Predictions from Joint Models
Predict method for object of class "jm".
## S3 method for class 'jm' predict(object, newdata = NULL, newdata2 = NULL, times = NULL, process = c("longitudinal", "event"), type_pred = c("response", "link"), type = c("subject_specific", "mean_subject"), control = NULL, ...) ## S3 method for class 'predict_jm' plot(x, x2 = NULL, subject = 1, outcomes = 1, fun_long = NULL, fun_event = NULL, CI_long = TRUE, CI_event = TRUE, xlab = "Follow-up Time", ylab_long = NULL, ylab_event = "Cumulative Risk", main = "", lwd_long = 2, lwd_event = 2, ylim_event = c(0, 1), ylim_long_outcome_range = TRUE, col_line_long = "#0000FF", col_line_event = c("#FF0000", "#03BF3D", "#8000FF"), pch_points = 16, col_points = "blue", cex_points = 1, fill_CI_long = "#0000FF4D", fill_CI_event = c("#FF00004D", "#03BF3D4D", "#8000FF4D"), cex_xlab = 1, cex_ylab_long = 1, cex_ylab_event = 1, cex_main = 1, cex_axis = 1, col_axis = "black", pos_ylab_long = c(0.1, 2, 0.08), bg = "white", ...) ## S3 method for class 'jmList' predict(object, weights, newdata = NULL, newdata2 = NULL, times = NULL, process = c("longitudinal", "event"), type_pred = c("response", "link"), type = c("subject_specific", "mean_subject"), control = NULL, ...)
object: an object inheriting from class "jm" or a list of "jm" objects.
weights: a numeric vector of model weights.
newdata, newdata2: data.frames.
times: a numeric vector of future times to calculate predictions.
process: for which process to calculation predictions, for the longitudinal outcomes or the event times.
type: level of predictions; only relevant when type_pred = "longitudinal". Option type = "subject_specific" combines the fixed- and random-effects parts, whereas type = "mean_subject" uses only the fixed effects.
type_pred: type of predictions; options are "response" using the inverse link function in GLMMs, and "link" that correspond to the linear predictor.
control: a named list of control parameters:
TRUE predictions for the longitudinal outcomes are calculated for all the times given in the times argumet, not only the ones after the last longitudinal measurement.TRUE the times argument is a vector of times equal to the number of subjects in newdata.predict() return the predictions as extra columns in newdata and newdata2.TRUE the mcmc sample for the predictions is returned. It can be TRUE only in conjuction with return_newdata being FALSE.object.n_samples; only the last iteration is retained."snow" (default) and "multicore".newdata, parallel computing is invoked with four cores by default. If cores = 1, no parallel computing is used.x, x2: objects returned by predict.jm() with argument return_data set to TRUE.
subject: when multiple subjects are included in the data.frames x and x2, it selects which one to plot. Only a single subject can be plotted each time.
outcomes: when multiple longitudinal outcomes are included in the data.frames x and x2, it selects which ones to plot. A maximum of three outcomes can be plotted each time.
fun_long, fun_event: function to apply to the predictions for the longitudinal and event outcomes, respectively. When multiple longitudinal outcomes are plotted, fun_long can be a list of functions; see examples below.
CI_long, CI_event: logical; should credible interval areas be plotted.
xlab, ylab_long, ylab_event: characture strings or a chracter vector for ylab_long when multiple longitudinal outcomes are considered with the labels for the horizontal axis, and the two vertical axes.
lwd_long, lwd_event, col_line_long, col_line_event, main, fill_CI_long, fill_CI_event, cex_xlab, cex_ylab_long, cex_ylab_event, cex_main, cex_axis, pch_points, col_points, cex_points, col_axis, bg: graphical parameters; see par.
pos_ylab_long: controls the position of the y-axis labels when multiple longitudinal outcomes are plotted.
ylim_event: the ylim for the event outcome.
ylim_long_outcome_range: logical; if TRUE, the range of the y-axis spans across the range of the outcome in the data used to fit the model; not only the range of values of the specific subject being plotted.
...: aguments passed to control.
A detailed description of the methodology behind these predictions is given here: https://drizopoulos.github.io/JMbayes2/articles/Dynamic_Predictions.html.
Method predict() returns a list or a data.frame (if return_newdata was set to TRUE) with the predictions.
Method plot() produces figures of the predictions from a single subject.
Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl
jm
# We fit a multivariate joint model pbc2.id$status2 <- as.numeric(pbc2.id$status != 'alive') CoxFit <- coxph(Surv(years, status2) ~ sex, data = pbc2.id) fm1 <- lme(log(serBilir) ~ ns(year, 3) * sex, data = pbc2, random = ~ ns(year, 3) | id, control = lmeControl(opt = 'optim')) fm2 <- lme(prothrombin ~ ns(year, 2) * sex, data = pbc2, random = ~ ns(year, 2) | id, control = lmeControl(opt = 'optim')) fm3 <- mixed_model(ascites ~ year * sex, data = pbc2, random = ~ year | id, family = binomial()) jointFit <- jm(CoxFit, list(fm1, fm2, fm3), time_var = "year", n_chains = 1L) # we select the subject for whom we want to calculate predictions # we use measurements up to follow-up year 3; we also set that the patients # were alive up to this time point t0 <- 3 ND <- pbc2[pbc2$id %in% c(2, 25), ] ND <- ND[ND$year < t0, ] ND$status2 <- 0 ND$years <- t0 # predictions for the longitudinal outcomes using newdata predLong1 <- predict(jointFit, newdata = ND, return_newdata = TRUE) # predictions for the longitudinal outcomes at future time points # from year 3 to 10 predLong2 <- predict(jointFit, newdata = ND, times = seq(t0, 10, length.out = 51), return_newdata = TRUE) # predictions for the event outcome at future time points # from year 3 to 10 predSurv <- predict(jointFit, newdata = ND, process = "event", times = seq(t0, 10, length.out = 51), return_newdata = TRUE) plot(predLong1) # for subject 25, outcomes in reverse order plot(predLong2, outcomes = 3:1, subject = 25) # prediction for the event outcome plot(predSurv) # combined into one plot, the first longitudinal outcome and cumulative risk plot(predLong2, predSurv, outcomes = 1) # the first two longitudinal outcomes plot(predLong1, predSurv, outcomes = 1:2) # all three longitudinal outcomes, we display survival probabilities instead # of cumulative risk, and we transform serum bilirubin to the original scale plot(predLong2, predSurv, outcomes = 1:3, fun_event = function (x) 1 - x, fun_long = list(exp, identity, identity), ylab_event = "Survival Probabilities", ylab_long = c("Serum Bilirubin", "Prothrombin", "Ascites"), pos_ylab_long = c(1.9, 1.9, 0.08))
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