View source: R/pseudo.geefit.R
pseudo.geefit | R Documentation |
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)
pseudo.geefit(pseudodata, covar_names)
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") |
An object of class pseudo.geefit
.
xi
contains the estimated model parameters
sigma
contains the estimated variance matrix corresponding to xi
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
# 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
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