vpc_tte {vpc}

VPC function for time-to-event (survival) data

Description

This function can be used for either single time-to-event (TTE) or repeated time-to-event (RTTE) data.

Usage

vpc_tte(
  sim = NULL,
  obs = NULL,
  psn_folder = NULL,
  rtte = FALSE,
  rtte_calc_diff = TRUE,
  rtte_conditional = TRUE,
  events = NULL,
  bins = FALSE,
  n_bins = 10,
  software = "auto",
  obs_cols = NULL,
  sim_cols = NULL,
  kmmc = NULL,
  reverse_prob = FALSE,
  stratify = NULL,
  stratify_color = NULL,
  ci = c(0.05, 0.95),
  plot = FALSE,
  xlab = "Time",
  ylab = "Survival (%)",
  show = NULL,
  as_percentage = TRUE,
  title = NULL,
  smooth = FALSE,
  vpc_theme = NULL,
  facet = "wrap",
  labeller = NULL,
  verbose = FALSE,
  vpcdb = FALSE
)

Arguments

sim

a data.frame with observed data, containing the independent and dependent variable, a column indicating the individual, and possibly covariates. E.g. load in from NONMEM using read_table_nm

obs

a data.frame with observed data, containing the independent and dependent variable, a column indicating the individual, and possibly covariates. E.g. load in from NONMEM using read_table_nm

psn_folder

instead of specifying "sim" and "obs", specify a PsN-generated VPC-folder

rtte

repeated time-to-event data? Default is FALSE (treat as single-event TTE)

rtte_calc_diff

recalculate time (T/F)? When simulating in NONMEM, you will probably need to set this to TRUE to recalculate the TIME to relative times between events (unless you output the time difference between events and specify that as independent variable to the vpc_tte() function.

rtte_conditional

'TRUE' (default) or 'FALSE'. Compute the probability for each event newly ('TRUE'), or calculate the absolute probability ('FALSE', i.e. the "probability of a 1st, 2nd, 3rd event etc" rather than the "probability of an event happening").

events

numeric vector describing which events to show a VPC for when repeated TTE data, e.g. c(1:4). Default is NULL, which shows all events.

bins

either "density", "time", or "data", or a numeric vector specifying the bin separators.

n_bins

number of bins

software

name of software platform using (e.g. nonmem, phoenix)

obs_cols

observation dataset column names (list elements: "dv", "idv", "id", "pred")

sim_cols

simulation dataset column names (list elements: "dv", "idv", "id", "pred", "sim")

kmmc

either NULL (for regular TTE vpc, default), or a variable name for a KMMC plot (e.g. "WT")

reverse_prob

reverse the probability scale (i.e. plot 1-probability)

stratify

character vector of stratification variables. Only 1 or 2 stratification variables can be supplied.

stratify_color

character vector of stratification variables. Only 1 stratification variable can be supplied, cannot be used in conjunction with 'stratify'.

ci

confidence interval to plot. Default is (0.05, 0.95)

plot

Boolean indicating whether to plot the ggplot2 object after creation. Default is FALSE.

xlab

label for x-axis

ylab

label for y-axis

show

what to show in VPC (obs_ci, obs_median, sim_median, sim_median_ci)

as_percentage

Show y-scale from 0-100 percent? TRUE by default, if FALSE then scale from 0-1.

title

title

smooth

"smooth" the VPC (connect bin midpoints) or show bins as rectangular boxes. Default is TRUE.

vpc_theme

theme to be used in VPC. Expects list of class vpc_theme created with function vpc_theme()

facet

either "wrap", "columns", or "rows"

labeller

ggplot2 labeller function to be passed to underlying ggplot object

verbose

TRUE or FALSE (default)

vpcdb

Boolean whether to return the underlying vpcdb rather than the plot

Details

Creates a VPC plot from observed and simulation survival data

Value

a list containing calculated VPC information, and a ggplot2 object

See Also

sim_data, vpc, vpc_tte, vpc_cens

Examples

## See vpc-docs.ronkeizer.com for more documentation and examples.

## Example for repeated) time-to-event data
## with NONMEM-like data (e.g. simulated using a dense grid)

data(rtte_obs_nm)
data(rtte_sim_nm)

# treat RTTE as TTE, no stratification
vpc_tte(sim = rtte_sim_nm[rtte_sim_nm$sim <= 20,],
       obs = rtte_obs_nm,
       rtte = FALSE,
       sim_cols=list(dv = "dv", idv = "t"), obs_cols=list(idv = "t"))