BayesID_HReg: The function to implement Bayesian parametric and... In SemiCompRisks: Hierarchical Models for Parametric and Semi-Parametric Analyses of Semi-Competing Risks Data

Description

Independent/cluster-correlated semi-competing risks data can be analyzed using HReg models that have a hierarchical structure. The priors for baseline hazard functions can be specified by either parametric (Weibull) model or non-parametric mixture of piecewise exponential models (PEM). The option to choose between parametric multivariate normal (MVN) and non-parametric Dirichlet process mixture of multivariate normals (DPM) is available for the prior of cluster-specific random effects distribution. The conditional hazard function for time to the terminal event given time to non-terminal event can be modeled based on either Markov (it does not depend on the timing of the non-terminal event) or semi-Markov assumption (it does depend on the timing).

Usage

 1 2 BayesID_HReg(Formula, data, id=NULL, model=c("semi-Markov", "Weibull"), hyperParams, startValues, mcmcParams, na.action = "na.fail", subset=NULL, path=NULL) 

Arguments

 Formula a Formula object of the form y_1+δ_1 | y_2+δ_2 ~ x_1 | x_2 | x_3. See Details and Examples below. data a data.frame in which to interpret the variables named in Formula. id a vector of cluster information for n subjects. The cluster membership must be consecutive positive integers, 1:J. model a character vector that specifies the type of components in a model. The first element is for the assumption on h_3: "semi-Markov" or "Markov". The second element is for the specification of baseline hazard functions: "Weibull" or "PEM". The third element needs to be set only for clustered semi-competing risks data and is for the specification of cluster-specific random effects distribution: "MVN" or "DPM". hyperParams a list containing lists or vectors for hyperparameter values in hierarchical models. Components include, theta (a numeric vector for hyperparameter in the prior of subject-specific frailty variance component), WB (a list containing numeric vectors for Weibull hyperparameters: WB.ab1, WB.ab2, WB.ab3, WB.cd1, WB.cd2, WB.cd3), PEM (a list containing numeric vectors for PEM hyperparameters: PEM.ab1, PEM.ab2, PEM.ab3, PEM.alpha1, PEM.alpha2, PEM.alpha3). Models for clustered data require additional components, MVN (a list containing numeric vectors for MVN hyperparameters: Psi_v, rho_v), DPM (a list containing numeric vectors for DPM hyperparameters: Psi0, rho0, aTau, bTau). See Details and Examples below. startValues a list containing vectors of starting values for model parameters. It can be specified as the object returned by the function initiate.startValues_HReg. mcmcParams a list containing variables required for MCMC sampling. Components include, run (a list containing numeric values for setting for the overall run: numReps, total number of scans; thin, extent of thinning; burninPerc, the proportion of burn-in). storage (a list containing numeric values for storing posterior samples for subject- and cluster-specific random effects: nGam_save, the number of γ to be stored; storeV, a vector of three logical values to determine whether all the posterior samples of V_1, V_2, V_3 are to be stored). tuning (a list containing numeric values relevant to tuning parameters for specific updates in Metropolis-Hastings-Green (MHG) algorithm: mhProp_theta_var, the variance of proposal density for θ; mhProp_Vg_var, the variance of proposal density for V_g in DPM models; mhProp_alphag_var, the variance of proposal density for α_g in Weibull models; Cg, a vector of three proportions that determine the sum of probabilities of choosing the birth and the death moves in PEM models. The sum of the three elements should not exceed 0.6; delPertg, the perturbation parameters in the birth update in PEM models. The values must be between 0 and 0.5; If rj.scheme=1, the birth update will draw the proposal time split from 1:s_{max}. If rj.scheme=2, the birth update will draw the proposal time split from uniquely ordered failure times in the data. Only required for PEM models; Kg_max, the maximum number of splits allowed at each iteration in MHG algorithm for PEM models; time_lambda1, time_lambda2, time_lambda3 - time points at which the log-hazard functions are calculated for predict.Bayes_HReg, Only required for PEM models). See Details and Examples below. na.action how NAs are treated. See model.frame. subset a specification of the rows to be used: defaults to all rows. See model.frame. path the name of directory where the results are saved.

