Here, we’ll go over some examples of using ITT. First we need to load the library before getting in to some sample use cases.
ITT With 5 bootstrap samples
options <- SEQopts(# tells SEQuential to create Kaplan-Meier curves
km.curves = TRUE,
# tells SEQuential to bootstrap
bootstrap = TRUE,
# tells SEQuential to run bootstraps 5 times
bootstrap.nboot = 5)
# use example data
data <- SEQdata
model <- SEQuential(data, id.col = "ID",
time.col = "time",
eligible.col = "eligible",
treatment.col = "tx_init",
outcome.col = "outcome",
time_varying.cols = c("N", "L", "P"),
fixed.cols = "sex",
method = "ITT",
options = options)
#>
#> Full dataset: 12,180 observations, 11 variables
#>
#> Non-required columns provided, pruning for efficiency
#>
#> Pruned
#>
#> Original dataset (eligible subjects): 9,203 observations, 9 variables
#>
#> Expanding Data...
#>
#> Pre-filter expansion: 310,080 observations
#>
#> Expanded dataset: 248,485 observations, 13 variables
#>
#> Expansion Successful
#>
#> Final analysis dataset: 248,485 observations, 13 variables
#>
#> Moving forward with ITT analysis
#>
#> Bootstrapping with 80% of 300 subjects (240 subjects, ~198,788 observations per resample) 5 times
#>
#> ITT model created successfully
#>
#> Creating Survival curves
#>
#> Completed
km_curve(model, plot.type = "risk") # retrieve risk plot
risk_data(model)
#> Index: <Followup>
#> Method Followup A Risk 95% LCI 95% UCI SE
#> <char> <num> <char> <num> <num> <num> <num>
#> 1: ITT 60 0 0.8372582 0.7646336 0.9098829 0.03705407
#> 2: ITT 60 1 0.8744359 0.8299662 0.9189056 0.02268902
risk_comparison(model)
#> Followup A_x A_y Risk Ratio RR 95% LCI RR 95% UCI Risk Differerence
#> <num> <fctr> <fctr> <num> <num> <num> <num>
#> 1: 60 risk_0 risk_1 1.0444041 0.9685600 1.126187 0.03717768
#> 2: 60 risk_1 risk_0 0.9574838 0.8879519 1.032461 -0.03717768
#> RD 95% LCI RD 95% UCI
#> <num> <num>
#> 1: -0.02814011 0.10249547
#> 2: -0.10249547 0.02814011ITT with 5 bootstrap samples and losses-to-followup
options <- SEQopts(km.curves = TRUE,
bootstrap = TRUE,
bootstrap.nboot = 5,
# tells SEQuential to expect LTFU as the censoring column
cense = "LTFU",
# tells SEQuential to treat this column as the
# censoring eligibility column
cense.eligible = "eligible_cense")
# use example data for LTFU
data <- SEQdata.LTFU
model <- SEQuential(data, id.col = "ID",
time.col = "time",
eligible.col = "eligible",
treatment.col = "tx_init",
outcome.col = "outcome",
time_varying.cols = c("N", "L", "P"),
fixed.cols = "sex",
method = "ITT",
options = options)
#>
#> Full dataset: 54,687 observations, 13 variables
#>
#> Non-required columns provided, pruning for efficiency
#>
#> Pruned
#>
#> Original dataset (eligible subjects): 29,624 observations, 10 variables
#>
#> Expanding Data...
