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.
aftsrr(formula, data, subset, id = NULL, contrasts = NULL, weights = NULL, B = 100, rankWeights = c("gehan", "logrank", "pw", "gp", "userdefined"), eqType = c("is", "ns", "mns", "mis"), se = c("NULL", "bootstrap", "MB", "ZLCF", "ZLMB", "sHCF", "sHMB", "ISCF", "ISMB"), control = list())
formula  a formula expression, of the form 

data  an optional data frame in which to interpret the variables
occurring in the 
subset  an optional vector specifying a subset of observations to be used in the fitting process. 
id  an optional vector used to identify the clusters.
If missing, then each individual row of 
contrasts  an optional list. 
weights  an optional vector of observation weights. 
B  a numeric value specifies the resampling number. When B = 0, only the beta estimate will be displayed. 
rankWeights  a character string specifying the type of general weights. The following are permitted:

eqType  a character string specifying the type of the estimating equation used to obtain the regression parameters. The following are permitted:

se  a character string specifying the estimating method for the variancecovariance matrix. The following are permitted:

control  controls equation solver, maxiter, tolerance, and resampling variance estimation.
The available equation solvers are 
aftsrr
returns an object of class "aftsrr
" representing the fit.
An object of class "aftsrr
" is a list containing at least the following components:
A vector of beta estimates
A list of covariance estimates
An integer code indicating type of convergence.
When variance = "MB"
, bhist
gives the bootstrap samples.
Chiou, S., Kang, S. and Yan, J. (2014) Fast Accelerated Failure Time Modeling for CaseCohort Data. Statistics and Computing, 24(4): 559568.
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): 123.
Huang, Y. (2002) Calibration Regression of Censored Lifetime Medical Cost. Journal of American Statistical Association, 97, 318327.
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): 126
Zeng, D. and Lin, D. Y. (2008) Efficient Resampling Methods for Nonsmooth Estimating Functions. Biostatistics, 9, 355363
## kidney data library(survival) data(kidney) foo < aftsrr(Surv(time, status) ~ age + sex, id = id, data = kidney, se = c("ISMB", "ZLMB"), B = 10) foo#> Call: #> aftsrr(formula = Surv(time, status) ~ age + sex, data = kidney, #> id = id, B = 10, se = c("ISMB", "ZLMB")) #> #> Coefficients: #> age sex #> 0.001237104 1.522001851## 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, se = c("ISMB", "ZLMB"), subset = stage == 4) summary(fit)#> Call: #> aftsrr(formula = Surv(edrel, rel) ~ histol + age12 + study, data = sub, #> subset = stage == 4, id = seqno, weights = hi, B = 10, se = c("ISMB", #> "ZLMB")) #> #> Variance Estimator: ISMB #> Estimate StdErr z.value p.value #> histol 4.318 1.250 3.453 0.001 *** #> age12 0.051 0.250 0.206 0.837 #> study 1.794 1.808 0.993 0.321 #>  #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Variance Estimator: ZLMB #> Estimate StdErr z.value p.value #> histol 4.318 0.413 10.448 <2e16 *** #> age12 0.051 0.127 0.406 0.685 #> study 1.794 0.760 2.362 0.018 * #>  #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1