summary.bayesCureModel {bayesCureRateModel}

Summary method.

Description

This function produces all summaries after fitting a cure rate model.

Usage

## S3 method for class 'bayesCureModel'
summary(object, burn = NULL, gamma_mix = TRUE, 
	K_gamma = 3,  K_max = 3, fdr = 0.1, 
	covariate_levels = NULL, yRange = NULL, alpha = 0.1, ...)

Arguments

object

An object of class bayesCureModel

burn

Positive integer corresponding to the number of mcmc iterations to discard as burn-in period

gamma_mix

Boolean. If TRUE, the density of the marginal posterior distribution of the \gamma parameter is estimated from the sampled MCMC values by fitting a normal mixture model.

K_gamma

Used only when gamma_mix = TRUE and corresponds to the number of normal mixture components used to estimate the marginal posterior density of the \gamma parameter.

K_max

Maximum number of components in order to cluster the (univariate) values of the joint posterior distribution across the MCMC run. Used to identify the main mode of the posterior distribution. See details.

fdr

The target value for controlling the False Discovery Rate when classifying subjects as cured or not.

covariate_levels

Optional levels for the covariates. It is only required when the user wishes to obtain a vector with the estimated posterior cured probabilities for a given combination of covariates. Include the value “1” in the case where the model contains constant term.

yRange

Optional range (a vector of two non-negative values) for computing the sequence of posterior probabilities for the given values in covariate_levels.

alpha

Scalar between 0 and 1 corresponding to 1 - confidencel level for computing Highest Density Intervals. If set to NULL, the confidence intervals are not computed.

...

ignored.

Details

The values of the posterior draws are clustered according to a (univariate) normal mixture model, and the main mode corresponds to the cluster with the largest mean. The maximum number of mixture components corresponds to the K_max argument. The mclust library is used for this purpose. The inference for the latent cure status of each (censored) observation is based on the MCMC draws corresponding to the main mode of the posterior distribution. The FDR is controlled according to the technique proposed in Papastamoulis and Rattray (2018).

In case where covariate_levels is set to TRUE, the summary function also returns a list named p_cured_output with the following entries

mcmc

It is returned only in the case where the argument covariate_values is not NULL. A vector of posterior cured probabilities for the given values in covariate_values, per retained MCMC draw.

map

It is returned only in the case where the argument covariate_values is not NULL. The cured probabilities computed at the MAP estimate of the parameters, for the given values covariate_values.

tau_values

tau values

covariate_levels

covariate levels

index_of_main_mode

the subset of MCMC draws allocated to the main mode of the posterior distribution.

Value

A list with the following entries

map_estimate

Maximum A Posteriori (MAP) estimate of the parameters of the model.

highest_density_intervals

Highest Density Interval per parameter

latent_cured_status

Estimated posterior probabilities of the latent cure status per censored subject.

cured_at_given_FDR

Classification as cured or not, at given FDR level.

p_cured_output

It is returned only in the case where the argument covariate_values is not NULL. See details.

main_mode_index

The retained MCMC iterations which correspond to the main mode of the posterior distribution.

Author(s)

Panagiotis Papastamoulis

References

Papastamoulis and Milienos (2023). Bayesian inference and cure rate modeling for event history data. arXiv:2310.06926.

Papastamoulis and Rattray (2018). A Bayesian Model Selection Approach for Identifying Differentially Expressed Transcripts from RNA Sequencing Data, Journal of the Royal Statistical Society Series C: Applied Statistics, Volume 67, Issue 1.

Scrucca L, Fraley C, Murphy TB, Raftery AE (2023). Model-Based Clustering, Classification, and Density Estimation Using mclust in R. Chapman and Hall/CRC. ISBN 978-1032234953

See Also

cure_rate_MC3

Examples

# simulate toy data just for cran-check purposes        
        set.seed(1)
        n = 4
        stat = rbinom(n, size = 1, prob = 0.5)
        x <- cbind(1, matrix(rnorm(2*n), n, 2))
        y <- rexp(n)
	fit1 <- cure_rate_MC3(y = y, X = x, Censoring_status = stat, 
		promotion_time = list(distribution = 'exponential'),
		nChains = 2, nCores = 1, 
		mcmc_cycles = 3, sweep = 2)
	mySummary <- summary(fit1, burn = 0)