Details

We view the semi-competing risks data as arising from an underlying illness-death model system in which individuals may undergo one or more of three transitions: 1) from some initial condition to non-terminal event, 2) from some initial condition to terminal event, 3) from non-terminal event to terminal event. Let t_{ji1}, t_{ji2} denote time to non-terminal and terminal event from subject i=1,...,n_j in cluster j=1,...,J. The system of transitions is modeled via the specification of three hazard functions:

h_1(t_{ji1} | γ_{ji}, x_{ji1}, V_{j1}) = γ_{ji} h_{01}(t_{ji1})\exp(x_{ji1}^{\top}β_1 +V_{j1}), t_{ji1}>0,

h_2(t_{ji2} | γ_{ji}, x_{ji2}, V_{j2}) = γ_{ji} h_{02}(t_{ji2})\exp(x_{ji2}^{\top}β_2 +V_{j2}), t_{ji2}>0,

h_3(t_{ji2} | t_{ji1}, γ_{ji}, x_{ji3}, V_{j3}) = γ_{ji} h_{03}(t_{ji2})\exp(x_{ji3}^{\top}β_3 +V_{j3}), 0<t_{ji1}<t_{ji2},

where γ_{ji} is a subject-specific frailty and V_j=(V_{j1}, V_{j2}, V_{j3}) is a vector of cluster-specific random effects (each specific to one of the three possible transitions), taken to be independent of x_{ji1}, x_{ji2}, and x_{ji3}. For g \in \{1,2,3\}, h_{0g} is an unspecified baseline hazard function and β_g is a vector of p_g log-hazard ratio regression parameters. The h_{03} is assumed to be Markov with respect to t_1. We refer to the model specified by three conditional hazard functions as the Markov model. An alternative specification is to model the risk of terminal event following non-terminal event as a function of the sojourn time. Specifically, retaining h_1 and h_2 as above, we consider modeling h_3 as follows:

h_3(t_{ji2} | t_{ji1}, γ_{ji}, x_{ji3}, V_{j3}) = γ_{ji} h_{03}(t_{ji2}-t_{ji1})\exp(x_{ji3}^{\top}β_3 +V_{j3}), 0<t_{ji1}<t_{ji2}.

We refer to this alternative model as the semi-Markov model.
For parametric MVN prior specification for a vector of cluster-specific random effects, we assume V_j arise as i.i.d. draws from a mean 0 MVN distribution with variance-covariance matrix Σ_V. The diagonal elements of the 3\times 3 matrix Σ_V characterize variation across clusters in risk for non-terminal, terminal and terminal following non-terminal event, respectively, which is not explained by covariates included in the linear predictors. Specifically, the priors can be written as follows:

V_j \sim MVN(0, Σ_V),

Σ_V \sim inverse-Wishart(Ψ_v, ρ_v).

For DPM prior specification for V_j, we consider non-parametric Dirichlet process mixture of MVN distributions: the V_j's are draws from a finite mixture of M multivariate Normal distributions, each with their own mean vector and variance-covariance matrix, (μ_m, Σ_m) for m=1,...,M. Let m_j\in\{1,...,M\} denote the specific component to which the jth cluster belongs. Since the class-specific (μ_m, Σ_m) are unknown they are taken to be draws from some distribution, G_0, often referred to as the centering distribution. Furthermore, since the true class memberships are not known, we denote the probability that the jth cluster belongs to any given class by the vector p=(p_1,..., p_M) whose components add up to 1.0. In the absence of prior knowledge regarding the distribution of class memberships for the J clusters across the M classes, a natural prior for p is the conjugate symmetric Dirichlet(τ/M,...,τ/M) distribution; the hyperparameter, τ, is often referred to as a the precision parameter. The prior can be represented as follows (M goes to infinity):

V_j | m_j \sim MVN(μ_{m_j}, Σ_{m_j}),

(μ_m, Σ_m) \sim G_{0},~~ for ~m=1,...,M,

m_j | p \sim Discrete(m_j| p_1,...,p_M),

p \sim Dirichlet(τ/M,...,τ/M),

where G_0 is taken to be a multivariate Normal/inverse-Wishart (NIW) distribution for which the probability density function is the following product:

f_{NIW}(μ, Σ | Ψ_0, ρ_0) = f_{MVN}(μ | 0, Σ) \times f_{inv-Wishart}(Σ | Ψ_0, ρ_0).

We consider Gamma(a_{τ}, b_{τ}) as the prior for concentration parameter τ.

