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Prepare number-needed-to-treat data for plotting

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

Stores pre-computed NNT (or ARR) data as an hv_nnt object. Rows where estimate_col is NA (undefined when ARR is near zero) are optionally removed during construction via na_rm. Pass the result to plot.hv_nnt() to render the plot. Covers the NNT component of tp.hp.numtreat.survdiff.matched.sas.

Usage

hv_nnt(
  nnt_data,
  x_col = "time",
  estimate_col = "nnt",
  lower_col = NULL,
  upper_col = NULL,
  group_col = NULL,
  na_rm = TRUE
)

Arguments

nnt_data

Data frame of pre-computed NNT estimates. See sample_nnt_data().

x_col

Name of the time column. Default "time".

estimate_col

Name of the NNT (or ARR) column. Default "nnt".

lower_col

Lower CI column, or NULL. Default NULL.

upper_col

Upper CI column, or NULL. Default NULL.

group_col

Grouping column for multiple comparisons, or NULL. Default NULL.

na_rm

Remove rows where estimate_col is NA before storing in $data. Applied at construction time. Default TRUE.

Value

An S3 object of class c("hv_nnt", "hv_data").

See also

plot.hv_nnt(), sample_nnt_data(), hv_survival_difference()

Examples

library(ggplot2)

nnt_dat <- sample_nnt_data(
  n = 500, time_max = 20,
  groups = c("SVG" = 1.0, "ITA" = 0.75)
)

# 1. Build data object
nn <- hv_nnt(nnt_dat, lower_col = "nnt_lower", upper_col = "nnt_upper")
nn  # prints estimate column and CI flags
#> <hv_nnt>
#>   x col       : time
#>   estimate    : nnt
#>   CI          : nnt_lower -- nnt_upper
#>   na_rm       : TRUE
#>   $data       : 496 rows × 7 cols

# 2. Bare plot -- undecorated ggplot returned by plot.hv_nnt
p <- plot(nn)

# 3. Decorate: axis scales, labels, theme
p +
  scale_x_continuous(limits = c(0, 20), breaks = seq(0, 20, 5)) +
  scale_y_continuous(limits = c(0, 50), breaks = seq(0, 50, 10)) +
  labs(x = "Years", y = "Number Needed to Treat (NNT)") +
  hv_theme("poster")
#> Warning: Removed 248 rows containing missing values or values outside the scale range
#> (`geom_ribbon()`).
#> Warning: Removed 27 rows containing missing values or values outside the scale range
#> (`geom_line()`).


# ARR curve -- same 3-step pattern with a different estimate column
ar <- hv_nnt(nnt_dat, estimate_col = "arr",
               lower_col = "arr_lower", upper_col = "arr_upper",
               na_rm = FALSE)
plot(ar) +
  scale_y_continuous(limits = c(0, 50),
                     labels = function(x) paste0(x, "%")) +
  labs(x = "Years", y = "Absolute Risk Reduction (%)") +
  hv_theme("poster")
#> Warning: Removed 178 rows containing missing values or values outside the scale range
#> (`geom_ribbon()`).