# Per-Protocol: Dose-Response Analysis

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.

    library(SEQTaRget)

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)

    km_curve(model, plot.type = "risk")        # retrieve risk plot
    risk_data(model)
    risk_comparison(model)

## Dose-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)

    km_curve(model, plot.type = "risk")
    risk_data(model)
    risk_comparison(model)

## Dose-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)

    km_curve(model, plot.type = "risk")
    risk_data(model)
    risk_comparison(model)

## Dose-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)

    # retrieve hazard ratios
    hazard_ratio(model)

## Dose-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)

    km_curve(model, plot.type = "risk")
    risk_data(model)
    risk_comparison(model)