View source: R/counting_process.R
counting_process | R Documentation |
Produces a data frame 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 (time-to-event), at risk count, and count of events at the specified TTE sorted by stratum and TTE.
counting_process(x, arm)
x |
A data frame 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.
The output produced by counting_process()
produces a counting process
dataset grouped by stratum and sorted within stratum by increasing times
where events occur. The object is assigned the class "counting_process". It
also has the attribute "ratio", which is the ratio of the events in the
treatment arm compared to the control arm in the input time-to-event data. If
the input data was generated by sim_pw_surv()
, the ratio attribute is
simply obtained from the attribute of the same name from the input object.
Otherwise, the returned ratio is the empirical ratio of treatment to control
events.
A data frame grouped by stratum
and sorted within stratum by tte
.
It only includes 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:
event_total
: Total number of events
event_trt
: Total number of events at treatment group
n_risk_total
: Number of subjects at risk
n_risk_trt
: Number of subjects at risk in treatment group
s
: Left-continuous Kaplan-Meier 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 log-rank test under the null
hypothesis)
var_o_minus_e
: Variance of o_minus_e
under the same assumption.
# Example 1
x <- data.frame(
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 (Z-value and 1-sided p-value)
z <- sum(y$o_minus_e) / sqrt(sum(y$var_o_minus_e))
c(z, pnorm(z))
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