Here, we’ll go over some examples of using dose-response. First we need to load the library before getting in to some sample use cases.
Currently, dose-response analysis through SEQuential only supports binary treatment values. Therefore; running multinomial models will lead to errors.
Dose-response 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 = "dose-response",
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, 15 variables
#>
#> Expansion Successful
#>
#> Final analysis dataset: 248,485 observations, 15 variables
#>
#> Moving forward with dose-response analysis
#>
#> Bootstrapping with 80% of 300 subjects (240 subjects, ~198,788 observations per resample) 5 times
#>
#> dose-response 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: dose-response 60 0 0.5282782 0.1234888 0.9330676 0.20652898
#> 2: dose-response 60 1 0.8949096 0.8385038 0.9513155 0.02877903
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 1.6940120 0.7764763 3.695769 0.3666314
#> 2: 60 risk_1 risk_0 0.5903146 0.2705797 1.287869 -0.3666314
#> RD 95% LCI RD 95% UCI
#> <num> <num>
#> 1: -0.07309383 0.80635663
#> 2: -0.80635663 0.07309383Dose-response 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 = "dose-response",
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, 17 variables
#>
#> Expansion Successful
#>
#> Final analysis dataset: 1,119,229 observations, 17 variables
#>
#> Moving forward with dose-response analysis
#>
#> Bootstrapping with 80% of 1,000 subjects (800 subjects, ~895,383 observations per resample) 5 times
#>
#> dose-response 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: dose-response 60 0 0.007847443 0.000000000 0.02684988 0.009695299
#> 2: dose-response 60 1 0.018827788 0.001997953 0.03565762 0.008586808
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 2.3992259 0.44856849 12.832566 0.01098034
#> 2: 60 risk_1 risk_0 0.4168011 0.07792674 2.229314 -0.01098034
#> RD 95% LCI RD 95% UCI
#> <num> <num>
#> 1: -0.01349983 0.03546052
#> 2: -0.03546052 0.01349983Dose-response 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 = "dose-response",
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, 16 variables
#>
#> Expansion Successful
#>
#> Final analysis dataset: 1,119,229 observations, 16 variables
#>
#> Moving forward with dose-response analysis
#>
#> Bootstrapping with 80% of 1,000 subjects (800 subjects, ~895,383 observations per resample) 5 times
#>
#> dose-response 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: dose-response 60 0 0.007586789 0 0.25972299 0.12864328
#> 2: dose-response 60 1 0.003641046 0 0.02703035 0.01193354
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 0.4799192 0.02752717 8.367096 -0.003945743
#> 2: 60 inc_1 inc_0 2.0836841 0.11951578 36.327751 0.003945743
#> RD 95% LCI RD 95% UCI
#> <num> <num>
#> 1: -0.2462717 0.2383802
#> 2: -0.2383802 0.2462717Dose-response 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 = "dose-response",
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, 16 variables
#>
#> Expansion Successful
#>
#> Final analysis dataset: 1,119,229 observations, 16 variables
#>
#> Moving forward with dose-response 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
#> 0.9582143 0.7107252 1.2918841Dose-response 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 = "dose-response",
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, 16 variables
#>
#> Expansion Successful
#>
#> Final analysis dataset: 1,119,229 observations, 16 variables
#>
#> Moving forward with dose-response analysis
#>
#> Bootstrapping with 80% of 1,000 subjects (800 subjects, ~895,383 observations per resample) 5 times
#>
#> dose-response 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: dose-response 60 0 0.01125753 0 0.04037235 0.01485477
#> 2: dose-response 60 1 0.01869016 0 0.06423635 0.02323828
#>
#> $sex_1
#> Index: <Followup>
#> Method Followup A Risk 95% LCI 95% UCI SE
#> <char> <num> <char> <num> <num> <num> <num>
#> 1: dose-response 60 0 0.00659838 0 0.47302574 0.23797751
#> 2: dose-response 60 1 0.01221464 0 0.04936991 0.01895712
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 1.6602358 2.459467e-05 112072.36 0.007432626
#> 2: 60 inc_1 inc_0 0.6023241 8.922806e-06 40659.22 -0.007432626
#> RD 95% LCI RD 95% UCI
#> <num> <num>
#> 1: -0.04559720 0.06046245
#> 2: -0.06046245 0.04559720
#>
#> $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 1.8511568 0.09376874 36.54503 0.005616256
#> 2: 60 inc_1 inc_0 0.5402028 0.02736350 10.66454 -0.005616256
#> RD 95% LCI RD 95% UCI
#> <num> <num>
#> 1: -0.4278731 0.4391056
#> 2: -0.4391056 0.4278731