recur_survival_table {card}

Recurrent Survival Data Format

Description

Reformats recurrent event data (wide) into different models for survival analysis, but can also be used for simple survival analysis tables as well. Importantly, for large datasets, this function will show significant slow-down since it uses an intuitive approach on defining the datasets. Future iterations will create a vectorized approach that should provide performance speed-ups.

• For recurrent events, the final censoring event can include death, or can be ignored if its not considered a failure event.

• For simple survival analysis, death censoring should be left as NULL, and the event (e.g. "date_of_death"), should be used as a single event.dates parameter. The function will do the rest.

Usage

recur_survival_table(
  data,
  id,
  first,
  last,
  event.dates,
  model.type,
  death = NULL
)

Arguments

data

A dataframe containing the subsequent parameters

id

Column in dataframe that contains unique IDs for each row

first

Column with left/enrollment dates

last

Column with right/censoring time point, or last contact

event.dates

Vector of columns that contain event dates

model.type

Character/string = c("marginal", "pwptt", "pwpgt")

death

Column created for if death is known (0 or 1), original dataframe (e.g. can add column of zeroes PRN). Death defaults to null for intermediate calculations otherwise.

Details

This function takes every data event date, and creates several types of recurrent event tables. It orders the data chronologically for repeat events. Currently does marginal and conditional A and B models. The large

Value

A data frame organized into a survival table format

Examples

# Data
data("mims")

# Parameters
id <- "patid"
first <- "first_visit_date_bl"
last <- "ldka"
event.dates <- c("mi_date_1", "mi_date_2", "mi_date_3")
model.type <- "marginal"
death <- "DEATH_CV_YN"

# Run analysis
tbl <- recur_survival_table(
  mims, id, first, last, event.dates, model.type, death
)