Pseudo values for survival.
Produce pseudo values from a survival curve.
pseudo(fit, times, type, addNA=TRUE, data.frame=FALSE, minus1=FALSE, ...)
fit |
a |
times |
a vector of time points, at which to evaluate the pseudo values. |
type |
the type of value, either the probabilty in state |
addNA |
If any observations were removed due to missing values
in the |
data.frame |
if TRUE, return the data in "long" form as a data.frame with id, time, and pseudo as variables. |
minus1 |
use n-1 as the multiplier rather than n |
.
... |
other arguments to the |
This function computes pseudo values based on a first order Taylor series, also known as the "infinitesimal jackknife" (IJ) or "dfbeta" residuals. To be completely correct these results could perhaps be called ‘IJ pseudo values’ or even pseudo psuedo-values. For moderate to large data, however, the resulting values will be almost identical, numerically, to the ordinary jackknife.
A primary advantage of this approach is computational speed. Other features, neither good nor bad, are that they will agree with robust standard errors of other survival package estimates, which are based on the IJ, and that the mean of the estimates, over subjects, is exactly the underlying survival estimate.
For the type variable, surv is an acceptable synonym for
pstate, and rmst, rmts are equivalent to sojourn.
All of these are case insensitive.
A vector, matrix, or array. The first dimension is always the number of
observations in fit object, in the same order as the original
data set (less any missing values that were removed when creating the
survfit object);
the second, if applicable, corresponds to fit$states, e.g.,
multi-state
survival, and the last dimension to the selected time points.
For the data.frame option, a data frame containing values for id,
time, and pseudo. If the original survfit call contained an
id statement, then the values in the id column will be
taken from that variable. If the id statement has a simple
form, e.g., id = patno, then the name of the id column will
be ‘patno’, otherwise it will be named ‘(id)’.
PK Andersen and M Pohar-Perme, Pseudo-observations in surivival analysis, Stat Methods Medical Res, 2010; 19:71-99
fit1 <- survfit(Surv(time, status) ~ 1, data=lung) yhat <- pseudo(fit1, times=c(365, 730)) dim(yhat) lfit <- lm(yhat[,1] ~ ph.ecog + age + sex, data=lung)
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