Plot longitudinal observations and predicted survival curve for one individual

Produces a single, self-explanatory panel with a common time axis. To the left of the landmark dashed line, the individual's observed longitudinal measurements are shown, together with model-based context depending on the longitudinal sub-model used at that landmark:

LOCF (or another summary measure): the last observed value is carried forward to the landmark (dashed segment).
lme4: the population-average trajectory (fixed effects only) and the individual's predicted trajectory (including their predicted random effects) are both drawn.
lcmm: the average trajectory of every latent cluster is drawn, together with the individual's own predicted trajectory (each cluster's fixed effects plus the individual's predicted random effects, averaged across clusters using the individual's posterior class-membership probabilities). The individual's most likely cluster and their posterior probability of belonging to each cluster are noted in the plot title/subtitle.

In every case, the value that is actually fed into the survival sub-model is highlighted at the landmark. To the right of the landmark dashed line, the individual's predicted survival curve is shown on a secondary axis. A legend identifies every series.

Usage

# S4 method for class 'LandmarkAnalysis'
plot(x, id, landmark, dynamic_covariate, horizon = NULL, train = TRUE, ...)

Arguments

x: An object of class LandmarkAnalysis.
id: Identifier of the individual to plot. Must match a value in the column x@ids.
landmark: Numeric landmark time.
dynamic_covariate: Character name of the dynamic covariate to display.
horizon: Numeric horizon time. If NULL (default), uses the single available horizon for landmark; errors when multiple horizons are available.
train: Logical. If TRUE (default), uses in-sample predictions. If FALSE, uses out-of-sample predictions (requires validation_fold > 0 in predict_survival).
...: Additional arguments (not currently used).

Value

A ggplot object.