dependAFT.reg: Semiparametric Inference for the AFT regression Model with... In depend.truncation: Statistical Methods for the Analysis of Dependently Truncated Data

Description

Regression estimation for the AFT regression model based on left-truncated and right-censored data (Emura & Wang 2016). The dependency of truncation on lifetime is modeled through the AFT regression form.

Usage

 1 2 dependAFT.reg(t.trunc, y.trunc, d, x1.trunc, initial = c(0, 0), LY = FALSE, beta1_low = -0.2, beta1_up = 0.2, alpha = 1, epsilon = 1/50)

Arguments

 t.trunc vector of left-truncation variables satisfying t.trunc<=y.trunc y.trunc vector of lifetime variables satisfying t.trunc<=y.trunc d vector of censoring indicators x1.trunc vector of 1-dimensional covariates initial a pair of initial values for (beta, gamma) LY Lai and Ying's estimator for initial values beta1_low lower bound for beta beta1_up upper bound for beta alpha some tuning parameter for optimization, alpha=1 is default epsilon some tuning parameter for kernel methods

Details

Only the univariate regression (only one covariate) is allowed.

Value

 beta inference results for beta gamma inference results for gamma beta_LY the estimator of Lai & Ying (1991) S2_Minimum minimum of the objective function detail detailed results for minimizing the estimating objective function "optim"

Takeshi Emura

References

Emura T, Wang W (2016), Semiparametric Inference for an Accelerated Failure Time Model with Dependent Truncation, Ann Inst Stat Math 68 (5): 1073-94.

Lai TL, Ying Z (1991), Rank Regression Methods for Left-Truncated and Right-Censored Data. Annals of Statistics 19: 531-556.

Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 y.trunc=c( -0.52, 0.22, -1.42, 0.05, 0.32, -1.02, -0.47, 0.10, -0.38, -0.18, 0.97, 0.04, -0.10, 0.50, 0.57, -0.80, -0.24, 0.07, -0.04, 0.88, -0.52, -0.28, -0.55, 0.53, 0.99, -0.52, -0.59, -0.48, -0.07, 0.20, -0.34, 1.00, -0.52) t.trunc=c( -2.05, -0.25, -2.43, -0.32, -0.27, -1.06, -0.95, -0.82, -0.66, -0.28, -1.14, -0.32, -1.19, -2.18, -0.45, -1.71, -0.84, -1.93, -1.04, -2.58, -1.97, -2.15, -0.59, -0.74, -1.26, -2.57, -2.40, -2.22, -1.52, -0.21, -1.50, -1.99, -1.79) d=c(1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,1) x1.trunc=c( 0.27, 0.66, 0.77, 0.21, 0.48, 0.11, 0.69, 0.32, 0.33, 0.43, 0.12, 0.60, 0.13, 0.43, 0.99, 0.21, 0.93, 0.60, 0.45, 0.41, 0.86, 0.90, 0.76, 0.93, 0.27, 0.13, 0.82, 0.17, 0.63, 0.31, 0.13, 0.48, 0.32) ### Data analysis in Emura & Wang (2016) ### # dependAFT.reg(t.trunc,y.trunc,d,x1.trunc,alpha=2,LY=TRUE,beta1_low=-5,beta1_up=5) dependAFT.reg(t.trunc,y.trunc,d,x1.trunc,LY=FALSE,beta1_low=-5,beta1_up=5) #### Channing hourse data analysis; Section 5 of Emura & Wang (2016) ##### # library(KMsurv) # data(channing) # y.trunc=log(channing\$age) # t.trunc=log(channing\$ageentry) # d=channing\$death # x1.trunc=as.numeric(channing\$gender==1) # dependAFT.reg(t.trunc,y.trunc,d,x1.trunc,beta1_low=-0.2,beta1_up=0.2) # dependAFT.reg(t.trunc,y.trunc,d,x1.trunc,LY=TRUE,alpha=2,beta1_low=-0.2,beta1_up=0.2)

depend.truncation documentation built on March 18, 2018, 1:51 p.m.