Calculate time variable for survival analysis

Clinical data often has separate columns for event dates and last follow-up dates. This function calculates the time variable to be used in time-to-event analysis (i.e. time to event or time to censor).

surv_time(
  .data,
  start_time,
  event,
  event_date,
  fu_date,
  surv_time = "surv_time",
  surv_end_time = "surv_end_time"
)

Arguments

.data	A tbl
start_time	A column of dates or datetimes to use as the starting point. This will often be date of diagnosis.
event	A column of numeric indicators (0,1) where 1 represents occurrence of the event.
event_date	A column of dates or datetimes corresponding to the event.
fu_date	A column of dates or datetimes corresponding to the last known follow-up date.
surv_time	The name of the output column representing survival time
surv_end_time	The name of the output column representing the last date to be used in survival. This will either be event_date or fu_date.

Examples

# Create some test data
set.seed(12215)

surv_data <- tibble::tibble(
  diagnosis_dt = sample(seq(as.Date("1985/01/01"), as.Date("1990/01/01"), by = "day"), 10),
  death = sample(c(0, 1), 10, replace = TRUE),
  fu_dt = as.Date("2009/11/13")
)

surv_data <- surv_data %>%
  dplyr::rowwise() %>%
  dplyr::mutate(death_dt = dplyr::if_else(death == 1,
       sample(seq(as.Date("2010/01/01"), as.Date("2019/01/01"), by = "day"), 1),
       as.Date(NA))) %>%
  dplyr::ungroup()

surv_data %>%
  surv_time(diagnosis_dt, death, death_dt, fu_dt)
#> # A tibble: 10 x 6
#>    diagnosis_dt death fu_dt      death_dt   surv_end_time surv_time
#>    <date>       <dbl> <date>     <date>     <date>            <dbl>
#>  1 1986-06-08       0 2009-11-13 NA         2009-11-13         23.4
#>  2 1988-01-05       0 2009-11-13 NA         2009-11-13         21.9
#>  3 1986-03-15       0 2009-11-13 NA         2009-11-13         23.7
#>  4 1985-05-22       0 2009-11-13 NA         2009-11-13         24.5
#>  5 1989-12-14       0 2009-11-13 NA         2009-11-13         19.9
#>  6 1988-05-01       0 2009-11-13 NA         2009-11-13         21.5
#>  7 1989-12-19       1 2009-11-13 2011-03-17 2011-03-17         21.2
#>  8 1989-08-26       1 2009-11-13 2017-04-26 2017-04-26         27.7
#>  9 1986-05-18       0 2009-11-13 NA         2009-11-13         23.5
#> 10 1986-04-20       1 2009-11-13 2012-09-22 2012-09-22         26.4