R: Compute Expected Survival

survexp {survival} | R Documentation

## Compute Expected Survival

### Description

Returns either the expected survival of a cohort of subjects, or the individual expected survival for each subject.

### Usage

    survexp(formula, data, weights, subset, na.action, rmap, times, cohort=TRUE,
            conditional=FALSE, ratetable=survexp.us, scale=1, npoints,
            se.fit, model=FALSE, x=FALSE, y=FALSE)

### Arguments

`formula` | formula object. The response variable is a vector of follow-up times and is optional. The predictors consist of optional grouping variables separated by the `+` operator (as in `survfit`), and is often `~1`, i.e., expected survival for the entire group.
`data` | data frame in which to interpret the variables named in the `formula`, `subset` and `weights` arguments.
`weights` | case weights.
`subset` | expression indicating a subset of the rows of `data` to be used in the fit.
`na.action` | function to filter missing data. This is applied to the model frame after `subset` has been applied. Default is `options()$na.action`. A possible value for `na.action` is `na.omit`, which deletes observations that contain one or more missing values.
`rmap` | an optional list that maps data set names to the ratetable names. See the details section below.
`times` | vector of follow-up times at which the resulting survival curve is evaluated. If absent, the result will be reported for each unique value of the vector of follow-up times supplied in `formula`.
`cohort` | logical value: if `FALSE`, each subject is treated as a subgroup of size 1. The default is `TRUE`.
`conditional` | logical value: if `TRUE`, the follow-up times supplied in `formula` are death times and conditional expected survival is computed. If `FALSE`, the follow-up times are potential censoring times. If follow-up times are missing in `formula`, this argument is ignored.
`ratetable` | a table of event rates, such as `survexp.uswhite`, or a fitted Cox model.
`scale` | numeric value to scale the results. If `ratetable` is in units/day, `scale = 365.25` causes the output to be reported in years.
`npoints` | number of points at which to calculate intermediate results, evenly spaced over the range of the follow-up times. The usual (exact) calculation is done at each unique follow-up time. For very large data sets specifying `npoints` can reduce the amount of memory and computation required. For a prediction from a Cox model `npoints` is ignored.
`se.fit` | compute the standard error of the predicted survival. The default is to compute standard errors whenever possible, which at this time is only for the Ederer method and a Cox model as the rate table.
`model,x,y` | flags to control what is returned. If any of these is true, then the model frame, the model matrix, and/or the vector of response times will be returned as components of the final result, with the same names as the flag arguments.

### Details

Individual expected survival is usually used in models or testing, to ‘correct’ for the age and sex composition of a group of subjects. For instance, assume that birth date, entry date into the study, sex and actual survival time are all known for a group of subjects. The `survexp.us` population tables contain expected death rates based on calendar year, sex and age. Then

      haz <- -log(survexp(fu.time ~ 1, data=mydata,
                          rmap = list(year=entry.dt, age=(birth.dt-entry.dt)),
                          cohort=FALSE))

gives for each subject the total hazard experienced up to their observed death time or last follow-up time (variable fu.time) This probability can be used as a rescaled time value in models:

    glm(status ~ 1 + offset(log(haz)), family=poisson)
    glm(status ~ x + offset(log(haz)), family=poisson)

In the first model, a test for intercept=0 is the one sample log-rank test of whether the observed group of subjects has equivalent survival to the baseline population. The second model tests for an effect of variable `x` after adjustment for age and sex. The ratetable being used may have different variable names than the user's data set, this is dealt with by the `rmap` argument. The rate table for the above calculation was `survexp.us`, a call to `summary{survexp.us}` reveals that it expects to have variables `age` = age in days, `sex`, and `year` = the date of study entry, we create them in the `rmap` line. The sex variable is not mapped, therefore the code assumes that it exists in `mydata` in the correct format. (Note: for factors such as sex, the program will match on any unique abbreviation, ignoring case.) Cohort survival is used to produce an overall survival curve. This is then added to the Kaplan-Meier plot of the study group for visual comparison between these subjects and the population at large. There are three common methods of computing cohort survival. In the "exact method" of Ederer the cohort is not censored; this corresponds to having no response variable in the formula. Hakulinen recommends censoring the cohort at the anticipated censoring time of each patient, and Verheul recommends censoring the cohort at the actual observation time of each patient. The last of these is the conditional method. These are obtained by using the respective time values as the follow-up time or response in the formula.

### Value

if `cohort=TRUE` an object of class `survexp`, otherwise a vector of per-subject expected survival values. The former contains the number of subjects at risk and the expected survival for the cohort at each requested time.

### References

Berry, G. (1983). The analysis of mortality by the subject-years method. Biometrics, 39:173-84.

Ederer, F., Axtell, L. and Cutler, S. (1961). The relative survival rate: a statistical methodology. Natl Cancer Inst Monogr, 6:101-21.

Hakulinen, T. (1982). Cancer survival corrected for heterogeneity in patient withdrawal. Biometrics, 38:933-942.

Verheul, H., Dekker, E., Bossuyt, P., Moulijn, A. and Dunning, A. (1993). Background mortality in clinical survival studies. Lancet, 341: 872-875.

### See Also

`survfit`, `pyears`, `survexp.us`, `survexp.fit`.

### Examples

    #
    # Stanford heart transplant data
    #  We don't have sex in the data set, but know it to be nearly all males.
    # Estimate of conditional survival
    survexp(futime ~ 1, rmap=list(sex="male", year=accept.dt,
              age=(accept.dt-birth.dt)), conditional=TRUE, data=jasa)
    # Estimate of expected  survival stratified by prior surgery
    survexp(futime ~ surgery, rmap= list(sex="male", year=accept.dt,
    	age=(accept.dt-birth.dt)), conditional=TRUE, data=jasa)

    ## Compare the survival curves for the Mayo PBC data to Cox model fit
    ##
    pfit <-coxph(Surv(time,status>0) ~ trt + log(bili) + log(protime) + age +
                    platelet, data=pbc)
    plot(survfit(Surv(time, status>0) ~ trt, data=pbc))
    lines(survexp( ~ trt, ratetable=pfit, data=pbc), col='purple')

[Package survival version 2.36-14 Index]