pseudo.threedim: Function that computes 3-dim pseudo-observations

In recurrentpseudo: Creates Pseudo-Observations and Analysis for Recurrent Event Data

Function that computes 3-dim pseudo-observations

Description

This function computes 3-dimensional pseudo-observations of the marginal mean function (in the presence of terminal events) and cumulative incidences of death causes 1 and 2

Usage

pseudo.threedim(tstart, tstop, status, covar_names, id, tk, data, deathtype)

Arguments

tstart	Start time - expecting counting process notation
tstop	Stop time - expecting counting process notation
status	Status variable (0 = censoring, 1 = recurrent event, 2 = death)
covar_names	Vector containing names of covariates intended for further analysis
id	ID variable for subject
tk	Vector of time points to calculate pseudo-observations at
data	Data set which contains variables of interest
deathtype	Type of death (cause 1 or cause 2)

Value

An object of class pseudo.threedim.

• outdata contains the semi-wide version of the computed pseudo-observations (one row per time, tk, per id).

• outdata_long contains the long version of the computed pseudo-observations (one row per observation, several per id).

• indata contains the input data which the pseudo-observations are based on.

• ts vector with time points used for computation of pseudo-observations.

• k number of time points used for computation of pseudo-observations (length(ts)).

References

Furberg, J.K., Andersen, P.K., Korn, S. et al. Bivariate pseudo-observations for recurrent event analysis with terminal events. Lifetime Data Anal (2021). https://doi.org/10.1007/s10985-021-09533-5

Examples

# Example: Bladder cancer data from survival package
require(survival)

# Make a three level status variable
bladder1$status3 <- ifelse(bladder1$status %in% c(2, 3), 2, bladder1$status)

# Add one extra day for the two patients with start=stop=0
# subset(bladder1, stop <= start)
bladder1[bladder1$id == 1, "stop"] <- 1
bladder1[bladder1$id == 49, "stop"] <- 1

# Restrict the data to placebo and thiotepa
bladdersub <- subset(bladder1, treatment %in% c("placebo", "thiotepa"))

# Make treatment variable two-level factor
bladdersub$Z <- as.factor(ifelse(bladdersub$treatment == "placebo", 0, 1))
levels(bladdersub$Z) <- c("placebo", "thiotepa")
head(bladdersub)

# Add deathtype variable to bladder data
# Deathtype = 1 (bladder disease death), deathtype = 2 (other death reason)
bladdersub$deathtype <- with(bladdersub, ifelse(status == 2, 1, ifelse(status == 3, 2, 0)))
table(bladdersub$deathtype, bladdersub$status)

# Pseudo-observations
pseudo_bladder_3d <- pseudo.threedim(tstart = bladdersub$start,
                                     tstop = bladdersub$stop,
                                     status = bladdersub$status3,
                                     id = bladdersub$id,
                                     deathtype = bladdersub$deathtype,
                                     covar_names = "Z",
                                     tk = c(30),
                                     data = bladdersub)
pseudo_bladder_3d

# GEE fit
fit_bladder_3d <- pseudo.geefit(pseudodata = pseudo_bladder_3d,
                                covar_names = c("Z"))
fit_bladder_3d

recurrentpseudo documentation built on Sept. 19, 2022, 5:05 p.m.