Extract information about non-parametric survival models

summaryKM(
  data,
  time_var,
  event_var,
  weight_var = "",
  strata_var,
  int_name,
  ref_name,
  types = c("survival", "cumhaz", "median", "rmst"),
  t = NULL,
  ci = FALSE,
  se = FALSE,
  ...
)

Arguments

data: A data frame containing individual patient data for the relevant time to event outcomes.
time_var: Name of time variable in 'data'. Variable must be numerical and >0.
event_var: Name of event variable in 'data'. Variable must be numerical and contain 1's to indicate an event and 0 to indicate a censor.
weight_var: Optional name of a variable in "data" containing case weights.
strata_var: Name of stratification variable in "data". This is usually the treatment variable and must be categorical. Not required if only one arm is being analyzed.
int_name: Character to indicate the name of the treatment of interest, must be a level of the "strata_var" column in "data", used for labelling the parameters.
ref_name: Character to indicate the name of the reference treatment, must be a level of the "strata_var" column in "data", used for labelling the parameters. Not required if only one arm is being analyzed.
types: A list of statistics to extract - options include "survival", "cumhaz", "median", and "rmst". For details see the vignette on descriptive analysis.
t: The time points to be used - this only controls the rmst statistic.
ci: Should a confidence interval be returned (TRUE or FALSE)
se: Should a standard error be returned (TRUE or FALSE)
...: Additional arguments passed to survfit

Value

A data frame containing the following values and similar to that returned by summaryPSM

Model - returned as "Kaplan Meier"
ModelF - an ordered factor of Model
Dist - returned as "Kaplan Meier"
DistF - an ordered factor of Dist
distr - returned as "km"
Strata - Either Intervention or Reference
StrataName - As specified by int_name and ref_name respectively.
type - as defined by the types parameter.
variable - "est", "lcl", "ucl", "se" respectively
time - either NA or the time the statistic is evaluated at
value - estimated value

Examples


require(dplyr)
require(ggplot2)
#> Loading required package: ggplot2

PFS_data <- sim_adtte(seed = 2020, rho = 0.6) %>%
filter(PARAMCD=="PFS") %>%
transmute(USUBJID,
            ARMCD,
            PFS_days = AVAL,
            PFS_event = 1- CNSR,
            wt = runif(500,0,1)
)

pfs_info <- summaryKM(
  data = PFS_data,
  time_var = "PFS_days",
  event_var = "PFS_event",
  strata_var = "ARMCD",
  int_name = "A",
  ref_name = "B",
  ci = TRUE,
  t = c(500, 700))

ggplot(data = filter(pfs_info, type == "survival", variable == "est"),
       aes(x = time, y = value, color = StrataName)) +
  geom_step() +
  geom_step(data = filter(pfs_info, type == "survival", variable == "lcl"), linetype = 2) +
  geom_step(data = filter(pfs_info, type == "survival", variable == "ucl"), linetype = 2) +
  geom_point(data = filter(pfs_info, type == "survival", variable == "censored")) +
  xlab("Time") +
  ylab("Survival") +
  ggtitle("KM estimates and 95% CI")


filter(pfs_info, type == "cumhaz", variable == "est") %>%
  ggplot(aes(x = time, y = value, color = StrataName)) +
  geom_step() +
  xlab("Time") +
  ylab("Cumulative hazard") 

 
filter(pfs_info, type == "median") %>%
  transmute(StrataName, variable, value)
#> # A tibble: 6 × 3
#>   StrataName variable value
#>   <chr>      <chr>    <dbl>
#> 1 A          est       168.
#> 2 A          lcl       151 
#> 3 A          ucl       186 
#> 4 B          est       324.
#> 5 B          lcl       283 
#> 6 B          ucl       388 
  
filter(pfs_info, type == "rmst") %>%
  transmute(StrataName, variable, time, value)
#> # A tibble: 4 × 4
#>   StrataName variable  time value
#>   <chr>      <chr>    <dbl> <dbl>
#> 1 A          est        500  199.
#> 2 A          est        700   NA 
#> 3 B          est        500  321.
#> 4 B          est        700  364.
 
# example with weights
 pfs_info_wt <- summaryKM(
   data = PFS_data,
   time_var = "PFS_days",
   event_var = "PFS_event",
   strata_var = "ARMCD",
   weight_var = "wt",
   int_name = "A",
   ref_name = "B",
   types = "survival"
   )
   
   ggplot(data = filter(pfs_info, type == "survival", variable == "est"),
          aes(x = time, y = value, color = StrataName)) +
     geom_step(aes(linetype = "Original")) +
     geom_step(data = filter(pfs_info_wt, type == "survival", variable == "est"), 
               aes(linetype = "Weighted")) +
     xlab("Time") +
     ylab("Survival") +
     ggtitle("KM estimates and 95% CI")