Data Frame, Estimates, and Confidence Limits of a Result

The results of the estimation and testing functions have, besides their own class, the class "fastsurv". Its as.data.frame() method returns every estimate and test of a result as a row of a data frame with the same columns for all the functions, so that results can be stacked with rbind(). coef() returns the estimates and confint() their confidence limits.

Usage

# S3 method for class 'fastsurv'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

# S3 method for class 'fastsurv'
coef(object, ...)

# S3 method for class 'fastsurv'
confint(object, parm, level = NULL, ...)

Arguments

x, object: A result of an estimation or testing function, of class "fastsurv".
row.names: NULL or a character vector of row names.
optional: Ignored; the column names are fixed.
...: Further arguments, currently ignored.
parm: The estimates for which limits are returned, as terms or as positions among the estimates. All estimates by default.
level: NULL (default) or the confidence level at which the result was computed.

Value

as.data.frame() returns a data frame with the columns described in Details. coef() returns a named numeric vector, and confint() a matrix with one row per estimate and two columns labeled by the percentages of the limits, as stats::confint().

Details

The data frame has the columns term, estimate, std.error, conf.low, conf.high, statistic, and p.value, with one row for each quantity in the table below. The terms are the names of the elements of the result. The estimate, its standard error, and its confidence limits are on the same scale. The statistic and the p-value are those of the test that the function reports for that quantity, following its side, as printed by the print() method. Entries that the function does not compute are NA.

`survfit_fast`	`surv`
`survdiff_fast`	`logrank`, or the name of the weight (test only)
`coxph_fast`	`coef` (the log hazard ratio)
`rmst_fast`	`rmst`; or `rmst.ctrl`, `rmst.trt`, `diff`, `log.ratio`
`wmst_fast`	`wmst`; or `wmst.control`, `wmst.treatment`, `diff`
`medsurv_fast`	`median`; or `median.control`, `median.treatment`, `diff`
`milestone_fast`	`surv.control`, `surv.treatment`, `diff`
`maxcombo_fast`	`maxcombo` and the component statistics, such as `FH(0,1)` (test only)
`rmw_fast`	`rmw`, `logrank`, `mwlrt` (test only)
`wkm_fast`	`wdiff`
`ahsw_fast`	`ah.ctrl`, `ah.trt`, `log.rah`, `dah`
`ahr_fast`	`log.ahr`, `theta`

Ratios are reported on the log scale, on which their confidence intervals are symmetric and their tests are computed: coef of coxph_fast() is the log hazard ratio (as from coef() of survival::coxph() with the group variable as a factor), and log.ratio, log.rah, and log.ahr are the logarithms of the ratio of restricted mean survival times, the ratio of average hazards, and the average hazard ratio. Take exp() of the estimate and of the confidence limits for the ratio. rmst_fast() and ahsw_fast() do not store the standard error of a log ratio or of the difference of average hazards; it is recovered from the confidence limits, which are symmetric on these scales, as (conf.high - conf.low) / (2 q), where q is the normal quantile of the confidence level, and the statistic of log.rah and dah is the estimate divided by this standard error.

The rows of a test only carry the statistic: the value of survdiff_fast() (the Z-score when side = 1, the chi-square when side = 2), the max-combo and rMW statistics with their components, which have no p-value of their own. For milestone_fast(), the statistic of diff is that of method, which for "loglog" is on the complementary log-log scale; a standard error is given only for method = "wald", whose interval is symmetric. For ahr_fast(), theta is the treatment group's share of the total hazard with its standard error and the primary test of ahr_fast(), and log.ahr carries the equivalent test on the log scale.

coef() returns the estimate column named by term, and confint() the confidence limits as a matrix, for the rows that are estimates. Both give an error for a test only. The limits are those computed by the function at its conf.level; they cannot be recomputed at another level, so level must be NULL or equal to that conf.level.

Examples

library(survival)

fit <- coxph_fast(Surv(futime, fustat) ~ rx, data = ovarian)
as.data.frame(fit)
#>   term estimate std.error  conf.low conf.high statistic   p.value
#> 1 coef -0.59638 0.5868442 -1.746574 0.5538135 -1.016249 0.3095107
coef(fit)
#>     coef 
#> -0.59638 
exp(confint(fit))
#>          2.5 %   97.5 %
#> coef 0.1743704 1.739875

as.data.frame(rmst_fast(Surv(futime, fustat) ~ rx, data = ovarian,
                        tau = 600))
#>        term    estimate  std.error     conf.low   conf.high statistic
#> 1 rmst.ctrl 427.5384615         NA           NA          NA        NA
#> 2  rmst.trt 534.9059829         NA           NA          NA        NA
#> 3      diff 107.3675214 62.5917172 -15.30999001 230.0450327  1.715363
#> 4 log.ratio   0.2240467  0.1418789  -0.05403079   0.5021243  1.579141
#>      p.value
#> 1         NA
#> 2         NA
#> 3 0.08627868
#> 4 0.11430378

# Results of a loop (with the vector interface) stacked into a data frame
res <- lapply(1:3, function(s) {
  set.seed(s)
  i <- sample(nrow(ovarian), replace = TRUE)
  fit_s <- coxph_fast(ovarian$futime[i], ovarian$fustat[i], ovarian$rx[i],
                      control = 1)
  cbind(sim = s, as.data.frame(fit_s))
})
do.call(rbind, res)
#>   sim term   estimate std.error  conf.low conf.high  statistic    p.value
#> 1   1 coef -0.9047674 0.5449332 -1.972817 0.1632820 -1.6603272 0.09684865
#> 2   2 coef -0.1123616 0.6784779 -1.442154 1.2174305 -0.1656084 0.86846515
#> 3   3 coef -0.5031459 0.5086887 -1.500157 0.4938656 -0.9891037 0.32261240

Usage

Arguments

Value

Details

See also

Examples