rttright: Compute redistribute-to-the-right weights

Description

For many survival estimands, one approach is to redistribute each censored observation's weight to those other observations with a longer survival time (think of distributing an estate to the heirs). Then compute on the remaining, uncensored data.

Usage

rttright(formula, data, weights, subset, na.action, times, id, timefix = TRUE)

Value

a vector or matrix of weights, with one column for each requested time

Arguments

formula: a formula object, which must have a Surv object as the response on the left of the ~ operator and, if desired, terms separated by + operators on the right. Each unique combination of predictors will define a separate strata.
data: a data frame in which to interpret the variables named in the formula, subset and weights arguments.
weights: The weights must be nonnegative and it is strongly recommended that they be strictly positive, since zero weights are ambiguous, compared to use of the subset argument.
subset: expression saying that only a subset of the rows of the data should be used in the fit.
na.action: a missing-data filter function, applied to the model frame, after any subset argument has been used. Default is options()$na.action.
times: a vector of time points, for which to return updated weights. If missing, a time after the largest time in the data is assumed.
id: optional: if the data set has multiple rows per subject, a a variable containing the subect identifier of each row.
timefix: correct for possible round-off error

Details

The formula argument is treated exactly the same as in the survfit function.

Redistribution is recursive: redistribute the weight of the first censored observation to all those with longer time, which may include other censored observations. Then redistribute the next smallest and etc. up to the specified time value. After re-distributing the weight for a censored observation to other observations that are not censored, ordinary non-censored methods can often be applied. For example, redistribution of the weights, followed by computation of the weighted cumulative distribution function, reprises the Kaplan-Meier estimator.

A primary use of this routine is illustration of methods or exploration of new methods. Methods that use RTTR directly, such as the Brier score, will normally do these compuations internally.

Examples

Run this code

afit <- survfit(Surv(time, status) ~1, data=aml)
rwt  <- rttright(Surv(time, status) ~1, data=aml)

index <- order(aml$time)
cdf <- cumsum(rwt[index])  # weighted CDF
cdf <- cdf[!duplicated(aml$time[index], fromLast=TRUE)]  # remove duplicates
cbind(time=afit$time, KM= afit$surv, RTTR= 1-cdf)