Treatment-effect estimates by model

Four competing efficacy models with their treatment-effect point estimate, 95% confidence-interval bounds, and nominal p-value. Shaped as a numeric-cell table (one row per model) rather than the usual pre-formatted character cells, so it exercises the col_spec(format = ...) + col_spec(na_text = ...) cascade. One row (MMRM) carries NA CI bounds to demonstrate na_text.

Usage

cdisc_eff_estimates

Format

A data frame with 4 rows and 5 columns:

model

Model name ("ANCOVA", "MMRM", "Cox PH", "Bootstrap (1000 reps)").

estimate

Numeric point estimate.

lower_ci, upper_ci

Numeric 95% CI bounds. The MMRM row has NA bounds.

p_value

Nominal p-value (numeric).

Source

Synthetic estimates following the _archive/.../arframe-examples/tables/tte-summary.qmd and efficacy-bor.qmd shapes. Not derived from any patient-level data — illustrative values only.

See also

col_spec() for the formatting cascade these values exercise.

Examples

# Numeric-cell efficacy table — format = "%.2f" pins precision,
# na_text = "--" renders the MMRM row's NA bounds as dashes.
tabular(cdisc_eff_estimates, titles = "Treatment-effect estimates by model") |>
  cols(
    model    = col_spec(usage = "group",  label = "Model", valign = "top"),
    estimate = col_spec(label = "Estimate", align = "decimal",
                        format = "%.2f"),
    lower_ci = col_spec(label = "Lower\n95% CI", align = "decimal",
                        format = "%.2f", na_text = "--"),
    upper_ci = col_spec(label = "Upper\n95% CI", align = "decimal",
                        format = "%.2f", na_text = "--"),
    p_value  = col_spec(label = "p-value",  align = "decimal",
                        format = "%.4f")
  )
 
Treatment-effect estimates by model
 

Estimate
Lower
95% CI
Upper
95% CI
p-value
ANCOVA
-2.31
-3.42
-1.20
0.0042
 
MMRM
-2.45
--   
--   
0.0061
 
Cox PH
 0.81
 0.68
 0.97
0.0087
 
Bootstrap (1000 reps)
-2.29
-3.50
-1.10
0.0050