survfit(formula, data=sys.parent(), weights, subset, na.action,
        newdata, individual=F, conf.int=.95, se.fit=T,
        type=<<see below>>, error=<<see below>>, conf.type="log",
        conf.lower="usual")

formula:

A formula object or a coxph object. If a formula object is supplied it must have a Surv object as the response on the left of the ~ operator and, if desired, terms separated by + operators on the right. One of the terms may be a strata object. For a single survival curve the "~ 1" part of the formula is not required.

data:

a data frame in which to interpret the variables named in the formula, or in the subset and the weights argument.

weights:

case weights, they 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.

newdata:

a data frame with the same variable names as those that appear in the coxph formula. Only applicable when formula is a coxph object. The curve(s) produced will be representative of a cohort who's covariates correspond to the values in newdata. Default is the mean of the covariates used in the coxph fit.

individual:

a logical value, if TRUE, the data frame represents different time epochs for only one individual. If FALSE, multiple rows indicate multiple individuals. If TRUE, only one curve will be produced, if FALSE, there will be one curve per row in newdata. Only applicable when formula is a coxph object.

conf.int:

The level for a two-sided confidence interval on the survival curve(s). Default is 0.95.

se.fit:

a logical value indicating whether standard errors should be computed. Default is true.

type:

a character string specifying the type of survival curve. Possible values are "kaplan-meier", "fleming-harrington" or "fh2", (only the first two characters are necessary). The default is "fleming-harrington" when a coxph object is given, and it is "kaplan-meier" otherwise.

error:

a character string specifying the error. Possible values are "greenwood" for the Greenwood formula or "tsiatis" for the Tsiatis formula, (only the first character is necessary). The default is "tsiatis" when a coxph object is given, and it is "greenwood" otherwise.

conf.type:

a character string specifying the the confidence interval type. Possible values are: "none" for no confidence intervals, "plain" for standard intervals, "curve +- k *se(curve)", where k is determined from conf.int, "log" for intervals based on the cumulative hazard or log(survival) (the default), and "log-log" for intervals based on the log hazard or log(-log(survival)). The last type will never extend past 0 or 1. Only enough of the string to uniquely identify it is necessary.

conf.lower:

a character string to specify modified lower limits to the curve, the upper limit remains unchanged. Possible values are "usual", "peto", and "modified". The modified lower limit is based on an "effective n" argument. The confidence bands will agree with the usual calculation at each death time, but unlike the usual bands the confidence interval becomes wider at each censored observation. The extra width is obtained by multiplying the usual variance by a factor m/n, where n is the number currently at risk and m is the number at risk at the last death time. (The bands thus agree with the un-modified bands at each death time.) This is especially useful for survival curves with a long flat tail.

The Peto lower limit is based on the same "effective n" argument as the modified limit, but also replaces the usual Greenwood variance term with a simple approximation. It is known to be conservative.

The Greenwood formula for the variance is a sum of terms d/(n*(n-m)), where d is the number of deaths at a given time point, n is the sum of wt for all individuals still at risk at that time, and m is the sum of weights for the deaths at that time. The justification is based on a binomial argument when weights are all equal to one; extension to the weighted case is ad hoc. Tsiatis (1981) proposes a sum of terms d/(n*n), based on a counting process argument which includes the weighted case.

For the Fleming-Harrington estimate, two different methods for handling ties have been implemented. If there were 3 deaths out of 10 at risk, then the method due to Fleming and Harrington increments the hazard by 3/10 while the Nelson estimate increments the hazard by 1/10 + 1/9 + 1/8. For curves created after a Cox model these correspond to the Breslow and Efron estimates, respectively, and the proper choice is made automatically. The fh2 method will give results closer to the Kaplan-Meier.

Based on the work of Link (1984), the log transform is expected to produce the most accurate confidence intervals. If there is heavy censoring, then based on the work of Dorey and Korn (1987) the modified estimate will give a more reliable confidence band for the tails of the curve.

Fleming, T. H. and Harrington, D. P. (1984). Nonparametric estimation of the survival distribution in censored data. Comm. in Statistics 13, 2469-86.

Kablfleisch, J. D. and Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data. New York:Wiley.

Link, C. L. (1984). Confidence intervals for the survival function using Cox's proportional hazards model with covariates. Biometrics 40, 601-610.

Tsiatis, A. (1981). A large sample study of the estimate for the integrated hazard function in Cox's regression model for survival data. Annals of Statistics 9, 93-108.

# fit a Kaplan-Meier and plot it
fit <- survfit(Surv(time, status) ~ group, data = leukemia)
plot(fit, lty = 2:3)
legend(100, .8, c("Maintained", "Nonmaintained"), lty = 2:3)
# fit a Cox proportional hazards model and plot the
# predicted survival curve at the average predictor
fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian)
plot(survfit(fit), xlab = "Survival in Days")