| Title: | Creates Pseudo-Observations and Analysis for Recurrent Event Data |
|---|---|
| Description: | Computation of one-, two- and three-dimensional pseudo-observations based on recurrent events and terminal events. Generalised linear models are fitted using generalised estimating equations. Technical details on the bivariate procedure can be found in "Bivariate pseudo-observations for recurrent event analysis with terminal events" (Furberg et al., 2021) <doi:10.1007/s10985-021-09533-5>. |
| Authors: | Julie Kjærulff Furberg [aut, cre]
|
| Maintainer: | Julie Kjærulff Furberg <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.0 |
| Built: | 2025-01-30 04:07:49 UTC |
| Source: | https://github.com/juliekfurberg/recurrentpseudo |
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)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# 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
This function computes univariate pseudo-observations of the marginal mean function (in the presence of terminal events)
pseudo.onedim(tstart, tstop, status, covar_names, id, tk, data)pseudo.onedim(tstart, tstop, status, covar_names, id, tk, data)
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 |
An object of class pseudo.onedim.
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)).
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
# 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) # Pseudo observations pseudo_bladder_1d <- pseudo.onedim(tstart = bladdersub$start, tstop = bladdersub$stop, status = bladdersub$status3, id = bladdersub$id, covar_names = "Z", tk = c(30), data = bladdersub) head(pseudo_bladder_1d$outdata) # GEE fit fit_bladder_1d <- pseudo.geefit(pseudodata = pseudo_bladder_1d, covar_names = c("Z")) fit_bladder_1d# Example: Bladder cancer data from survival package require(survival) # Make a three level status variable bladder1$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$Z) <- c("placebo", "thiotepa") head(bladdersub) # Pseudo observations pseudo_bladder_1d <- pseudo.onedim(tstart = bladdersub$start, tstop = bladdersub$stop, status = bladdersub$status3, id = bladdersub$id, covar_names = "Z", tk = c(30), data = bladdersub) head(pseudo_bladder_1d$outdata) # GEE fit fit_bladder_1d <- pseudo.geefit(pseudodata = pseudo_bladder_1d, covar_names = c("Z")) fit_bladder_1d
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
pseudo.threedim(tstart, tstop, status, covar_names, id, tk, data, deathtype)pseudo.threedim(tstart, tstop, status, covar_names, id, tk, data, deathtype)
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) |
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)).
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
# 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# 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
This function computes bivariate pseudo-observations of the marginal mean function (in the presence of terminal events) and the survival probability
pseudo.twodim(tstart, tstop, status, covar_names, id, tk, data)pseudo.twodim(tstart, tstop, status, covar_names, id, tk, data)
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 |
An object of class pseudo.twodim.
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)).
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
# 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) # 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) head(pseudo_bladder_2d$outdata) # GEE fit fit_bladder_2d <- pseudo.geefit(pseudodata = pseudo_bladder_2d, covar_names = c("Z")) fit_bladder_2d# Example: Bladder cancer data from survival package require(survival) # Make a three level status variable bladder1$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$Z) <- c("placebo", "thiotepa") head(bladdersub) # Pseudo observations pseudo_bladder_2d <- pseudo.twodim(tstart = bladdersub$start, tstop = bladdersub$stop, status = bladdersub$status3, id = bladdersub$id, covar_names = "Z", tk = c(30), data = bladdersub) head(pseudo_bladder_2d$outdata) # GEE fit fit_bladder_2d <- pseudo.geefit(pseudodata = pseudo_bladder_2d, covar_names = c("Z")) fit_bladder_2d