For non-parametric PEM prior specification for baseline hazard functions, let s_{g,\max} denote the largest observed event time for each transition g \in \{1,2,3\}. Then, consider the finite partition of the relevant time axis into K_{g} + 1 disjoint intervals: 0<s_{g,1}<s_{g,2}<...<s_{g, K_g+1} = s_{g, \max}. For notational convenience, let I_{g,k}=(s_{g, k-1}, s_{g, k}] denote the k^{th} partition. For given a partition, s_g = (s_{g,1}, …, s_{g, K_g + 1}), we assume the log-baseline hazard functions is piecewise constant:

λ_{0g}(t)=\log h_{0g}(t) = ∑_{k=1}^{K_g + 1} λ_{g,k} I(t\in I_{g,k}),

where I(\cdot) is the indicator function and s_{g,0} \equiv 0. Note, this specification is general in that the partitions of the time axes differ across the three hazard functions. our prior choices are, for g\in\{1,2,3\}:

λ_g | K_g, μ_{λ_g}, σ_{λ_g}^2 \sim MVN_{K_g+1}(μ_{λ_g}1, σ_{λ_g}^2Σ_{λ_g}),

K_g \sim Poisson(α_g),

π(s_g | K_g) \propto \frac{(2K_g+1)! ∏_{k=1}^{K_g+1}(s_{g,k}-s_{g,k-1})}{(s_{g,K_g+1})^{(2K_g+1)}},

π(μ_{λ_g}) \propto 1,

σ_{λ_g}^{-2} \sim Gamma(a_g, b_g).

Note that K_g and s_g are treated as random and the priors for K_g and s_g jointly form a time-homogeneous Poisson process prior for the partition. The number of time splits and their positions are therefore updated within our computational scheme using reversible jump MCMC.

For parametric Weibull prior specification for baseline hazard functions, h_{0g}(t) = α_g κ_g t^{α_g-1}. In our Bayesian framework, our prior choices are, for g\in\{1,2,3\}:

π(α_g) \sim Gamma(a_g, b_g),

π(κ_g) \sim Gamma(c_g, d_g).

Our prior choice for remaining model parameters in all of four models (Weibull-MVN, Weibull-DPM, PEM-MVN, PEM-DPM) is given as follows:

π(β_g) \propto 1,

γ_{ji}|θ \sim Gamma(θ^{-1}, θ^{-1}),

θ^{-1} \sim Gamma(ψ, ω).

We provide a detailed description of the hierarchical models for cluster-correlated semi-competing risks data. The models for independent semi-competing risks data can be obtained by removing cluster-specific random effects, V_j, and its corresponding prior specification from the description given above.

Value

BayesID_HReg returns an object of class Bayes_HReg.

Note

The posterior samples of γ and V_g are saved separately in working directory/path. For a dataset with large n, nGam_save should be carefully specified considering the system memory and the storage capacity.

Author(s)

Kyu Ha Lee and Sebastien Haneuse
Maintainer: Kyu Ha Lee <[email protected]>

References

Lee, K. H., Haneuse, S., Schrag, D., and Dominici, F. (2015), Bayesian semiparametric analysis of semicompeting risks data: investigating hospital readmission after a pancreatic cancer diagnosis, Journal of the Royal Statistical Society: Series C, 64, 2, 253-273.

Lee, K. H., Dominici, F., Schrag, D., and Haneuse, S. (2016), Hierarchical models for semicompeting risks data with application to quality of end-of-life care for pancreatic cancer, Journal of the American Statistical Association, 111, 515, 1075-1095.

Alvares, D., Haneuse, S., Lee, C., Lee, K. H. (2018+), SemiCompRisks: an R package for independent and cluster-correlated analyses of semi-competing risks data, submitted, arXiv:1801.03567.

