Fits a semiparametric accelerated failure time (AFT) model with
rankbased approach.
General weights, additional sampling weights and fast sandwich
variance estimations are also incorporated.
Estimating equations are solved with BarzilarBorwein spectral method
implemented as BBsolve
in package BB.
1 2 3 4 5 
formula 
a formula expression, of the form 
data 
an optional data frame in which to interpret the variables
occurring in the 
id 
an optional vector used to identify the clusters.
If missing, then each individual row of 
subset 
an optional vector specifying a subset of observations to be used in the fitting process. 
contrasts 
an optional list. 
strata 
a vector which identifies strata. This can also be used to distinct casecohort sampling and stratified sampling. 
weights 
an optional vector of observation weights. 
rankWeights 
a character string specifying the type of general
weights.
The following are permitted:

method 
a character string specifying the methods to estimate
the regression parameter.
The following are permitted:

variance 
a character string specifying the covariance estimating method.
The following are permitted:

B 
a numeric value specifies the resampling number. When B = 0, only the beta estimate will be displayed. 
SigmaInit 
the initial covariance matrix; default at identity matrix. 
control 
controls maxiter and tolerance. 
aftsrr
returns an object of class "aftsrr" representing
the fit.\
An object of class "aftsrr" is a list containing at least the
following components:
beta 
A vector of beta estimates 
covmat 
A list of covariance estimates 
convergence 
An integer code indicating type of convergence. 0
indicates successful convergence. Error codes are 1 indicates that
the iteration limit 
bhist 
When 
Sy Han Chiou, Sangwook Kang, Jun Yan.
Chiou, S., Kang, S. and Yan, J. (2014) Fast Accelerated Failure Time Modeling for CaseCohort Data. Statistics and Computing, 24(4): 559–568
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
Huang, Y. (2002) Calibration Regression of Censored Lifetime Medical Cost. Journal of American Statistical Association, 97, 318–327
Johnson, L. M. and Strawderman, R. L. (2009) Induced Smoothing for the Semiparametric Accelerated Failure Time Model: Asymptotic and Extensions to Clustered Data. Biometrika, 96, 577 – 590
Varadhan, R. and Gilbert, P. (2009) BB: An R Package for Solving a Large System of Nonlinear Equations and for Optimizing a HighDimensional Nonlinear Objective Function. Journal of Statistical Software, 32(4): 1–26
Zeng, D. and Lin, D. Y. (2008) Efficient Resampling Methods for Nonsmooth Estimating Functions. Biostatistics, 9, 355–363
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  #### kidney data
library(survival)
data(kidney)
foo < aftsrr(Surv(time, status) ~ age + sex  1, id = id,
data = kidney, variance = c("ISMB", "ZLMB"), B = 10)
foo
#### nwtco data
library(survival)
data(nwtco)
subinx < sample(1:nrow(nwtco), 668, replace = FALSE)
nwtco$subcohort < 0
nwtco$subcohort[subinx] < 1
pn < table(nwtco$subcohort)[[2]] / sum(table(nwtco$subcohort))
nwtco$hi < nwtco$rel + ( 1  nwtco$rel) * nwtco$subcohort / pn
nwtco$age12 < nwtco$age / 12
nwtco$study < nwtco$study  3
nwtco$histol = nwtco$histol  1
sub < nwtco[subinx,]
fit < aftsrr(Surv(edrel, rel) ~ histol + age12 + study, id = seqno,
weights = hi, data = sub, B = 10, variance = c("ISMB", "ZLMB"),
subset = stage == 4)
summary(fit)

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