TTET01 Binary Outcomes Summary.
ttet01.RdTTET01 template may be used to summarize any binary outcome or response variable at
a single time point. Typical application for oncology
Usage
ttet01_main(
adam_db,
dataset = "adtte",
arm_var = "ARM",
ref_group = NULL,
summarize_event = TRUE,
perform_analysis = "unstrat",
strata = NULL,
...
)
ttet01_pre(adam_db, dataset = "adtte", ...)
ttet01_post(tlg, prune_0 = TRUE, ...)
ttet01Arguments
- adam_db
(
listofdata.frames) object containing theADaMdatasets- dataset
(
string) the name of a table in theadam_dbobject.- arm_var
(
string) variable used for column splitting- ref_group
(
string) The name of the reference group, the value should be identical to the values inarm_var, if not specified, it will by default use the first level or value ofarm_var.- summarize_event
(
flag) should the event description be displayed, default is TRUE- perform_analysis
(
string) option to display statistical comparisons using stratified analyses, or unstratified analyses, or both, e.g.c("unstrat", "strat"). Only unstratified will be displayed by default- strata
(
string) stratification factors, e.g.strata = c("STRATA1", "STRATA2"), by default as NULL- ...
Further arguments passed to
control_surv_time(),control_coxph(),control_survtp(), andsurv_timepoint(). For details, see the documentation intern. Commonly used arguments includepval_method,conf_level,conf_type,quantiles,ties,timepoint,method, etc.- tlg
(
TableTree,Listingorggplot) object typically produced by amainfunction.- prune_0
(
flag) remove 0 count rows
Functions
ttet01_main(): Main TLG functionttet01_pre(): Preprocessingttet01_post(): Postprocessing
Examples
library(dplyr)
library(dunlin)
proc_data <- log_filter(syn_data, PARAMCD == "PFS", "adtte")
run(ttet01, proc_data)
#> A: Drug X B: Placebo C: Combination
#> (N=134) (N=134) (N=132)
#> ————————————————————————————————————————————————————————————————————————————
#> Patients with event (%) 86 (64.2%) 90 (67.2%) 92 (69.7%)
#> Earliest contributing event
#> Death 38 (44.2%) 48 (53.3%) 41 (44.6%)
#> Disease Progression 48 (55.8%) 42 (46.7%) 51 (55.4%)
#> Patients without event (%) 48 (35.8%) 44 (32.8%) 40 (30.3%)
#> Time to Event (MONTHS)
#> Median 16.9 19.7 12.3
#> 95% CI (12.8, 19.0) (13.3, 25.2) (8.4, 14.9)
#> 25% and 75%-ile 7.7, 30.0 7.2, 34.1 4.5, 25.3
#> Range (censored) 0.3 to 65.9 0.4 to 83.0 1.0 to 81.8
#> Range (event) 0.5 to 85.8 0.1 to 58.7 0.2 to 77.8
#> Unstratified Analysis
#> p-value (log-rank) 0.4808 0.0429
#> Hazard Ratio 0.90 1.36
#> 95% CI (0.67, 1.21) (1.01, 1.82)
run(ttet01, proc_data,
summarize_event = FALSE, perform_analysis = c("unstrat", "strat"),
strata = c("STRATA1", "STRATA2"),
conf_type = "log-log",
time_point = c(6, 12),
method = "both"
)
#> A: Drug X B: Placebo C: Combination
#> (N=134) (N=134) (N=132)
#> ———————————————————————————————————————————————————————————————————————————————————
#> Patients with event (%) 86 (64.2%) 90 (67.2%) 92 (69.7%)
#> Patients without event (%) 48 (35.8%) 44 (32.8%) 40 (30.3%)
#> Time to Event (MONTHS)
#> Median 16.9 19.7 12.3
#> 95% CI (12.8, 19.0) (13.2, 25.2) (8.4, 14.9)
#> 25% and 75%-ile 7.7, 30.0 7.2, 34.1 4.5, 25.3
#> Range (censored) 0.3 to 65.9 0.4 to 83.0 1.0 to 81.8
#> Range (event) 0.5 to 85.8 0.1 to 58.7 0.2 to 77.8
#> Unstratified Analysis
#> p-value (log-rank) 0.4808 0.0429
#> Hazard Ratio 0.90 1.36
#> 95% CI (0.67, 1.21) (1.01, 1.82)
#> Stratified Analysis
#> p-value (log-rank) 0.8068 0.0252
#> Hazard Ratio 0.96 1.42
#> 95% CI (0.71, 1.31) (1.04, 1.92)
#> 6 MONTHS
#> Patients remaining at risk 87 92 77
#> Event Free Rate (%) 77.27 77.85 67.70
#> 95% CI (68.76, 83.74) (69.53, 84.15) (58.64, 75.20)
#> Difference in Event Free Rate 0.57 -9.57
#> 95% CI (-9.82, 10.97) (-20.70, 1.57)
#> p-value (Z-test) 0.9137 0.0921
#> 12 MONTHS
#> Patients remaining at risk 65 68 51
#> Event Free Rate (%) 62.83 62.69 50.16
#> 95% CI (53.36, 70.91) (53.31, 70.71) (40.69, 58.89)
#> Difference in Event Free Rate -0.14 -12.68
#> 95% CI (-12.53, 12.25) (-25.38, 0.03)
#> p-value (Z-test) 0.9822 0.0505