# survexp.fit: Compute Expected Survival In survival: Survival Analysis

## Description

Compute expected survival times.

## Usage

 `1` ```survexp.fit(group, x, y, times, death, ratetable) ```

## Arguments

 `group` if there are multiple survival curves this identifies the group, otherwise it is a constant. Must be an integer. `x` A matrix whose columns match the dimensions of the `ratetable`, in the correct order. `y` the follow up time for each subject. `times` the vector of times at which a result will be computed. `death` a logical value, if `TRUE` the conditional survival is computed, if `FALSE` the cohort survival is computed. See `survexp` for more details. `ratetable` a rate table, such as `survexp.uswhite`.

## Details

For conditional survival `y` must be the time of last follow-up or death for each subject. For cohort survival it must be the potential censoring time for each subject, ignoring death.

For an exact estimate `times` should be a superset of `y`, so that each subject at risk is at risk for the entire sub-interval of time. For a large data set, however, this can use an inordinate amount of storage and/or compute time. If the `times` spacing is more coarse than this, an actuarial approximation is used which should, however, be extremely accurate as long as all of the returned values are > .99.

For a subgroup of size 1 and `times` > `y`, the conditional method reduces to exp(-h) where h is the expected cumulative hazard for the subject over his/her observation time. This is used to compute individual expected survival.

## Value

A list containing the number of subjects and the expected survival(s) at each time point. If there are multiple groups, these will be matrices with one column per group.

## Warning

Most users will call the higher level routine `survexp`. Consequently, this function has very few error checks on its input arguments.

`survexp`, `survexp.us`.