rmeanglm {eventglm} | R Documentation |
Generalized linear models for the restricted mean survival
Description
Using pseudo observations for the restricted mean, or the restricted mean
lifetime lost in the competing risks case, this function then runs a
generalized linear model to estimate associations of the restricted
mean/lifetime lost up to a particular time (specified by the time
argument) with covariates. The link function can be "identity" for estimating
differences in the restricted mean, "log" for estimating ratios, and any of
the other link functions supported by quasi.
Usage
rmeanglm(
formula,
time,
cause = 1,
link = "identity",
model.censoring = "independent",
formula.censoring = NULL,
ipcw.method = "binder",
data,
weights,
subset,
na.action,
offset,
control = list(...),
model = FALSE,
x = TRUE,
y = TRUE,
singular.ok = TRUE,
contrasts = NULL,
...
)
Arguments
formula |
A formula specifying the model. The left hand side must be a Surv object specifying a right censored survival or competing risks outcome. The status indicator, normally 0=alive, 1=dead. Other choices are TRUE/FALSE (TRUE = death) or 1/2 (2=death). For competing risks, the event variable will be a factor, whose first level is treated as censoring. The right hand side is the usual linear combination of covariates. |
time |
Numeric constant specifying the time up to which the restricted mean effect estimates are desired. |
cause |
Numeric or character constant specifying the cause indicator of interest. |
link |
Link function for the restricted mean regression model. |
model.censoring |
Type of model for the censoring distribution. Options are "stratified", which computes the pseudo-observations stratified on a set of categorical covariates, "aareg" for Aalen's additive hazards model, and "coxph" for Cox's proportional hazards model. With those options, we assume that the time to event and event indicator are conditionally independent of the censoring time, and that the censoring model is correctly specified. If "independent", we assume completely independent censoring, i.e., that the time to event and covariates are independent of the censoring time. the censoring time is independent of the covariates in the model. Can also be a custom function, see Details and the "Extending eventglm" vignette. |
formula.censoring |
A one sided formula (e.g., |
ipcw.method |
Which method to use for calculation of inverse probability of censoring weighted pseudo observations. "binder" the default, uses the number of observations as the denominator, while the "hajek" method uses the sum of the weights as the denominator. |
data |
Data frame in which all variables of formula can be interpreted. |
weights |
an optional vector of 'prior weights' to be used in the fitting process. Should be NULL or a numeric vector. |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
na.action |
a function which indicates what should happen when the data
contain |
offset |
this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector of length equal to the number of cases. One or more offset terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See model.offset. |
control |
a list of parameters for controlling the fitting process. This is passed to glm.control. |
model |
a logical value indicating whether model frame should be included as a component of the returned value. |
x |
logical value indicating whether the model matrix used in the fitting process should be returned as components of the returned value. |
y |
logical value indicating whether the response vector (pseudo-observations) used in the fitting process should be returned as components of the returned value. |
singular.ok |
logical; if FALSE a singular fit is an error. |
contrasts |
an optional list. See the contrasts.arg of model.matrix.default. |
... |
Other arguments passed to glm.fit |
Details
The argument "model.censoring" determines how the pseudo observations are calculated. This can be the name of a function or the function itself, which must have arguments "formula", "time", "cause", "data", "type", "formula.censoring", and "ipcw.method". If it is the name of a function, this code will look for a function with the prefix "pseudo_" first, to avoid clashes with related methods such as coxph. The function then must return a vector of pseudo observations, one for each subject in data which are used in subsequent calculations. For examples of the implementation, see the "pseudo-modules.R" file, or the vignette "Extending eventglm".
Value
A pseudoglm object, with its own methods for print, summary, and vcov. It inherits from glm, so predict and other glm methods are supported.
Examples
cumincipcw <- rmeanglm(Surv(etime, event) ~ age + sex,
time = 200, cause = "pcm", link = "identity",
model.censoring = "independent", data = mgus2)
# stratified on only the categorical covariate
cumincipcw2 <- rmeanglm(Surv(etime, event) ~ age + sex,
time = 200, cause = "pcm", link = "identity",
model.censoring = "stratified",
formula.censoring = ~ sex, data = mgus2)