svycoxph               package:survey               R Documentation

_S_u_r_v_e_y-_w_e_i_g_h_t_e_d _C_o_x _m_o_d_e_l_s.

_D_e_s_c_r_i_p_t_i_o_n:

     Fit a proportional hazards model to data from a complex survey
     design, with inverse-probability weighting and with standard
     errors corrected for cluster sampling.

_U_s_a_g_e:

     svycoxph(formula, design,subset=NULL, ...)

_A_r_g_u_m_e_n_t_s:

 formula: Model formula. Any 'cluster()' terms will be ignored.

  design: 'survey.design' object. Must contain all variables in the
          formula

  subset: Expression to select a subpopulation

     ...: Other arguments passed to 'coxph'. 

_D_e_t_a_i_l_s:

     The main difference between this function and the 'robust=TRUE'
     option to 'coxph' in the survival package is that this function
     accounts for the reduction in variance from stratified sampling
     and the increase in variance from having only a small number of
     clusters.

     Note that 'strata' terms in the model formula describe subsets
     that have a separate baseline hazard function and need not have
     anything to do with the stratification of the sampling.

_V_a_l_u_e:

     An object of class 'svycoxph'.

_A_u_t_h_o_r(_s):

     Thomas Lumley

_R_e_f_e_r_e_n_c_e_s:

     Binder DA. (1992) Fitting Cox's proportional hazards models from
     survey data. Biometrika 79: 139-147

_S_e_e _A_l_s_o:

     'svydesign', 'coxph', 'svyCprod'

_E_x_a_m_p_l_e_s:

     ## Somewhat unrealistic example of nonresponse bias.
     data(pbc, package="survival")

     biasmodel<-glm(I(trt>0)~age*edema,data=pbc)
     pbc$randprob<-fitted(biasmodel)

     dpbc<-svydesign(id=~1, prob=~randprob, strata=~edema, data=subset(pbc,trt>0))
     rpbc<-as.svrepdesign(dpbc)

     svycoxph(Surv(time,status)~log(bili)+protime+alb,design=dpbc)

     svycoxph(Surv(time,status)~log(bili)+protime+alb,design=rpbc)

