# Function to Fit the Penalized Parametric Bayesian Accelerated Failure Time Model with Group Lasso Prior for Left-Truncated and Interval-Censored Data Penalized parametric Bayesian accelerated failure time model with group lasso prior is implemented to analyze left-truncated and interval-censored survival data with high-dimensional covariates. ```r aftGL_LT(Y, X, XC, grpInx, hyperParams, startValues, mcmcParams) ``` ## Arguments - `Y`: Outcome matrix with three column vectors corresponding to lower and upper bounds of interval-censored data and left-truncation time - `X`: Covariate matrix $p$ covariate vectors from `n` subjects. It is of dimension $n\times p$. - `XC`: Matrix for confound variables: $q$ variable vectors from `n` subjects. It is of dimension $n\times q$. - `grpInx`: a vector of $p$ group indicator for each variable - `hyperParams`: a list containing hyperparameter values in hierarchical models: (`a.sigSq`, `a.sigSq`): hyperparameters for the prior of $\sigma^2$; (`mu0`, `h0`): hyperparameters for the prior of $\mu$; (`v`): hyperparameter for the prior of $\beta_C$. - `startValues`: a list containing starting values for model parameters. See Examples below. - `mcmcParams`: a list containing variables required for MCMC sampling. Components include, `numReps`, total number of scans; `thin`, extent of thinning; `burninPerc`, the proportion of burn-in. See Examples below. ## Returns `aftGL_LT` returns an object of class `aftGL_LT`. ## References Reeder, H., Haneuse, S., Lee, K. H. (2024+). Group Lasso Priors for Bayesian Accelerated Failure Time Models with Left-Truncated and Interval-Censored Data. **under review** ## Author(s) Kyu Ha Lee, Harrison Reeder ## See Also `VS` ## Examples ```r ## Not run: data(survData) X <- survData[,c(4:5)] XC <- NULL n <- dim(survData)[1] p <- dim(X)[2] q <- 0 c0 <- rep(0, n) yL <- yU <- survData[,1] yU[which(survData[,2] == 0)] <- Inf Y <- cbind(yL, yU, c0) grpInx <- 1:p K <- length(unique(grpInx)) ##################### ## Hyperparameters a.sigSq= 0.7 b.sigSq= 0.7 mu0 <- 0 h0 <- 10^6 v = 10^6 hyperParams <- list(a.sigSq=a.sigSq, b.sigSq=b.sigSq, mu0=mu0, h0=h0, v=v) ################### ## MCMC SETTINGS ## Setting for the overall run ## numReps <- 100 thin <- 1 burninPerc <- 0.5 ## Tuning parameters for specific updates ## L.beC <- 50 M.beC <- 1 eps.beC <- 0.001 L.be <- 100 M.be <- 1 eps.be <- 0.001 mu.prop.var <- 0.5 sigSq.prop.var <- 0.01 ## mcmcParams <- list(run=list(numReps=numReps, thin=thin, burninPerc=burninPerc), tuning=list(mu.prop.var=mu.prop.var, sigSq.prop.var=sigSq.prop.var, L.beC=L.beC, M.beC=M.beC, eps.beC=eps.beC, L.be=L.be, M.be=M.be, eps.be=eps.be)) ##################### ## Starting Values w <- log(Y[,1]) mu <- 0.1 beta <- rep(2, p) sigSq <- 0.5 tauSq <- rep(0.4, p) lambdaSq <- 100 betaC <- rep(0.11, q) startValues <- list(w=w, beta=beta, tauSq=tauSq, mu=mu, sigSq=sigSq, lambdaSq=lambdaSq, betaC=betaC) fit <- aftGL_LT(Y, X, XC, grpInx, hyperParams, startValues, mcmcParams) ## End(Not run) ```

aftGL_LT function