#>
#> Pre-filter expansion: 1,609,859 observations
#>
#> Expanded dataset: 1,119,229 observations, 18 variables
#>
#> Expansion Successful
#>
#> Final analysis dataset: 1,119,229 observations, 18 variables
#>
#> Moving forward with ITT analysis
#>
#> Bootstrapping with 80% of 1,000 subjects (800 subjects, ~895,383 observations per resample) 5 times
#>
#> ITT model created successfully
#>
#> Creating Survival curves
#>
#> Completed
km_curve(model, plot.type = "risk")
risk_data(model)
#> Index: <Followup>
#> Method Followup A Risk 95% LCI 95% UCI SE
#> <char> <num> <char> <num> <num> <num> <num>
#> 1: ITT 60 0 0.02374360 0 0.05930117 0.01814195
#> 2: ITT 60 1 0.02614576 0 0.05307095 0.01373759
risk_comparison(model)
#> Followup A_x A_y Risk Ratio RR 95% LCI RR 95% UCI Risk Differerence
#> <num> <fctr> <fctr> <num> <num> <num> <num>
#> 1: 60 risk_0 risk_1 1.1011710 0.5047355 2.402402 0.002402164
#> 2: 60 risk_1 risk_0 0.9081242 0.4162501 1.981236 -0.002402164
#> RD 95% LCI RD 95% UCI
#> <num> <num>
#> 1: -0.009778824 0.014583152
#> 2: -0.014583152 0.009778824ITT with 5 bootstrap samples and competing events
options <- SEQopts(km.curves = TRUE,
bootstrap = TRUE,
bootstrap.nboot = 5,
# Using LTFU as our competing event
compevent = "LTFU")
data <- SEQdata.LTFU
model <- SEQuential(data, id.col = "ID",
time.col = "time",
eligible.col = "eligible",
treatment.col = "tx_init",
outcome.col = "outcome",
time_varying.cols = c("N", "L", "P"),
fixed.cols = "sex",
method = "ITT",
options = options)
#>
#> Full dataset: 54,687 observations, 13 variables
#>
#> Non-required columns provided, pruning for efficiency
#>
#> Pruned
#>
#> Original dataset (eligible subjects): 29,624 observations, 10 variables
#>
#> Expanding Data...
#>
#> Pre-filter expansion: 1,609,859 observations
#>
#> Expanded dataset: 1,119,229 observations, 14 variables
#>
#> Expansion Successful
#>
#> Final analysis dataset: 1,119,229 observations, 14 variables
#>
#> Moving forward with ITT analysis
#>
#> Bootstrapping with 80% of 1,000 subjects (800 subjects, ~895,383 observations per resample) 5 times
#>
#> ITT model created successfully
#>
#> Creating Survival curves
#>
#> Completed
km_curve(model, plot.type = "risk")
risk_data(model)
#> Index: <Followup>
#> Method Followup A Risk 95% LCI 95% UCI SE
#> <char> <num> <char> <num> <num> <num> <num>
#> 1: ITT 60 0 0.02185652 0 0.05286839 0.01582267
#> 2: ITT 60 1 0.02381601 0 0.04835280 0.01251900
risk_comparison(model)
#> Followup A_x A_y Risk Ratio RR 95% LCI RR 95% UCI Risk Differerence
#> <num> <fctr> <fctr> <num> <num> <num> <num>
#> 1: 60 inc_0 inc_1 1.0896524 0.5100804 2.327755 0.001959489
#> 2: 60 inc_1 inc_0 0.9177239 0.4295985 1.960475 -0.001959489
#> RD 95% LCI RD 95% UCI
#> <num> <num>
#> 1: -0.007920837 0.011839816
#> 2: -0.011839816 0.007920837ITT hazard ratio with 5 bootstrap samples and competing events
options <- SEQopts(# km.curves must be set to FALSE to turn on hazard
# ratio creation
km.curves = FALSE,
# set hazard to TRUE for hazard ratio creation
hazard = TRUE,
bootstrap = TRUE,
bootstrap.nboot = 5,
compevent = "LTFU")
data <- SEQdata.LTFU
model <- SEQuential(data, id.col = "ID",
time.col = "time",
eligible.col = "eligible",
treatment.col = "tx_init",
outcome.col = "outcome",
time_varying.cols = c("N", "L", "P"),
fixed.cols = "sex",
method = "ITT",
options = options)
#>
#> Full dataset: 54,687 observations, 13 variables
#>
#> Non-required columns provided, pruning for efficiency
#>
#> Pruned
#>
#> Original dataset (eligible subjects): 29,624 observations, 10 variables
#>
#> Expanding Data...
