View source: R/counting_process.R
counting_process  R Documentation 
Produces a tibble that is sorted by stratum and time. Included in this is only the times at which one or more event occurs. The output dataset contains Stratum, tte (timetoevent), at risk count and count of events at the specified tte sorted by Stratum and tte.
counting_process(x, arm)
x 
a tibble with no missing values and contain variables

arm 
value in the input 
The function only considered two group situation.
The tie is handled by the Breslow's Method.
A tibble
grouped by Stratum
and sorted within strata by tte
.
Remain rows with at least one event in the population, at least one subject
is at risk in both treatment group and control group.
Other variables in this represent the following within each stratum at
each time at which one or more events are observed:
events
: Total number of events
n_event_tol
: Total number of events at treatment group
n_risk_tol
: Number of subjects at risk
n_risk_trt
: Number of subjects at risk in treatment group
S
: Leftcontinuous KaplanMeier survival estimate
o_minus_e
: In treatment group, observed number of events minus expected
number of events. The expected number of events is estimated by assuming
no treatment effect with hypergeometric distribution with parameters total
number of events, total number of events at treatment group and number of
events at a time. (Same assumption of logrank test under the null hypothesis)
var_o_minus_e
: variance of o_minus_e
under the same assumption.
library(dplyr)
library(tibble)
# example 1
x < tibble(Stratum = c(rep(1, 10),rep(2, 6)),
Treatment = rep(c(1, 1, 0, 0), 4),
tte = 1:16,
event= rep(c(0, 1), 8))
counting_process(x, arm = 1)
# example 2
x < sim_pw_surv(n = 400)
y < cut_data_by_event(x, 150) %>% counting_process(arm = "Experimental")
# weighted logrank test (Zvalue and 1sided pvalue)
z < sum(y$o_minus_e) / sqrt(sum(y$var_o_minus_e))
c(z, pnorm(z))
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