frailty               package:survival               R Documentation

(_A_p_p_r_o_x_i_m_a_t_e) _F_r_a_i_l_t_y _m_o_d_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     When included in a coxph or survreg, fits by penalised likelihood
     a random effects (frailty) model. 'frailty' is generic, with
     methods for t, Gaussian and Gamma distributions.

_U_s_a_g_e:

     frailty(x, distribution="gamma", ...)
     frailty.gamma(x, sparse = (nclass > 5), theta, df, eps = 1e-05, method = c("em","aic", "df", "fixed"), ...) 
     frailty.gaussian(x, sparse = (nclass > 5), theta, df, method = c("reml","aic", "df", "fixed"), ...)
     frailty.t(x, sparse = (nclass > 5), theta, df, eps = 1e-05, tdf = 5,method = c("aic", "df", "fixed"), ...)

_A_r_g_u_m_e_n_t_s:

       x: group indicator

distribution: frailty distribution 

     ...: Arguments for specific distribution, including (but not
          limited to) 

  sparse: Use sparse Newton-Raphson algorithm

      df: Approximate degrees of freedom

   theta: Penalty

     eps: Accuracy of 'df'

  method: maximisation algorithm

     tdf: df of t-distribution

_D_e_t_a_i_l_s:

     The penalised likelihood method is equivalent to maximum (partial)
     likelihood for the gamma frailty but not for the others.

     The sparse algorithm uses the diagonal of the information matrix
     for the random effects, which saves a lot of space. 

     The frailty distributions are really the log-t and lognormal: t
     and Gaussian are random effects on the scale of the linear
     predictor.

_V_a_l_u_e:

     An object of class 'coxph.penalty' containing a factor with
     attributes specifying the control functions.

_R_e_f_e_r_e_n_c_e_s:

     Therneau TM, Grambsch PM, Pankratz VS (2003) "Penalized survival
     models and frailty" Journal of Computational and Graphical
     Statistics 12, 1: 156-175

_S_e_e _A_l_s_o:

     'coxph','survreg','ridge','pspline'

_E_x_a_m_p_l_e_s:

     kfit <- coxph(Surv(time, status)~ age + sex + disease + frailty(id), kidney)
     kfit0 <- coxph(Surv(time, status)~ age + sex + disease, kidney)
     kfitm1 <- coxph(Surv(time,status) ~ age + sex + disease + 
                     frailty(id, dist='gauss'), kidney)
     coxph(Surv(time, status) ~ age + sex + frailty(id, dist='gauss', method='aic',caic=TRUE), kidney)
     # uncorrected aic
     coxph(Surv(time, status) ~ age + sex + frailty(id, method='aic', caic=FALSE), kidney)

     rfit2a <- survreg(Surv(time, status) ~ rx +
                       frailty.gaussian(litter, df=13, sparse=FALSE), rats )
     rfit2b <- survreg(Surv(time, status) ~ rx +
                       frailty.gaussian(litter, df=13, sparse=TRUE), rats )
     rfit2a
     rfit2b