#>
#> Pre-filter expansion: 1,609,859 observations
#>
#> Expanded dataset: 1,119,229 observations, 14 variables
#>
#> Expansion Successful
#>
#> Final analysis dataset: 1,119,229 observations, 14 variables
#>
#> Moving forward with ITT analysis
#>
#> Bootstrapping with 80% of 1,000 subjects (800 subjects, ~895,383 observations per resample) 5 times
#>
#> Completed
# retrieve hazard ratios
hazard_ratio(model)
#> Hazard ratio LCI UCI
#> 1.0033697 0.5993046 1.6798651ITT with 5 bootstrap samples and competing events in subgroups defined by sex
options <- SEQopts(km.curves = TRUE,
bootstrap = TRUE,
bootstrap.nboot = 5,
compevent = "LTFU",
# define the subgroup
subgroup = "sex")
data <- SEQdata.LTFU
model <- SEQuential(data, id.col = "ID",
time.col = "time",
eligible.col = "eligible",
treatment.col = "tx_init",
outcome.col = "outcome",
time_varying.cols = c("N", "L", "P"),
fixed.cols = "sex",
method = "ITT",
options = options)
#>
#> Full dataset: 54,687 observations, 13 variables
#>
#> Non-required columns provided, pruning for efficiency
#>
#> Pruned
#>
#> Original dataset (eligible subjects): 29,624 observations, 11 variables
#>
#> Expanding Data...
#>
#> Pre-filter expansion: 1,609,859 observations
#>
#> Expanded dataset: 1,119,229 observations, 14 variables
#>
#> Expansion Successful
#>
#> Final analysis dataset: 1,119,229 observations, 14 variables
#>
#> Moving forward with ITT analysis
#>
#> Bootstrapping with 80% of 1,000 subjects (800 subjects, ~895,383 observations per resample) 5 times
#>
#> ITT model created successfully
#>
#> Creating Survival Curves for sex_0
#>
#> Creating Survival Curves for sex_1
#>
#> Completed
km_curve(model, plot.type = "risk")
#> $sex_0
#>
#> $sex_1
risk_data(model)
#> $sex_0
#> Index: <Followup>
#> Method Followup A Risk 95% LCI 95% UCI SE
#> <char> <num> <char> <num> <num> <num> <num>
#> 1: ITT 60 0 0.04213833 0 0.1185452 0.03898383
#> 2: ITT 60 1 0.04911213 0 0.1322803 0.04243352
#>
#> $sex_1
#> Index: <Followup>
#> Method Followup A Risk 95% LCI 95% UCI SE
#> <char> <num> <char> <num> <num> <num> <num>
#> 1: ITT 60 0 0.01577026 0.000000000 0.03706622 0.010865485
#> 2: ITT 60 1 0.01484521 0.003496357 0.02619406 0.005790336
risk_comparison(model)
#> $sex_0
#> Followup A_x A_y Risk Ratio RR 95% LCI RR 95% UCI Risk Differerence
#> <num> <fctr> <fctr> <num> <num> <num> <num>
#> 1: 60 inc_0 inc_1 1.1654977 2.006387e-08 67703041 0.006973797
#> 2: 60 inc_1 inc_0 0.8580026 1.477039e-08 49840838 -0.006973797
#> RD 95% LCI RD 95% UCI
#> <num> <num>
#> 1: -0.02096462 0.03491222
#> 2: -0.03491222 0.02096462
#>
#> $sex_1
#> Followup A_x A_y Risk Ratio RR 95% LCI RR 95% UCI Risk Differerence
#> <num> <fctr> <fctr> <num> <num> <num> <num>
#> 1: 60 inc_0 inc_1 0.9413422 0.4013910 2.207635 -0.0009250492
#> 2: 60 inc_1 inc_0 1.0623130 0.4529734 2.491336 0.0009250492
#> RD 95% LCI RD 95% UCI
#> <num> <num>
#> 1: -0.01668972 0.01483962
#> 2: -0.01483962 0.01668972