lgstbymv {afthd}

Multivariate estimates of AFT model with log logistic distribution using MCMC.

Description

Provides estimate of AFT model with log logistic distribution using MCMC for multivariable (maximum 5 covariates of column at a time) in high dimensional gene expression data. It also deals covariates with missing values.

Usage

lgstbymv(m, n, STime, Event, nc, ni, data)

Arguments

m

Starting column number of covariates of study from high dimensional entered data.

n

Ending column number of covariates of study from high dimensional entered data.

STime

name of survival time in data.

Event

name of event in data. 0 is for censored and 1 for occurrence of event.

nc

number of MCMC chain.

ni

number of MCMC iteration to update the outcome.

data

High dimensional gene expression data that contains event status, survival time and and set of covariates.

Details

This function deals covariates (in data) with missing values. Missing value in any column (covariate) is replaced by mean of that particular covariate. AFT model is log-linear regression model for survival time T_1, T_{2},..,T_{n}. i.e.,

log(T_i)= x_i'\beta +\sigma\epsilon_i ;~\epsilon_i \sim F_\epsilon (.)~which~is~iid

Where F_\epsilon is known cdf which is defined on real line. When baseline distribution is logistic then T follows log logistic distribution.

T \sim Log-Logis(x'\beta,\sqrt{\tau)}

Value

Data frame is containing mean, sd, n.eff, Rhat and credible intervals (2.5%, 25%, 50%, 75% and 97.5%) for beta's, sigma, tau and deviance of the model for the selected covariates. beta[1] is for intercept and others are for covariates (which is/are chosen as columns in data). sigma is the scale parameter of the distribution.

Author(s)

Atanu Bhattacharjee, Gajendra Kumar Vishwakarma and Pragya Kumari

References

Prabhash et al(2016) <doi: 10.21307/stattrans-2016-046>

See Also

wbysmv, lgnbymv, lgstbyuni

Examples

##
data(hdata)
lgstbymv(10,12,STime="os",Event="death",5,100,hdata)
##