pseudo.geefit {recurrentpseudo}

Function that makes GEE model fit for recurrent pseudo-observations

Description

This function fits a GEE model based on pseudo-observations of the marginal mean function, and the survival probability or cumulative incidences of two death causes as returned by pseudo.onedim() (marginal mean function), or pseudo.twodim() (marginal mean function and survival probability), or pseudo.threedim() (marginal mean function and cumulative incidences of death causes 1 and 2)

Usage

pseudo.geefit(pseudodata, covar_names)

Arguments

pseudodata

Data set containing pseudo-observations. Expecting output from pseudo.twodim()

covar_names

Vector with covariate names to be found in "pseudodata". E.g. covar_names = c("Z", "Z1")

Value

An object of class pseudo.geefit.

• xi contains the estimated model parameters

• sigma contains the estimated variance matrix corresponding to xi

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

# 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)

# Two-dimensional (bivariate pseudo-obs) model fit

# Computation of pseudo-observations
pseudo_bladder_2d <- pseudo.twodim(tstart = bladdersub$start,
                                   tstop = bladdersub$stop,
                                   status = bladdersub$status3,
                                   id = bladdersub$id,
                                   covar_names = "Z",
                                   tk = c(30),
                                   data = bladdersub)

# Data in wide format
head(pseudo_bladder_2d$outdata)

# Data in long format
head(pseudo_bladder_2d$outdata_long)

# GEE fit
fit_bladder_2d <- pseudo.geefit(pseudodata = pseudo_bladder_2d,
                                covar_names = c("Z"))
fit_bladder_2d