JMbayes (version 0.8-85)

bma.combine: Combines Predictions for Bayesian Model Averaging

Description

Combines estimated survival probabilities or predictions for longitudinal responses.

Usage

bma.combine(…, JMlis = NULL, weights = NULL)

Arguments

…

objects inheriting from class survfit.JMbayes or predict.JMbayes.

JMlis

a list of survfit.JMbayes or predict.JMbayes objects.

weights

a numeric vector of weights to be applied in each object.

Value

an object of class survfit.JMbayes or predict.JMbayes.

References

Rizopoulos, D. (2016). The R package JMbayes for fitting joint models for longitudinal and time-to-event data using MCMC. Journal of Statistical Software 72(7), 1--45. doi:10.18637/jss.v072.i07.

Rizopoulos, D., Hatfield, L., Carlin, B. and Takkenberg, J. (2014). Combining dynamic predictions from joint models for longitudinal and time-to-event data using Bayesian model averaging. Journal of the American Statistical Association 109, 1385--1397.

Examples

# NOT RUN {
# we construct the composite event indicator (transplantation or death)
pbc2$status2 <- as.numeric(pbc2$status != "alive")
pbc2.id$status2 <- as.numeric(pbc2.id$status != "alive")

# we fit two joint models using splines for the subject-specific 
# longitudinal trajectories and a spline-approximated baseline
# risk function; the first one with the current value parameterization
# and the other with the shared random effects parameterization
lmeFit <- lme(log(serBilir) ~ ns(year, 2), data = pbc2,
              random = ~ ns(year, 2) | id)
survFit <- coxph(Surv(years, status2) ~ drug, data = pbc2.id, x = TRUE)

jointFit1 <- jointModelBayes(lmeFit, survFit, timeVar = "year")
jointFit2 <- update(jointFit1, param = "shared-RE")

# we compute survival probabilities for Subject 2 with 
# different weights
ND <- pbc2[pbc2$id == 2, ] # the data of Subject 2
survPreds1 <- survfitJM(jointFit1, newdata = ND, weight = 0.4)
survPreds2 <- survfitJM(jointFit2, newdata = ND, weight = 0.6)

survPreds.bma <- bma.combine(survPreds1, survPreds2)
survPreds.bma
plot(survPreds.bma)
# }