Package {ambs}

Fit a Bayesian Alpha-Mixture Survival Model

Description

Fits a right-censored Bayesian alpha-mixture survival model by Metropolis-Hastings within Gibbs sampling.

Usage

alpmixsurv(
  time,
  status,
  model = c("ww", "gw", "ll"),
  mcmc = list(nburn = 5000, nsamp = 5000, thin = 1),
  prior = NULL,
  ident = c("auto", "relabel", "none"),
  init = NULL
)

Arguments

Details

Alpha is always estimated as a continuous parameter. At alpha equal to zero and numerically near zero, the likelihood is evaluated stably using the geometric-mixture limit or its continuous expansion. Posterior draws are not rounded or forced to zero. The fitted object reports Pr(|alpha| < 0.01 | data) as a near-zero interval probability; this is not a posterior point mass at alpha equal to zero.

Relabeling resolves label switching but does not remove structural non-identifiability. The default assessment also checks component overlap, near-boundary mixture weights, and known model-specific degeneracies. Detected problems are recorded in the fitted object, while summary() reports the overall identifiability status.

One chain is run by default for a fast initial fit. Single-chain effective sample size is reported, but convergence is marked as not assessed because R-hat requires multiple chains. Use mcmcdiag() to run three additional chains and obtain four-chain split R-hat and effective sample-size diagnostics. The default proposals follow the two-component unknown-shape simulations: alpha starts at zero with a normal random-walk standard deviation of 0.05, the mixture weight starts at 0.5 and is updated on the logit scale, positive shape parameters start at 2 and are updated on the log scale, and Weibull scale parameters use relative-variance gamma random walks, whose conditional variance is proportional to the square of the current parameter value. Positive shape parameters are updated by symmetric normal random walks on the log-parameter scale, using posterior targets expressed on that transformed scale. Every proposal scale is adapted in 50-iteration batches during burn-in toward an acceptance rate of 0.44 and is then frozen before retained sampling. Sampling acceptance rates are reported separately from burn-in acceptance rates. Internal sampling diagnostics identify extreme acceptance, low one-chain ESS, non-finite draws, or extreme parameter tails; multi-chain assessment with mcmcdiag() remains recommended. For burn-in of at least 100 iterations, the first half of burn-in is also used to select the two transformed parameters most correlated with alpha. A three-dimensional normal random walk for alpha and those parameters is then adapted during the remainder of burn-in and frozen for retained sampling. The original scalar updates remain in place.

WW and GW estimation is performed internally using time / median(time). Their default scale-parameter priors and automatic initial values are defined on this standardized scale. Theta draws, automatic initial values, likelihoods, and model criteria are transformed back to the original time unit before being returned. User-specified theta priors and initial values retain their original-scale interpretation through exact internal transformation. This makes the default computation invariant to whether time is recorded in days, months, or years.

For Lognormal-Lognormal, the corresponding fixed first-chain values are mu1 = 0, mu2 = -log(2), and sig1 = sig2 = 1. The location parameters use normal random walks with standard deviation 0.10, and positive standard deviations use log-scale random walks with standard deviation 0.10. LL estimation is performed internally on (log(time) - mean(log(time))) / sd(log(time)). The default working-scale priors are exchangeable, with normal location priors and sigma^2 ~ inverse-gamma(0.5, 0.01). This proper heavy-tailed prior allows both narrow and diffuse components. Posterior draws, initial values, likelihoods, and model criteria are returned on the original time scale. User-specified LL priors and initial values are transformed internally and retain their original-scale interpretation.

Value

An object of class alpmixsurv containing posterior draws, acceptance rates, model criteria, the normalized prior, and the supplied data. The object also contains alpha_near_zero_band and alpha_near_zero_probability for assessing proximity to the geometric mixture. The posterior_raw and identifiability elements retain the unmodified draws and the identification assessment, respectively. Chain-level draws, initial values, convergence status, calibration and warm-up acceptance rates, and final proposal scales are also retained. The block_proposals element records the selected transformed parameters, frozen covariance and scale, and warm-up and retained-sample acceptance rates.

Examples

set.seed(1)
data(veteran_alpmix)
fit <- alpmixsurv(
  time = veteran_alpmix$time,
  status = veteran_alpmix$status,
  model = "ll", mcmc = list(nburn = 50, nsamp = 100, thin = 1)
)
summary(fit)

Plot a Fitted Alpha-Mixture Curve

Description

Evaluates and plots a survival, hazard, or density curve from a fitted alpha-mixture model.

Usage

getcurve(
  object, type = c("s", "h", "d"), t = NULL, col = "black",
  add = FALSE, lwd = 1, lty = 1, xlab = "Time", ylab = NULL,
  main = NULL, level = 0.95, estimate = c("median", "mean"), ...
)

Arguments

Details

The curve is evaluated for every posterior draw and summarized pointwise with a credible band. At alpha equal to zero, the geometric-mixture limit is used consistently with the fitted likelihood.

Value

Invisibly, a data frame containing time, posterior estimate, lower limit, and upper limit.

Run Multi-Chain Diagnostics for an Alpha-Mixture Fit

Description

Runs three additional chains from dispersed automatic initial values and combines them with the original chain for convergence assessment.

Usage

mcmcdiag(object, seed = NULL)

Arguments

Details

The initial alpmixsurv() fit uses one chain for speed and therefore reports convergence as not assessed. This function adds three chains using the same data, model, priors, and MCMC settings. It then recomputes posterior summaries, identifiability diagnostics, model criteria, split R-hat, and effective sample size. The input object is not modified.

Value

An updated alpmixsurv object containing four chains and multi-chain convergence diagnostics.

Examples

set.seed(1)
fit <- alpmixsurv(
  c(0.5, 1, 1.5, 2), c(1, 1, 0, 1), model = "ww",
  mcmc = list(nburn = 10, nsamp = 20)
)
fit4 <- mcmcdiag(fit, seed = 2)
fit4$convergence$status

Plot Posterior Samples

Description

Draws a trace plot or histogram for one retained posterior parameter. Trace plots display all available chains separately after mcmcdiag() has been run.

Usage

plotmcmc(object, param, type = c("line", "hist"), ...)

Arguments

Value

Invisibly, the plotted posterior sample vector.

Veterans' Administration Lung Cancer Trial Data

Description

A right-censored survival dataset from a randomized trial of two treatments for male patients with advanced, inoperable lung cancer. This bundled copy is provided as a directly runnable example for alpha-mixture survival models.

Usage

data(veteran_alpmix)

Format

A data frame with 137 observations and 8 variables:

trt

Treatment code: 1 for standard and 2 for test treatment.

celltype

Tumor cell type: squamous, small-cell, adenocarcinoma, or large-cell.

time

Survival or censoring time in days.

status

Event indicator: 1 for death and 0 for right censoring.

karno

Karnofsky performance score.

diagtime

Months from diagnosis to randomization.

age

Age in years.

prior

Prior therapy code: 0 for none and 10 for prior therapy.

Source

Veterans' Administration Lung Cancer Trial data distributed with the survival R package as veteran.

Examples

data(veteran_alpmix)

set.seed(123)
fit <- alpmixsurv(
  veteran_alpmix$time,
  veteran_alpmix$status,
  model = "ll",
  mcmc = list(nburn = 50, nsamp = 100, thin = 1)
)
summary(fit)