initiate.startValues_HReg, print.Bayes_HReg, summary.Bayes_HReg, predict.Bayes_HReg
  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 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 ## Not run: # loading a data set data(scrData) id=scrData$cluster form <- Formula(time1 + event1 | time2 + event2 ~ x1 + x2 + x3 | x1 + x2 | x1 + x2) ##################### ## Hyperparameters ## ##################### ## Subject-specific frailty variance component ## - prior parameters for 1/theta ## theta.ab <- c(0.7, 0.7) ## Weibull baseline hazard function: alphas, kappas ## WB.ab1 <- c(0.5, 0.01) # prior parameters for alpha1 WB.ab2 <- c(0.5, 0.01) # prior parameters for alpha2 WB.ab3 <- c(0.5, 0.01) # prior parameters for alpha3 ## WB.cd1 <- c(0.5, 0.05) # prior parameters for kappa1 WB.cd2 <- c(0.5, 0.05) # prior parameters for kappa2 WB.cd3 <- c(0.5, 0.05) # prior parameters for kappa3 ## PEM baseline hazard function ## PEM.ab1 <- c(0.7, 0.7) # prior parameters for 1/sigma_1^2 PEM.ab2 <- c(0.7, 0.7) # prior parameters for 1/sigma_2^2 PEM.ab3 <- c(0.7, 0.7) # prior parameters for 1/sigma_3^2 ## PEM.alpha1 <- 10 # prior parameters for K1 PEM.alpha2 <- 10 # prior parameters for K2 PEM.alpha3 <- 10 # prior parameters for K3 ## MVN cluster-specific random effects ## Psi_v <- diag(1, 3) rho_v <- 100 ## DPM cluster-specific random effects ## Psi0 <- diag(1, 3) rho0 <- 10 aTau <- 1.5 bTau <- 0.0125 ## hyperParams <- list(theta=theta.ab, WB=list(WB.ab1=WB.ab1, WB.ab2=WB.ab2, WB.ab3=WB.ab3, WB.cd1=WB.cd1, WB.cd2=WB.cd2, WB.cd3=WB.cd3), PEM=list(PEM.ab1=PEM.ab1, PEM.ab2=PEM.ab2, PEM.ab3=PEM.ab3, PEM.alpha1=PEM.alpha1, PEM.alpha2=PEM.alpha2, PEM.alpha3=PEM.alpha3), MVN=list(Psi_v=Psi_v, rho_v=rho_v), DPM=list(Psi0=Psi0, rho0=rho0, aTau=aTau, bTau=bTau)) ################### ## MCMC SETTINGS ## ################### ## Setting for the overall run ## numReps <- 2000 thin <- 10 burninPerc <- 0.5 ## Settings for storage ## nGam_save <- 0 storeV <- rep(TRUE, 3) ## Tuning parameters for specific updates ## ## - those common to all models mhProp_theta_var <- 0.05 mhProp_Vg_var <- c(0.05, 0.05, 0.05) ## ## - those specific to the Weibull specification of the baseline hazard functions mhProp_alphag_var <- c(0.01, 0.01, 0.01) ## ## - those specific to the PEM specification of the baseline hazard functions Cg <- c(0.2, 0.2, 0.2) delPertg <- c(0.5, 0.5, 0.5) rj.scheme <- 1 Kg_max <- c(50, 50, 50) sg_max <- c(max(scrData$time1[scrData$event1 == 1]), max(scrData$time2[scrData$event1 == 0 & scrData$event2 == 1]), max(scrData$time2[scrData$event1 == 1 & scrData\$event2 == 1])) time_lambda1 <- seq(1, sg_max[1], 1) time_lambda2 <- seq(1, sg_max[2], 1) time_lambda3 <- seq(1, sg_max[3], 1) ## mcmc.WB <- list(run=list(numReps=numReps, thin=thin, burninPerc=burninPerc), storage=list(nGam_save=nGam_save, storeV=storeV), tuning=list(mhProp_theta_var=mhProp_theta_var, mhProp_Vg_var=mhProp_Vg_var, mhProp_alphag_var=mhProp_alphag_var)) ## mcmc.PEM <- list(run=list(numReps=numReps, thin=thin, burninPerc=burninPerc), storage=list(nGam_save=nGam_save, storeV=storeV), tuning=list(mhProp_theta_var=mhProp_theta_var, mhProp_Vg_var=mhProp_Vg_var, Cg=Cg, delPertg=delPertg, rj.scheme=rj.scheme, Kg_max=Kg_max, time_lambda1=time_lambda1, time_lambda2=time_lambda2, time_lambda3=time_lambda3)) ##################### ## Starting Values ## ##################### ## Sigma_V <- diag(0.1, 3) Sigma_V[1,2] <- Sigma_V[2,1] <- -0.05 Sigma_V[1,3] <- Sigma_V[3,1] <- -0.06 Sigma_V[2,3] <- Sigma_V[3,2] <- 0.07 ################################################################# ## Analysis of Independent Semi-Competing Risks Data ############ ################################################################# ############# ## WEIBULL ## ############# ## myModel <- c("semi-Markov", "Weibull") myPath <- "Output/01-Results-WB/" startValues <- initiate.startValues_HReg(form, scrData, model=myModel, nChain=2) ## fit_WB <- BayesID_HReg(form, scrData, id=NULL, model=myModel, hyperParams, startValues, mcmc.WB, path=myPath) fit_WB summ.fit_WB <- summary(fit_WB); names(summ.fit_WB) summ.fit_WB pred_WB <- predict(fit_WB, tseq=seq(from=0, to=30, by=5)) plot(pred_WB, plot.est="Haz") plot(pred_WB, plot.est="Surv") ######### ## PEM ## ######### ## myModel <- c("semi-Markov", "PEM") myPath <- "Output/02-Results-PEM/" startValues <- initiate.startValues_HReg(form, scrData, model=myModel, nChain=2) ## fit_PEM <- BayesID_HReg(form, scrData, id=NULL, model=myModel, hyperParams, startValues, mcmc.PEM, path=myPath) fit_PEM summ.fit_PEM <- summary(fit_PEM); names(summ.fit_PEM) summ.fit_PEM pred_PEM <- predict(fit_PEM) plot(pred_PEM, plot.est="Haz") plot(pred_PEM, plot.est="Surv") ################################################################# ## Analysis of Correlated Semi-Competing Risks Data ############# ################################################################# ################# ## WEIBULL-MVN ## ################# ## myModel <- c("semi-Markov", "Weibull", "MVN") myPath <- "Output/03-Results-WB_MVN/" startValues <- initiate.startValues_HReg(form, scrData, model=myModel, id, nChain=2) ## fit_WB_MVN <- BayesID_HReg(form, scrData, id, model=myModel, hyperParams, startValues, mcmc.WB, path=myPath) fit_WB_MVN summ.fit_WB_MVN <- summary(fit_WB_MVN); names(summ.fit_WB_MVN) summ.fit_WB_MVN pred_WB_MVN <- predict(fit_WB_MVN, tseq=seq(from=0, to=30, by=5)) plot(pred_WB_MVN, plot.est="Haz") plot(pred_WB_MVN, plot.est="Surv") ################# ## WEIBULL-DPM ## ################# ## myModel <- c("semi-Markov", "Weibull", "DPM") myPath <- "Output/04-Results-WB_DPM/" startValues <- initiate.startValues_HReg(form, scrData, model=myModel, id, nChain=2) ## fit_WB_DPM <- BayesID_HReg(form, scrData, id, model=myModel, hyperParams, startValues, mcmc.WB, path=myPath) fit_WB_DPM summ.fit_WB_DPM <- summary(fit_WB_DPM); names(summ.fit_WB_DPM) summ.fit_WB_DPM pred_WB_DPM <- predict(fit_WB_MVN, tseq=seq(from=0, to=30, by=5)) plot(pred_WB_DPM, plot.est="Haz") plot(pred_WB_DPM, plot.est="Surv") ############# ## PEM-MVN ## ############# ## myModel <- c("semi-Markov", "PEM", "MVN") myPath <- "Output/05-Results-PEM_MVN/" startValues <- initiate.startValues_HReg(form, scrData, model=myModel, id, nChain=2) ## fit_PEM_MVN <- BayesID_HReg(form, scrData, id, model=myModel, hyperParams, startValues, mcmc.PEM, path=myPath) fit_PEM_MVN summ.fit_PEM_MVN <- summary(fit_PEM_MVN); names(summ.fit_PEM_MVN) summ.fit_PEM_MVN pred_PEM_MVN <- predict(fit_PEM_MVN) plot(pred_PEM_MVN, plot.est="Haz") plot(pred_PEM_MVN, plot.est="Surv") ############# ## PEM-DPM ## ############# ## myModel <- c("semi-Markov", "PEM", "DPM") myPath <- "Output/06-Results-PEM_DPM/" startValues <- initiate.startValues_HReg(form, scrData, model=myModel, id, nChain=2) ## fit_PEM_DPM <- BayesID_HReg(form, scrData, id, model=myModel, hyperParams, startValues, mcmc.PEM, path=myPath) fit_PEM_DPM summ.fit_PEM_DPM <- summary(fit_PEM_DPM); names(summ.fit_PEM_DPM) summ.fit_PEM_DPM pred_PEM_DPM <- predict(fit_PEM_DPM) plot(pred_PEM_DPM, plot.est="Haz") plot(pred_PEM_DPM, plot.est="Surv") ## End(Not run)