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,
seed = 1636,
# 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 0.7519414 0.922575 0.04352979
#> 2: ITT 60 1 0.8744359 0.8461448 0.902727 0.01443450
risk_comparison(model)
#> Followup A_x A_y Risk Ratio RR 95% LCI RR 95% UCI log(RR) SE
#> <num> <fctr> <fctr> <num> <num> <num> <num>
#> 1: 60 risk_0 risk_1 1.0444041 0.9648478 1.130520 0.04042492
#> 2: 60 risk_1 risk_0 0.9574838 0.8845486 1.036433 0.04042492
#> Risk Difference RD 95% LCI RD 95% UCI RD SE
#> <num> <num> <num> <num>
#> 1: 0.03717768 -0.0278918 0.1022472 0.03319932
#> 2: -0.03717768 -0.1022472 0.0278918 0.03319932ITT with 5 bootstrap samples and losses-to-followup
options <- SEQopts(km.curves = TRUE,
bootstrap = TRUE,
seed = 1636,
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, 11 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 0.05562653 0.01626710
#> 2: ITT 60 1 0.02614576 0 0.07255455 0.02367839
risk_comparison(model)
#> Followup A_x A_y Risk Ratio RR 95% LCI RR 95% UCI log(RR) SE
#> <num> <fctr> <fctr> <num> <num> <num> <num>
#> 1: 60 risk_0 risk_1 1.1011710 0.8154451 1.487013 0.1532658
#> 2: 60 risk_1 risk_0 0.9081242 0.6724890 1.226324 0.1532658
#> Risk Difference RD 95% LCI RD 95% UCI RD SE
#> <num> <num> <num> <num>
#> 1: 0.002402164 -0.01469810 0.01950243 0.008724784
#> 2: -0.002402164 -0.01950243 0.01469810 0.008724784ITT with 5 bootstrap samples and competing events
options <- SEQopts(km.curves = TRUE,
bootstrap = TRUE,
seed = 1636,
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 0.05072346 0.01472830
#> 2: ITT 60 1 0.02381601 0 0.06530060 0.02116599
risk_comparison(model)
#> Followup A_x A_y Risk Ratio RR 95% LCI RR 95% UCI log(RR) SE
#> <num> <fctr> <fctr> <num> <num> <num> <num>
#> 1: 60 inc_0 inc_1 1.0896524 0.8076389 1.470140 0.1528084
#> 2: 60 inc_1 inc_0 0.9177239 0.6802073 1.238177 0.1528084
#> Risk Difference RD 95% LCI RD 95% UCI RD SE
#> <num> <num> <num> <num>
#> 1: 0.001959489 -0.01330796 0.01722693 0.007789656
#> 2: -0.001959489 -0.01722693 0.01330796 0.007789656ITT 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,
seed = 1636,
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
#> 1.1241440 0.8026793 1.5743518ITT with 5 bootstrap samples and competing events in subgroups defined by sex
options <- SEQopts(km.curves = TRUE,
bootstrap = TRUE,
seed = 1636,
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, 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 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 0.1040693 0.03159803
#> 2: ITT 60 1 0.04911213 0 0.1479362 0.05042138
#>
#> $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 0.004561386 0.02697913 0.005718917
#> 2: ITT 60 1 0.01484521 0.003821687 0.02586873 0.005624349
risk_comparison(model)
#> $sex_0
#> Followup A_x A_y Risk Ratio RR 95% LCI RR 95% UCI log(RR) SE
#> <num> <fctr> <fctr> <num> <num> <num> <num>
#> 1: 60 inc_0 inc_1 1.1654977 0.4953457 2.742297 0.436563
#> 2: 60 inc_1 inc_0 0.8580026 0.3646578 2.018792 0.436563
#> Risk Difference RD 95% LCI RD 95% UCI RD SE
#> <num> <num> <num> <num>
#> 1: 0.006973797 -0.06046781 0.07441540 0.03440961
#> 2: -0.006973797 -0.07441540 0.06046781 0.03440961
#>
#> $sex_1
#> Followup A_x A_y Risk Ratio RR 95% LCI RR 95% UCI log(RR) SE
#> <num> <fctr> <fctr> <num> <num> <num> <num>
#> 1: 60 inc_0 inc_1 0.9413422 0.6122541 1.447316 0.2194731
#> 2: 60 inc_1 inc_0 1.0623130 0.6909341 1.633309 0.2194731
#> Risk Difference RD 95% LCI RD 95% UCI RD SE
#> <num> <num> <num> <num>
#> 1: -0.0009250492 -0.008838726 0.006988628 0.004037664
#> 2: 0.0009250492 -0.006988628 0.008838726 0.004037664