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README.md

Project Status: Active – The project has reached a stable, usable state and is being actively developed. minimal R version CRAN_Status_Badge packageversion Travis-CI Build Status AppVeyor Build Status Last-changedate


aftgee


aftgee: Accelerated Failure Time with Generalized Estimating Equations

The aftgee package implements recently developed inference procedures for the accelerated failure time (AFT) models with both the rank-based approach and the least squares approach. For the rank-based approach, the package allows various weight choices and uses an induced smoothing procedure that leads to much more efficient computation than the linear programming method. With the rank-based estimator as an initial value, the generalized estimating equation (GEE) approach is used as an extension of the least squares approach to the multivariate case, where the within cluster dependency is accounted for with working covariance structures. Additional sampling weights are incorporated to handle missing data needed as in case-cohort studies or general sampling schemes.

Installation

Install and load the package from CRAN using

install.packages("aftgee")
library(aftgee)

Install and load the package from GitHub using

devtools::install_github("stc04003/aftgee")
library(aftgee)

Online documentation

Online document.

  • Package vignette coming up.

Examples

Here are some examples to get started:

library(survival)
data(kidney)

library(aftgee)
set.seed(123)
fit.rk <- aftsrr(Surv(time, status) ~ age + sex, id = id, data = kidney, se = c("ISMB", "ZLMB"))
summary(fit.rk)
#> Call:
#> aftsrr(formula = Surv(time, status) ~ age + sex, data = kidney, 
#>     id = id, se = c("ISMB", "ZLMB"))
#> 
#> Variance Estimator: ISMB
#>     Estimate StdErr z.value p.value    
#> age   -0.001  0.015  -0.085   0.932    
#> sex    1.522  0.419   3.631  <2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Variance Estimator: ZLMB
#>     Estimate StdErr z.value p.value    
#> age   -0.001  0.019  -0.066   0.947    
#> sex    1.522  0.458   3.320   0.001 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.ge <- aftgee(Surv(time, status) ~ age + sex, id = id, data = kidney)
summary(fit.ge)
#> Call:
#> aftgee(formula = Surv(time, status) ~ age + sex, data = kidney, 
#>     id = id)
#> 
#> AFTGEE Estimator
#>             Estimate StdErr z.value p.value    
#> (Intercept)    2.071  0.659   3.141   0.002 ** 
#> age           -0.005  0.010  -0.528   0.597    
#> sex            1.374  0.405   3.393   0.001 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Reference

Chiou, S., Kang, S., and Yan, J. (2015). Rank-based estimating equations with general weight for accelerated failure time models: An induced smoothing approach. Statistics in Medicine, 34(9): 1495--1510.

Chiou, S., Kang, S., and Yan, J. (2014). Fitting accelerated failure time model in routine survival analysis with R package aftgee. Journal of Statistical Software, 61(11): 1--23.

Chiou, S., Kang, S., and Yan, J. (2014). Fast accelerated failure time modeling for case-cohort data. Statistics and Computing, 24(4): 559--568.

Chiou, S., Kang, S., Kim, J., and Yan, J. (2014). Marginal semiparametric multivariate accelerated failure time model with generalized estimating equations. Lifetime Data Analysis, 20(4): 599--618.

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