survival (version 3.5-3)

pseudo: Pseudo values for survival.

Description

Produce pseudo values from a survival curve.

Usage

pseudo(fit, times, type, addNA=TRUE, data.frame=FALSE, minus1=FALSE, ...)

Value

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. (If there are multiple rows for a given id, there is only one pseudovalue per unique id.)

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)'.

Arguments

fit

a survfit object, or one that inherits that class.

times

a vector of time points, at which to evaluate the pseudo values.

type

the type of value, either the probabilty in state pstate, the cumulative hazard cumhaz or the expected sojourn time in the state sojourn.

addNA

If any observations were removed due to missing values in the fit object, add those rows (as NA) into the return. This causes the result of pseudo to match the original dataframe.

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 residuals.survfit function, which does the majority of the work, e.g., collapse and weighted.

Details

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 and rmts are equivalent to sojourn. All of these are case insensitive.

The result from this routine is simply n times the IJ value, where n is the number of subjects. (If the the survfit call included and id option, n is the number of unique id values, otherwise the number of rows in the data set.) IJ values are well defined for all variants of the Aalen-Johansen estimate, as computed by the survfit function; indeed, they are the basis for standard errors of the result. Understanding of the properties of the pseudo-values, however, is still evolving. Validity has been shown for the simplest case (Kaplan-Meier), for competing risks, and for the corresponding sojourn times. On the other hand, one must be careful when the data includes left-truncation (P. K. Andersen, personal communication), and also with pseudo-values for the cumulative hazard. As understanding evolves, treat this routine's results as a reseach tool, not production, for the more complex models.

References

PK Andersen and M Pohar-Perme, Pseudo-observations in surivival analysis, Stat Methods Medical Res, 2010; 19:71-99

See Also

residuals.survfit

Examples

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)

# Restricted Mean Time in State (RMST) 
rms <- pseudo(fit1, times= 730, type='RMST') # 2 years
rfit <- lm(rms ~ ph.ecog + sex, data=lung)
rhat <- predict(rfit, newdata=expand.grid(ph.ecog=0:3, sex=1:2), se.fit=TRUE)
# print it out nicely
temp1 <- cbind(matrix(rhat$fit, 4,2))
temp2 <- cbind(matrix(rhat$se.fit, 4, 2))
temp3 <- cbind(temp1[,1], temp2[,1], temp1[,2], temp2[,2])
dimnames(temp3) <- list(paste("ph.ecog", 0:3), 
                        c("Male RMST", "(se)", "Female RMST", "(se)"))

round(temp3, 1)
# compare this to the fully non-parametric estimate
fit2 <- survfit(Surv(time, status) ~ ph.ecog, data=lung)
print(fit2, rmean=730)
# the estimate for ph.ecog=3 is very unstable (n=1), pseudovalues smooth it.
#
# In all the above we should be using the robust variance, e.g., svyglm, but
#  a recommended package can't depend on external libraries.
# See the vignette for a more complete exposition.