plot-survfitJM: Plot Method for survfitJM Objects In drizopoulos/JM: Joint Modeling of Longitudinal and Survival Data

Description

Produces plots of conditional probabilities of survival.

Usage

 ```1 2 3 4 5 6 7 8``` ```## S3 method for class 'survfitJM' plot(x, estimator = c("both", "mean", "median"), which = NULL, fun = NULL, conf.int = FALSE, fill.area = FALSE, col.area = "grey", col.abline = "black", col.points = "black", add.last.time.axis.tick = FALSE, include.y = FALSE, main = NULL, xlab = NULL, ylab = NULL, ylab2 = NULL, lty = NULL, col = NULL, lwd = NULL, pch = NULL, ask = NULL, legend = FALSE, ..., cex.axis.z = 1, cex.lab.z = 1) ```

Arguments

 `x` an object inheriting from class `survfitJM`. `estimator` character string specifying, whether to include in the plot the mean of the conditional probabilities of survival, the median or both. The mean and median are taken as estimates of these conditional probabilities over the M replications of the Monte Carlo scheme described in `survfitJM`. `which` a numeric or character vector specifying for which subjects to produce the plot. If a character vector, then is should contain a subset of the values of the `idVar` variable of the `newdata` argument of `survfitJM`. `fun` a vectorized function defining a transformation of the survival curve. For example with `fun=log` the log-survival curve is drawn. `conf.int` logical; if `TRUE`, then a pointwise confidence interval is included in the plot. `fill.area` logical; if `TRUE` the area defined by the confidence interval of the survival function is put in color. `col.area` the color of the area defined by the confidence interval of the survival function. `col.abline,col.points` the color for the vertical line and the points when `include.y` is `TRUE`. `add.last.time.axis.tick` logical; if `TRUE`, a tick is added in the x-axis for the last available time point for which a longitudinal measurement was available. `include.y` logical; if `TRUE`, two plots are produced per subject, i.e., the plot of conditional probabilities of survival and a scatterplot of his longitudinal measurements. `main` a character string specifying the title in the plot. `xlab` a character string specifying the x-axis label in the plot. `ylab` a character string specifying the y-axis label in the plot. `ylab2` a character string specifying the y-axis label in the plotm when `include.y = TRUE`. `lty` what types of lines to use. `col` which colors to use. `lwd` the thickness of the lines. `pch` the type of points to use. `ask` logical; if `TRUE`, the user is asked before each plot, see `par()`. `legend` logical; if `TRUE`, a legend is included in the plot. `cex.axis.z, cex.lab.z` the par `cex` argument for the axis at side 4, when `include.y = TRUE`. `...` extra graphical parameters passed to `plot()`.

Author(s)

Dimitris Rizopoulos [email protected]

References

Rizopoulos, D. (2012) Joint Models for Longitudinal and Time-to-Event Data: with Applications in R. Boca Raton: Chapman and Hall/CRC.

Rizopoulos, D. (2011). Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data. Biometrics 67, 819–829.

Rizopoulos, D. (2010) JM: An R Package for the Joint Modelling of Longitudinal and Time-to-Event Data. Journal of Statistical Software 35 (9), 1–33. http://www.jstatsoft.org/v35/i09/

`survfitJM`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```# linear mixed model fit fitLME <- lme(sqrt(CD4) ~ obstime + obstime:drug, random = ~ 1 | patient, data = aids) # cox model fit fitCOX <- coxph(Surv(Time, death) ~ drug, data = aids.id, x = TRUE) # joint model fit fitJOINT <- jointModel(fitLME, fitCOX, timeVar = "obstime", method = "weibull-PH-aGH") # sample of the patients who are still alive ND <- aids[aids\$patient == "141", ] ss <- survfitJM(fitJOINT, newdata = ND, idVar = "patient", M = 50) plot(ss) plot(ss, include.y = TRUE, add.last.time.axis.tick = TRUE, legend = TRUE) ```