Per-Protocol: Censoring Analysis

Here, we’ll go over some examples of using per-protocol, censoring. First we need to load the library before getting in to some sample use cases.

Per-protocol, censoring, weights in pre-expanded data and no truncation, no excused conditions (i.e. static interventions)

options <- SEQopts(# tells SEQuential to create kaplan meier curves
                   km.curves = TRUE,
                   # tells SEQuential to weight the outcome model
                   weighted = TRUE, 
                   # tells SEQuential to build weights from the pre-expanded data
                   weight.preexpansion = TRUE)

# use some example data in the package
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 = "censoring",
                    options = options)

# retrieve risk plot
km_curve(model, plot.type = "risk")
# retrieve survival and risk data
survival_data <- km_data(model)

Per-protocol, censoring, weights in post-expanded data and no truncation, no excused conditions (i.e. static interventions)

options <- SEQopts(km.curves = TRUE,
                   weighted = TRUE, 
                   # tells SEQuential to build weights from the post-expanded data
                   weight.preexpansion = FALSE)

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 = "censoring",
                    options = options)

km_curve(model, plot.type = "risk")
survival_data <- km_data(model)

Per-protocol, censoring, weights in pre-expanded data and no truncation, excused conditions for initiators and non-initiators (i.e. dynamic interventions)

options <- SEQopts(km.curves = TRUE,
                   weighted = TRUE,
                   weight.preexpansion = TRUE,
                   # tells SEQuential to run a dynamic intervention
                   excused = TRUE,                               
                   # tells SEQuential to use columns excusedOne and 
                   # excusedZero as excused conditions for treatment switches
                   excused.cols = c("excusedZero", "excusedOne"), 
                   # tells SEQuential to expect treatment levels 0, 1
                   # (mapping to the same positions as the list in excused.cols)
                   treat.level = c(0, 1))
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 = "censoring",
                    options = options)

km_curve(model, plot.type = "risk")
survival_data <- km_data(model)

Per-protocol, censoring, weights in post-expanded data and no truncation, excused conditions for initiators and non-initiators (i.e. dynamic interventions)

options <- SEQopts(km.curves = TRUE,
                   weighted = TRUE,
                   weight.preexpansion = FALSE,
                   excused = TRUE,                               
                   excused.cols = c("excusedZero", "excusedOne"), 
                   treat.level = c(0, 1))
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 = "censoring",
                    options = options)

km_curve(model, plot.type = "risk")
survival_data <- km_data(model)

Per-protocol, censoring, weights in post-expanded data and no truncation, excused conditions for initiators and non-initiators (i.e. dynamic interventions) and a competing event

options <- SEQopts(km.curves = TRUE,
                   weighted = TRUE,
                   weight.preexpansion = FALSE,
                   excused = TRUE,                               
                   excused.cols = c("excusedZero", "excusedOne"), 
                   treat.level = c(0, 1),
                   # add a 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 = "censoring",
                    options = options)

km_curve(model, plot.type = "risk")
survival_data <- km_data(model)

Per-protocol, censoring, weights in post-expanded data and no truncation, excused conditions for initiators and non-initiators (i.e. dynamic interventions) and hazard ratio

options <- SEQopts(# tell SEQuential to run hazard ratios
                   hazard = TRUE,
                   weighted = TRUE,
                   weight.preexpansion = FALSE,
                   excused = TRUE,                               
                   excused.cols = c("excusedZero", "excusedOne"))

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 = "censoring",
                    options = options)