cutLMsuper: Build a stacked super dataset from original dataset (wide or...

In anyafries/dynLM: Dynamic w-year risk predictions from landmark time points

Build a stacked super dataset from original dataset (wide or long format)

Description

Build a stacked super dataset from original dataset (wide or long format)

Usage

cutLMsuper(
  data,
  outcome,
  LMs,
  w,
  covs,
  format = c("wide", "long"),
  id,
  rtime,
  right = T
)

Arguments

data	Data frame from which to construct landmark super dataset
outcome	List with items time and status, containing character strings identifying the names of time and status variables, respectively, of the survival outcome
LMs	vector, the value of the landmark time points (points at which prediction is made)
w	Scalar, the value of the prediction window (ie predict w-year/other time period risk from the LM points)
covs	List with items fixed and varying, containing character strings specifying column names in the data containing time-fixed and time-varying covariates, respectively
format	Character string specifying whether the original data are in wide (default) or in long format
id	Character string specifying the column name in data containing the subject id; only needed if format="long"
rtime	Character string specifying the column name in data containing the (running) time variable associated; only needed if format="long"
right	Boolean (default=TRUE), indicating if the intervals for the time-varying covariates are closed on the right (and open on the left) or vice versa, see cut

Details

This function calls cutLM from the library dynpred, more documentation can be found there. Note that for every landmark tLM given in LMs, there must be at least one patient alive after tLM.

Value

An object of class "LM.data.frame". This the following components:

• LMdata: containing the stacked data set, i.e., the outcome and the values of time-fixed and time-varying covariates taken at the landmark time points. The value of the landmark time point is stored in column LM.

• outcome: same as input

• w: same as input

• end_time: final landmarking point used in training

Examples

## Not run: 
data(relapse)
outcome = list(time="Time", status="event")
covars = list(fixed=c("ID","age.at.time.0","male","stage","bmi"),
              varying=c("treatment"))
w = 60; LMs = c(0,12,24)
# Covariate-landmark time interactions
func.covars <- list( function(t) t, function(t) t^2)
# let hazard depend on landmark time
func.LMs <- list( function(t) t, function(t) t^2)
# Choose covariates that will have time interaction
pred.covars <- c("age","male","stage","bmi","treatment")
# Stack landmark datasets
LMdata <- cutLMsuper(relapse, outcome, LMs, w, covs, format="long", id="ID", rtime="fup_time", right=F)

## End(Not run)

anyafries/dynLM documentation built on July 26, 2022, 12:17 a.m.