Cut simulated trial data at a calendar date — cut_data_by

Censors follow-up at a specified calendar time and aggregates events per subject. Returns one row per subject randomized before the cut date, with the total number of observed events and follow-up times.

Usage

cut_data_by_date(data, cut_date, event_gap = 0, ...)

# Default S3 method
cut_data_by_date(data, cut_date, event_gap = 0, ...)

# S3 method for class 'nb_sim_data'
cut_data_by_date(data, cut_date, event_gap = 0, ...)

# S3 method for class 'nb_sim_seasonal'
cut_data_by_date(data, cut_date, event_gap = 0, ...)

Arguments

data: Data generated by nb_sim().
cut_date: Calendar time (relative to trial start) at which to censor follow-up.
event_gap: Gap duration after each event during which no new events are counted. Can be a numeric value (default 0) or a function returning a numeric value. The time at risk is reduced by the sum of these gaps (truncated by the cut date).
...: Additional arguments passed to methods.

Value

A data frame with one row per subject randomized prior to cut_date containing:

id: Subject identifier
treatment: Treatment group
enroll_time: Time of enrollment relative to trial start
tte: Time at risk (total follow-up minus event gap periods)
tte_total: Total follow-up time (calendar time, not adjusted for gaps)
events: Number of observed events

A data frame with one row per subject randomized prior to cut_date. This method stops with an error for unsupported classes.

A data frame with one row per subject randomized prior to cut_date. Includes total events and follow-up time within the cut window.

A data frame with one row per subject randomized prior to cut_date. Includes season and follow-up time within the cut window.

Methods (by class)

cut_data_by_date(default): Default method.
cut_data_by_date(nb_sim_data): Method for nb_sim data.
cut_data_by_date(nb_sim_seasonal): Method for nb_sim_seasonal data.

Examples

enroll_rate <- data.frame(rate = 20 / (5 / 12), duration = 5 / 12)
fail_rate <- data.frame(treatment = c("Control", "Experimental"), rate = c(0.5, 0.3))
dropout_rate <- data.frame(
  treatment = c("Control", "Experimental"),
  rate = c(0.1, 0.05), duration = c(100, 100)
)
sim <- nb_sim(enroll_rate, fail_rate, dropout_rate, max_followup = 2, n = 20)
cut_data_by_date(sim, cut_date = 1)
#>    id    treatment enroll_time       tte tte_total events
#> 1   1      Control  0.02287797 0.3536237 0.3536237      1
#> 2   2 Experimental  0.03279845 0.9672016 0.9672016      1
#> 3   3 Experimental  0.03964368 0.9603563 0.9603563      1
#> 4   4      Control  0.09054028 0.3479680 0.3479680      1
#> 5   5 Experimental  0.09165196 0.9083480 0.9083480      0
#> 6   6      Control  0.13854364 0.8614564 0.8614564      0
#> 7   7 Experimental  0.14631736 0.8536826 0.8536826      0
#> 8   8      Control  0.18163569 0.8183643 0.8183643      0
#> 9   9 Experimental  0.18628345 0.8137165 0.8137165      0
#> 10 10 Experimental  0.18773699 0.8122630 0.8122630      1
#> 11 11      Control  0.19327308 0.8067269 0.8067269      0
#> 12 12      Control  0.19373652 0.8062635 0.8062635      0
#> 13 13 Experimental  0.19677352 0.8032265 0.8032265      0
#> 14 14 Experimental  0.26004958 0.7399504 0.7399504      0
#> 15 15      Control  0.26358648 0.7364135 0.7364135      0
#> 16 16      Control  0.28121348 0.7187865 0.7187865      0
#> 17 17 Experimental  0.28548428 0.7145157 0.7145157      0
#> 18 18      Control  0.28922751 0.3071060 0.3071060      1
#> 19 19 Experimental  0.30002577 0.6999742 0.6999742      0
#> 20 20      Control  0.31402891 0.6859711 0.6859711      1