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Sample Number Needed to Treat Data

Source: R/hazard-plot.R
sample_nnt_data.Rd

Computes the number needed to treat (NNT) and absolute risk reduction (ARR) over time from a two-group survival difference, matching the output of tp.hp.numtreat.survdiff.matched.sas.

Usage

sample_nnt_data(
  n = 500,
  time_max = 10,
  n_points = 500,
  groups = c(Control = 1, Treatment = 0.7),
  shape = 1.5,
  scale = 8,
  ci_level = 0.95,
  seed = 42L
)

Arguments

n

Number of patients per group (used for CI width). Default 500.

time_max

Upper end of the time axis (years). Default 10.

n_points

Number of prediction grid points. Default 500.

groups

Named numeric vector of length 2; hazard multipliers for groups 1 and 2. The group with the smaller multiplier has better survival. Default c("Control" = 1.0, "Treatment" = 0.7).

shape

Weibull shape. Default 1.5.

scale

Weibull scale (years). Default 8.0.

ci_level

Confidence level. Default 0.95.

seed

Random seed. Default 42.

Value

A data frame with columns time, arr, arr_lower, arr_upper, nnt, nnt_lower, nnt_upper.

Details

SAS column mapping:

  • time ← prediction grid (years)

  • arr ← absolute risk reduction (survival difference, %)

  • arr_lower / arr_upper ← CI on ARR

  • nnt ← number needed to treat (= 100 / ARR)

  • nnt_lower / nnt_upper ← CI on NNT (inverted from ARR CI)

See also

hv_nnt(), hv_survival_difference()

Examples

nnt_dat <- sample_nnt_data(
  groups = c("Control" = 1.0, "Treatment" = 0.7)
)
head(nnt_dat)
#>         time         arr   arr_lower  arr_upper nnt nnt_lower nnt_upper
#> 1 0.01000000 0.001831068 -0.03739885 0.04106099  NA        NA        NA
#> 2 0.03002004 0.009522643 -0.08737593 0.10642122  NA  939.6622        NA
#> 3 0.05004008 0.020489267 -0.13072627 0.17170481  NA  582.3949        NA
#> 4 0.07006012 0.033934691 -0.17121839 0.23908777  NA  418.2564        NA
#> 5 0.09008016 0.049459769 -0.21006489 0.30898443  NA  323.6409        NA
#> 6 0.11010020 0.066810699 -0.24784775 0.38146915  NA  262.1444        NA