Plot conditional mean trajectories (CMT)

This function generates the Conditional Mean Trajectories (CMT) plot. All patients in this plot experience events at the same time point, specified by condi_time2event. Several evenly spaced time points between the baseline and condi_time2event are selected for plotting. Each point is calculated using the mean value of all patients' biomarker values at that time point. The interval between two time points is defined by interval_time

Usage

cmtPlot(
  data_plot_all,
  condi_time2event,
  event_type_variable,
  event_type,
  bio_variable,
  time_variable,
  survival_variable,
  interval_time = 1/12,
  id_variable = "id"
)

Arguments

data_plot_all: A data.frame that includes the biomarker used for plotting. It is utilized to plot conditional mean trajectories (CMT).
condi_time2event: Conditional event time, indicating that all patients should have events at this time in the plot. If NULL, it defaults to the midpoint of the observed range of time_variable in data_plot_all.
event_type_variable: Competing risks variable indicator name. Set to NULL if there are no competing risks.
event_type: A vector containing the names of all event types.
bio_variable: Name of the biomarker variable used for plotting.
time_variable: The name of time variable in linear mixed model.
survival_variable: Name of the time-to-event outcomes variable.
interval_time: The time interval between two time points. Time points are plotted within the baseline to event time.
id_variable: Name of the patient ID column in data_plot_all. Default is "id".

Value

Conditional mean trajectories plot.

Examples


# example without competing risks

data(pbc3)

pbc.cmt <- cmtPlot(data_plot_all = pbc3, condi_time2event = 5, 
   event_type_variable = NULL, event_type = NULL,
   bio_variable = "serBilir", time_variable = "year", 
   survival_variable = "years", 
   interval_time = 1/12
)

pbc.cmt
#> `geom_smooth()` using formula = 'y ~ x'



# example with competing risks

data(pbc3)

data_plot_all = pbc3[!is.na(pbc3$status4),]

pbc.cmt.cr <- cmtPlot(data_plot_all, condi_time2event = 5, 
   event_type_variable = 'status4', event_type = c("0", "1"),
   bio_variable = "albumin", time_variable = "year", 
   survival_variable = "years", 
   interval_time = 1/4
)

pbc.cmt.cr
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'