ci: *c*onfidence *i*ntervals for survival curves.

Description Usage Arguments Details Value Note Source References See Also Examples

View source: R/ci.R

Description

confidence intervals for survival curves.

Usage

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ci(x, ...)

## S3 method for class 'ten'
ci(x, ..., CI = c("0.95", "0.9", "0.99"), how = c("point",
  "nair", "hall"), trans = c("log", "lin", "asi"), tL = NULL, tU = NULL,
  reCalc = FALSE)

## S3 method for class 'stratTen'
ci(x, ..., CI = c("0.95", "0.9", "0.99"),
  how = c("point", "nair", "hall"), trans = c("log", "lin", "asi"),
  tL = NULL, tU = NULL)

Arguments

x

An object of class ten.

CI

Confidence intervals. As the function currently relies on lookup tables, currently only 90%, 95% (the default) and 99% are supported.

how

Method to use for confidence interval.
point (the default) uses pointwise confirence intervals.
The alternatives use confidence bands (see details).

trans

Transformation to use.
The default is trans="log".
Also supported are linear and arcsine-square root transformations.

tL

Lower time point. Used in construction of confidence bands.

tU

Upper time point. Used in construction of confidence bands.

...

Additional arguments (not implemented).

reCalc

Recalcuate the values?
If reCalc=FALSE (the default) and the ten object already has the calculated values stored as an attribute, the value of the attribute is returned directly.

Details

In the equations below

sigma^2(t) = V[S(t)]/[S(t)]^2

Where S(t) is the Kaplan-Meier survival estimate and V[S(t)] is Greenwood's estimate of its variance.
The pointwise confidence intervals are valid for individual times, e.g. median and quantile values. When plotted and joined for multiple points they tend to be narrower than the bands described below. Thus they tend to exaggerate the impression of certainty when used to plot confidence intervals for a time range. They should not be interpreted as giving the intervals within which the entire survival function lies.
For a given significance level alpha, they are calculated using the standard normal distribution Z as follows:

Confidence bands give the values within which the survival function falls within a range of timepoints.

The time range under consideration is given so that tL >= min(t), the minimum or lowest event time and tU <= max(t), the maximum or largest event time.
For a sample size n and 0 < a_l < a_u <1:

a_l = n*sigma^2(t_l) / [1+n*sigma^2(t_l)]

a_u = n*sigma^2(t_u) / [1+n*sigma^2(t_u)]

For the Nair or equal precision (EP) confidence bands, we begin by obtaining the relevant confidence coefficient c[alpha]. This is obtained from the upper a-th fractile of the random variable

U = sup{ |W(x)[x(1-x)]^0.5|, a_l <= x <= a_u}

Where W is a standard Brownian bridge.
The intervals are:

For the Hall-Wellner bands the confidence coefficient k[alpha] is obtained from the upper a-th fractile of a Brownian bridge.
In this case t_l can be =0.
The intervals are:

Value

The ten object is modified in place by the additional of a data.table as an attribute.
attr(x, "ci") is printed.
This A survfit object. The upper and lower elements in the list (representing confidence intervals) are modified from the original.
Other elements will also be shortened if the time range under consideration has been reduced from the original.

Note

Source

The function is loosely based on km.ci::km.ci.

References

Nair V, 1984. Confidence bands for survival functions with censored data: a comparative study. Technometrics. 26(3):265-75. JSTOR.

Hall WJ, Wellner JA, 1980. Confidence bands for a survival curve from censored data. Biometrika. 67(1):133-43. JSTOR.

See Also

sf

quantile

Examples

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## K&M 2nd ed. Section 4.3. Example 4.2, pg 105.
data("bmt", package="KMsurv")
b1 <- bmt[bmt$group==1, ] # ALL patients
## K&M 2nd ed. Section 4.4. Example 4.2 (cont.), pg 111.
## patients with ALL
t1 <- ten(Surv(t2, d3) ~ 1, data=bmt[bmt$group==1, ])
ci(t1, how="nair", trans="lin", tL=100, tU=600)
## Table 4.5, pg. 111.
lapply(list("lin", "log", "asi"),
       function(x) ci(t1, how="nair", trans=x, tL=100, tU=600))
## Table 4.6, pg. 111.
lapply(list("lin", "log", "asi"),
       function(x) ci(t1, how="hall", trans=x, tL=100, tU=600))
t1 <- ten(Surv(t2, d3) ~ group, data=bmt)
ci(t1, CI="0.95", how="nair", trans="lin", tL=100, tU=600)
## stratified model
data("pbc", package="survival")
t1 <- ten(coxph(Surv(time, status==2) ~ log(bili) + age + strata(edema), data=pbc))
ci(t1)

dardisco/survMisc documentation built on May 12, 2017, 5:37 p.m.