Description Usage Arguments Details Value Author(s) Examples
This function performs MetropolisHastings exploration of the posterior distribution for Bayesian discrete time survival models. The models may have competing risks, semiparametric covariate effects (like arbitrary baseline hazards), and random effects shared across repeated events and correlated across competing risks.
1 
x 
an 
y 
an 
S 
the number of MCMC iterations to perform 
priors 
a list with one element for each predictor variable (column of 
n 
a vector of positive integers with length equal to the number of persontime observations whose entries equal the number of replicated observations that row stands for (defaults to 1 for each observation) 
K 
a vector of positive integers with length equal to the number of predictors giving the number of distinct values each discretized predictor may assume; this is not used if a dataframe of factors is provided for 
store_re 
if 
The data provided to the brea_mcmc
function is specified at the persontime level: there is one row in x
and y
for each discrete time point each person or thing was at risk for event occurrence. All predictors in x
must be encoded as factors (or their corresponding integer codes in the case that x
is an integer
matrix). The underlying type of predictor is specified in the priors
argument, which is a list with one element for each predictor variable which specifies both the type of that predictor and the prior distribution to use. The allowed predictor types are:
"cat"
for categorical variables. The first element of the prior specification list is the string "cat"
, and second element is a positive conditional prior standard deviation parameter.
"gmrf"
for underlying continuous predictors; continuous predictors should be cut()
before being included into x
; Gaussian Markov random field (GMRF) priors are then used to smooth the effects of adjacent categories of the discretized continuous predictor. The first element of the prior specification list is the string "gmrf"
, the second element is a prior degrees of freedom for the scaled inverse chisquared prior for the random walk increment variance, and the third element is a prior scale for the scaled inverse chisquared.
"re"
for variables (like subject id numbers) that represent random effects. The first element of the prior specification list is the string "re"
, the second element is a prior degrees of freedom for an inverse Wishart prior for the random effects covariance, and the third element is a prior scale matrix for the random effects covariance.
"zero"
for predictors that are not used in the current MCMC run. This is provided as a convenient way to exclude predictors from certain runs. The first and only element of the prior specification list is the string "zero"
.
A list providing the values from each of the S
MCMC iterations for the intercept, the other linear predictor parameters, standard deviations of the GMRF random walk increments, and covariances of the random effects:
b_0_s 
an 
b_m_s 
a list whose m^th element is the 
s_m_s 
a list whose m^th element is, in the case of a GMRF prior, the 
b_m_a 
a list whose m^th element is the length 
Adam King
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45  # leukemia remission times data from Table 1 of "Regression Models and
# LifeTables" (Cox, 1972)
# times of event occurrence or right censoring:
study_time < c(6,6,6,6,7,9,10,10,11,13,16,17,19,20,22,23,25,32,32,34,35,
1,1,2,2,3,4,4,5,5,8,8,8,8,11,11,12,12,15,17,22,23)
# event/censoring indicator (1 for event occurrence, 0 for right censoring):
cens < c(0,1,1,1,1,0,0,1,0,1,1,0,0,0,1,1,0,0,0,0,0,
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
# treatment assignment (1 for treatment, 2 for control):
treat < c(rep(1L,21),rep(2L,21))
# total number of persontime observations among all subjects:
N < sum(study_time)
# create and fill in persontime predictor and event indicator matrices:
x < matrix(0L,N,2) # first column is time, second column is treatment
y < matrix(0L,N,1)
next_row < 1 # next available row in the persontime matrices
for (i in seq_along(study_time)) {
x[next_row:(next_row + study_time[i]  1),] < cbind(seq_len(study_time[i]),
rep(treat[i],study_time[i]))
y[next_row:(next_row + study_time[i]  1),] < c(rep(0L,study_time[i]  1),
as.integer(cens[i]))
next_row < next_row + study_time[i]
}
# group the time variable into 6week intervals:
x[,1] < cut(x[,1],seq(0,36,6),labels=FALSE)
# use GMRF prior for time, and categorical prior for treatment group:
priors < list(list("gmrf",3,.01),list("cat",2))
# run 1,100 MCMC iterations:
fit < brea_mcmc(x, y, 1100, priors)
# look at the structure of the returned posterior samples and acceptance counts:
str(fit)
# approximate posterior mean hazard ratio:
ss < 101:1100 # use last 1,000 samples, discarding first 100 as burnin
exp(2*mean(fit$b_m_s[[2]][1,2,ss]))
# hazard ratio of approximately 5

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