.18
(-2.54, -1.82)
< 0.001
Week 48
    
LS Mean (Placebo)
0.04
0.19
(-0.33, 0.40)
0.846
LS Mean (Drug A)
−3.76
0.18
(-4.11, -3.41)
< 0.001
Treatment Difference
−3.79
0.26
(-4.30, -3.29)
< 0.001
Pooling method: rubin
    
Number of imputations: 20
    
Confidence level: 95%
    
# }