# km.rs

0th

Percentile

##### Kaplan-Meier and Reduced Sample Estimator using Histograms

Compute the Kaplan-Meier and Reduced Sample estimators of a survival time distribution function, using histogram techniques

Keywords
spatial
##### Usage
km.rs(o, cc, d, breaks)
##### Arguments
o
vector of observed survival times
cc
vector of censoring times
d
vector of non-censoring indicators
breaks
Vector of breakpoints to be used to form histograms.
##### Details

This function is needed mainly for internal use in spatstat, but may be useful in other applications where you want to form the Kaplan-Meier estimator from a huge dataset.

Suppose $T_i$ are the survival times of individuals $i=1,\ldots,M$ with unknown distribution function $F(t)$ which we wish to estimate. Suppose these times are right-censored by random censoring times $C_i$. Thus the observations consist of right-censored survival times $\tilde T_i = \min(T_i,C_i)$ and non-censoring indicators $D_i = 1{T_i \le C_i}$ for each $i$.

The arguments to this function are vectors o, cc, d of observed values of $\tilde T_i$, $C_i$ and $D_i$ respectively. The function computes histograms and forms the reduced-sample and Kaplan-Meier estimates of $F(t)$ by invoking the functions kaplan.meier and reduced.sample. This is efficient if the lengths of o, cc, d (i.e. the number of observations) is large.

The vectors km and hazard returned by kaplan.meier are (histogram approximations to) the Kaplan-Meier estimator of $F(t)$ and its hazard rate $\lambda(t)$. Specifically, km[k] is an estimate of F(breaks[k+1]), and lambda[k] is an estimate of the average of $\lambda(t)$ over the interval (breaks[k],breaks[k+1]). This approximation is exact only if the survival times are discrete and the histogram breaks are fine enough to ensure that each interval (breaks[k],breaks[k+1]) contains only one possible value of the survival time.

The vector rs is the reduced-sample estimator, rs[k] being the reduced sample estimate of F(breaks[k+1]). This value is exact, i.e. the use of histograms does not introduce any approximation error in the reduced-sample estimator.

##### Value

• A list with five elements
• rsReduced-sample estimate of the survival time c.d.f. $F(t)$
• kmKaplan-Meier estimate of the survival time c.d.f. $F(t)$
• hazardcorresponding Nelson-Aalen estimate of the hazard rate $\lambda(t)$
• rvalues of $t$ for which $F(t)$ is estimated
• breaksthe breakpoints vector

##### See Also

reduced.sample, kaplan.meier

##### Aliases
• km.rs
Documentation reproduced from package spatstat, version 1.5-5, License: GPL version 2 or newer

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