Log-rank test for Kaplan-Meier survival curves

Compare survival curves between groups using the log-rank test.

Usage

logrank_test(
  data,
  time,
  event,
  by,
  digits = 2,
  format = c("flextable", "gt", "tibble"),
  theme = c("minimal")
)

Arguments

data

A data.frame containing survival time, event status, and grouping variable.

time

Survival follow-up time. Quoted and bare names are accepted.

event

Event indicator. Quoted and bare names are accepted. Numeric 0/1, numeric 1/2, logical, character, and factor variables are accepted. For two-level character or factor variables, the second level is treated as the event.

by

Grouping variable used to compare survival curves. Quoted and bare names are accepted.

digits

Number of digits for the chi-square statistic and expected events.

format

Output format. One of "flextable" (default), "gt", or "tibble".

theme

Table styling preset.

Value

A list of class c("gtregression","logrank_test", ...) with elements:

table

A flextable, gt_tbl, or NULL when format = "tibble".

table_body

Tibble with group-level log-rank components and overall test statistics.

test

One-row tibble with chi-square statistic, degrees of freedom, and p-value.

fit

The survdiff object.

time,event,by,format,source

Metadata fields.

Details

The log-rank test compares the observed number of events with the expected number of events in each group under the null hypothesis that the survival curves are the same. It is a group comparison, not an effect-size estimate; use cox_reg() when a hazard ratio is needed.

Examples

lung_data <- data_lungcancer
lung_data$trt <- factor(lung_data$trt, levels = c(1, 2),
                        labels = c("Standard", "Test"))

logrank_test(
  data = lung_data,
  time = time,
  event = status,
  by = trt
)

logrank_test(
  data = lung_data,
  time = "time",
  event = "status",
  by = "trt",
  format = tibble
)
#> # A tibble: 2 × 7
#>   Group        N Observed Expected Chi.square    df p.value
#>   <chr>    <dbl>    <dbl>    <dbl>      <dbl> <int>   <dbl>
#> 1 Standard    69       64     64.5    0.00823     1   0.928
#> 2 Test        68       64     63.5    0.00823     1   0.928