horseshoenlm (version 0.0.6)

afths: Horseshoe shrinkage prior in Bayesian survival regression

Description

This function employs the algorithm provided by van der Pas et. al. (2016) for log normal Accelerated Failure Rate (AFT) model to fit survival regression. The censored observations are updated according to the data augmentation approach described in Maity et. al. (2019) and Maity et. al. (2020).

Usage

afths(
  ct,
  X,
  method.tau = c("fixed", "truncatedCauchy", "halfCauchy"),
  tau = 1,
  method.sigma = c("fixed", "Jeffreys"),
  Sigma2 = 1,
  burn = 1000,
  nmc = 5000,
  thin = 1,
  alpha = 0.05,
  Xtest = NULL
)

Arguments

ct

survival response, a \(n*2\) matrix with first column as response and second column as right censored indicator, 1 is event time and 0 is right censored.

X

Matrix of covariates, dimension \(n*p\).

method.tau

Method for handling \(\tau\). Select "truncatedCauchy" for full Bayes with the Cauchy prior truncated to [1/p, 1], "halfCauchy" for full Bayes with the half-Cauchy prior, or "fixed" to use a fixed value (an empirical Bayes estimate, for example).

tau

Use this argument to pass the (estimated) value of \(\tau\) in case "fixed" is selected for method.tau. Not necessary when method.tau is equal to "halfCauchy" or "truncatedCauchy". The default (tau = 1) is not suitable for most purposes and should be replaced.

method.sigma

Select "Jeffreys" for full Bayes with Jeffrey's prior on the error variance \(\sigma^2\), or "fixed" to use a fixed value (an empirical Bayes estimate, for example).

Sigma2

A fixed value for the error variance \(\sigma^2\). Not necessary when method.sigma is equal to "Jeffreys". Use this argument to pass the (estimated) value of Sigma2 in case "fixed" is selected for method.sigma. The default (Sigma2 = 1) is not suitable for most purposes and should be replaced.

burn

Number of burn-in MCMC samples. Default is 1000.

nmc

Number of posterior draws to be saved. Default is 5000.

thin

Thinning parameter of the chain. Default is 1 (no thinning).

alpha

Level for the credible intervals. For example, alpha = 0.05 results in 95% credible intervals.

Xtest

test design matrix.

Value

SurvivalHat

Predictive survival probability

LogTimeHat

Predictive log time

BetaHat

Posterior mean of Beta, a \(p\) by 1 vector

LeftCI

The left bounds of the credible intervals

RightCI

The right bounds of the credible intervals

BetaMedian

Posterior median of Beta, a \(p\) by 1 vector

LambdaHat

Posterior samples of \(\lambda\), a \(p*1\) vector

Sigma2Hat

Posterior mean of error variance \(\sigma^2\). If method.sigma = "fixed" is used, this value will be equal to the user-selected value of Sigma2 passed to the function

TauHat

Posterior mean of global scale parameter tau, a positive scalar

BetaSamples

Posterior samples of \(\beta\)

TauSamples

Posterior samples of \(\tau\)

Sigma2Samples

Posterior samples of Sigma2

LikelihoodSamples

Posterior samples of likelihood

DIC

Devainace Information Criterion of the fitted model

WAIC

Widely Applicable Information Criterion

Details

The model is: \(t_i\) is response, \(c_i\) is censored time, \(t_i^* = \min_(t_i, c_i)\) is observed time, \(w_i\) is censored data, so \(w_i = \log t_i^*\) if \(t_i\) is event time and \(w_i = \log t_i^*\) if \(t_i\) is right censored \(\log t_i=X\beta+\epsilon, \epsilon \sim N(0,\sigma^2)\).

References

Maity, A. K., Carroll, R. J., and Mallick, B. K. (2019) "Integration of Survival and Binary Data for Variable Selection and Prediction: A Bayesian Approach", Journal of the Royal Statistical Society: Series C (Applied Statistics).

Maity, A. K., Bhattacharya, A., Mallick, B. K., & Baladandayuthapani, V. (2020). Bayesian data integration and variable selection for pan cancer survival prediction using protein expression data. Biometrics, 76(1), 316-325.

Stephanie van der Pas, James Scott, Antik Chakraborty and Anirban Bhattacharya (2016). horseshoe: Implementation of the Horseshoe Prior. R package version 0.1.0. https://CRAN.R-project.org/package=horseshoe

Enes Makalic and Daniel Schmidt (2016). High-Dimensional Bayesian Regularised Regression with the BayesReg Package arXiv:1611.06649

Examples

# NOT RUN {
burnin <- 500
nmc    <- 1000
thin <- 1
y.sd   <- 1  # standard deviation of the response

p <- 100  # number of predictors
ntrain <- 100  # training size
ntest  <- 50   # test size
n <- ntest + ntrain  # sample size
q <- 10   # number of true predictos

beta.t <- c(sample(x = c(1, -1), size = q, replace = TRUE), rep(0, p - q)) 
x <- mvtnorm::rmvnorm(n, mean = rep(0, p), sigma = diag(p))    

tmean <- x %*% beta.t
y <- rnorm(n, mean = tmean, sd = y.sd)
X <- scale(as.matrix(x))  # standarization

T <- exp(y)   # AFT model
C <- rgamma(n, shape = 1.75, scale = 3)   # 42% censoring time
time <- pmin(T, C)  # observed time is min of censored and true
status = time == T   # set to 1 if event is observed
ct <- as.matrix(cbind(time = time, status = status))  # censored time


# Training set
cttrain <- ct[1:ntrain, ]
Xtrain  <- X[1:ntrain, ]

# Test set
cttest <- ct[(ntrain + 1):n, ]
Xtest  <- X[(ntrain + 1):n, ]

posterior.fit <- afths(ct = cttrain, X = Xtrain, method.tau = "halfCauchy",
                             method.sigma = "Jeffreys", burn = burnin, nmc = nmc, thin = 1,
                             Xtest = Xtest)
                             
posterior.fit$BetaHat

# Posterior processing to recover the true predictors
cluster <- kmeans(abs(posterior.fit$BetaHat), centers = 2)$cluster
cluster1 <- which(cluster == 1)
cluster2 <- which(cluster == 2)
min.cluster <- ifelse(length(cluster1) < length(cluster2), 1, 2)
which(cluster == min.cluster)  # this matches with the true variables


# }