addLMtime: Add landmarking time interactions to a super dataset

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

Add landmarking time interactions to a super dataset

Description

Add landmarking time interactions to a super dataset

Usage

addLMtime(LMdata, LMcovars, func_covars, func_LMs, LM_col = "LM", keep = T)

Arguments

LMdata	An object of class "LMdataframe". This can be created by running cutLMsuper, or creating a stacked data set and storing it in a list with attributes outcome, w and end_time (see cutLMsuper for further description of outcome and w), end_time is the largest landmarking time.
LMcovars	Vector of strings indicating the columns that are to have a LM interaction
func_covars	A list of functions to use for interactions between LMs and covariates.
func_LMs	A list of functions to use for transformations of the landmark times.
LM_col	Character string specifying the column name that indicates the landmark time point for a row.
keep	Boolean value to indicate whether or not to keep the columns given by LMcovars without the time interactions or not. Default=FALSE.

Details

For each variable "var" in LMcovars, new columns var_1,...,var_i (length(func_covars) == i) are added; one column for each interaction given in func_covars is added Transformations of the LM column are added and labelled as LM_1,...,LM_j (length(func_LMs) == j); one column for each interaction given in func_LMs is added

Value

An object of class "LMdataframe" which now also contains LM time-interactions. The object has the following components:

• w, outcome: as the input (obtained from LMdata)

• func_covars: as the input

• func_LMs: as the input

• LMcovars: as the input

• allLMcovars: a list of the new columns added

• LM_col: as the input

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)
# Update complex LM-varying covariates
LMdata$LMdata$age <- LMdata$LMdata$age.at.time.0 + LMdata$LMdata$LM/12 # age is in years and LM is in months
# Add LM-time interactions
LMdata <- addLMtime(LMdata, pred.covars, func.covars, func.LMs)

## End(Not run)

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