Plot Actual and Predicted Response Variable Means by Study Arm and Subgroup.

This function accepts a data set, probably produced by ncs_analysis_subgroup(), and it uses ggplot2 to produce a grid of plots, one for each combination of study arm and subgroup. The time variable is along the x-axis, and the response variable is along the y-axis. The actual means of the response variable are points plotted in one color, and the modeled means are plotted in another color. Each point also has its confidence interval plotted.

Usage

ncs_plot_means_subgroup(
  data,
  arm = "arm",
  time = "time",
  subgroup = "subgroup",
  est = "est",
  lower = "lower",
  upper = "upper",
  model_est = "response_est",
  model_lower = "response_lower",
  model_upper = "response_upper"
)

Arguments

data: (data frame)
a data frame, probably produced by ncs_analysis(), containing the actual and predicted means. Each row should have a unique combination of arm, time, and subgroup.
arm: (string)
the name of the study arm variable in data. There will be a separate column of plots produced for each study arm.
time: (string)
the name of the time or visit variable in data. These values correspond to the x-axis.
subgroup: (string)
the name of the subgroup variable in data. There will be a separate row of plots produced for each subgroup.
est, lower, upper: (string)
the name of the variables in data containing the actual response variable's mean and confidence interval bounds. These values correspond to the y-axis.
model_est, model_lower, model_upper: (string)
the name of the variables in data containing the predicted response variable's mean and confidence interval bounds. These values correspond to the y-axis.

Value

An object returned by ggplot2::ggplot().

Examples

if (FALSE) { # interactive()
# Create a usable data set out of mmrm::fev_data
fev_mod <- mmrm::fev_data
fev_mod$VISITN <- fev_mod$VISITN * 10
fev_mod$time_cont <- fev_mod$VISITN + rnorm(nrow(fev_mod))
fev_mod$obs_visit_index <- round(fev_mod$time_cont)

# Analysis result data set
ncs_data_results_subgroup <-
  ncs_analysis_subgroup(
    data = fev_mod,
    response = FEV1,
    subject = USUBJID,
    arm = ARMCD,
    control_group = "PBO",
    subgroup = RACE,
    subgroup_comparator = "Asian",
    time_observed_continuous = time_cont,
    df = 2,
    time_observed_index = obs_visit_index,
    time_scheduled_continuous = VISITN,
    time_scheduled_baseline = 10,
    time_scheduled_label = AVISIT,
    covariates = ~ FEV1_BL + RACE,
    cov_structs = c("ar1", "us")
  )

ncs_plot_means_subgroup(ncs_data_results_subgroup$between)
}