Use the empirical likelihood ratio and Wilks theorem to test if the regression coefficient is equal to beta, based on the rank estimator/estimating equation of the AFT model.
The log empirical likelihood been maximized is the hazard empirical likelihood.
1  RankRegTestH(y, d, x, beta, type="Gehan")

y 
a vector of length N, containing the censored responses. 
d 
a vector (length N) of either 1's or 0's. d=1 means y is uncensored; d=0 means y is right censored. 
x 
a matrix of size N by q. 
beta 
a vector of length q. the value of the regression coefficient to be tested in the model y_i = β x_i + ε_i 
.
type 
default to Gehan type. The other option is Logrank type. 
The estimator of beta can be obtained by function
rankaft( )
in the package rankreg
. But here you may test other values of
beta. If you test the beta value that is obtained from the rankaft( )
,
then the 2LLR should be 0 and the pvalue should be 1.
The above likelihood should be understood as the likelihood of the error term, so in the regression model the error e_i should be iid.
The estimating equation used when maximize the empirical likelihood is
0 = ∑_i R(e_i) φ (e_i) d_i Δ A(e_i) (x_i  \bar x_i )
where all notation was described in detail in the references below.
A list with the following components:
"2LLR" 
the 2 loglikelihood ratio; should have approximate chisq distribution under H_0. 
logel2 
the log empirical likelihood, under estimating equation. 
logel 
the log empirical likelihood of the KaplanMeier of e's. 
Mai Zhou
Zhou, M. (2016) Empirical Likelihood Methods in Survival Analysis. CRC Press.
Kalbfleisch, J. and Prentice, R. (2002) The Statistical Analysis of Failure Time Data. 2nd Ed. Wiley, New York. (Chapter 7)
Jin, Z., Lin, D.Y., Wei, L. J. and Ying, Z. (2003). Rankbased inference for the accelerated failure time model. Biometrika, 90, 34153.
Zhou, M. (2005). Empirical likelihood analysis of the rank estimator for the censored accelerated failure time model. Biometrika, 92, 492–498.
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