timeSplitter {Greg}

A function for splitting a time according to time periods

Description

If we have a violation of the cox proprtional hazards assumption we need to split an individual's followup time into several. See vignette("timeSplitter", package = "Greg") for a detailed description.

Usage

timeSplitter(
  data,
  by,
  time_var,
  event_var,
  event_start_status,
  time_related_vars,
  time_offset
)

Arguments

data

The dataset that you want to split according to the time_var option.

by

The time period that you want to split the dataset by. The size of the variable must be in proportion to the the time_var. The by variable can also be a vector for each time split, useful if the effect has large varyations over time.

time_var

The name of the main time variable in the dataset. This variable must be a numeric variable.

event_var

The event variable

event_start_status

The start status of the event status, e.g. "Alive"

time_related_vars

A dataset often contains other variabels that you want to update during the split, most commonly these are age or calendar time.

time_offset

If you want to skip the initial years you can offset the entire dataset by setting this variable. See detailed description below.

Details

Important note: The time variables must have the same time unit. I.e. function can not dedu if all variables are in years or if one happens to be in days.

Value

data.frame with the split data. The starting time for each period is named Start_time and the ending time is called Stop_time. Note that the resulting event_var will now contain the time-splitted eventvar.

The time_offset - details

Both time_var and other variables will be adjusted by the time_offset, e.g. if we the time scale is in years and we want to skip the first 4 years we set the time_offset = 4. In the outputted dataset the smallest time_var will be 0. Note: 0 will not be included as we generally want to look at those that survived the start date, e.g. if a patient dies on the 4-year mark we would not include him/her in our study.

Examples

test_data <- data.frame(
  id = 1:4,
  time = c(4, 3.5, 1, 5),
  event = c("alive", "censored", "dead", "dead"),
  age = c(62.2, 55.3, 73.7, 46.3),
  date = as.Date(
    c("2003-01-01", 
      "2010-04-01", 
      "2013-09-20",
      "2002-02-23")),
  stringsAsFactors = TRUE
)
timeSplitter(test_data, .5, 
             time_var = "time",
             time_related_vars = c("age", "date"),
             event_var = "event")