rs.br                package:relsurv                R Documentation

_T_e_s_t _t_h_e _P_r_o_p_o_r_t_i_o_n_a_l _H_a_z_a_r_d_s _A_s_s_u_m_p_t_i_o_n _f_o_r _R_e_l_a_t_i_v_e _S_u_r_v_i_v_a_l
_R_e_g_r_e_s_s_i_o_n _M_o_d_e_l_s

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

     Test the proportional hazards assumption for relative survival 
     models  ('rsadd', 'rsmul' or 'rstrans') by forming a Brownian
     Bridge.

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

     rs.br(fit,sc,rho=0,test="max",global=TRUE)

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

     fit: the result of fitting a relative survival model, using the
          'rsadd', 'rsmul' or 'rstrans' function.  

      sc: partial residuals calculated by the 'resid' function. This is
          used to save time if several tests are to be calculated on
          these residuals and can otherwise be omitted. 

     rho: a number controlling the weigths of residuals. The weights
          are the number of individuals at risk at each event time to
          the power 'rho'. The default is 'rho=0', which sets all
          weigths to 1. 

    test: a character string specifying the test to be performed on
          Brownian bridge. Possible values are '"max"' (default), which
          tests the maximum absolute value of the  bridge, and 'cvm',
          which calculates the Cramer Von Mises statistic. 

  global: should a global Brownian bridge test be performed, in
          addition to the per-variable tests

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

     an object of class 'rs.br'. This function would usually be
     followed by both a print and a plot of the result. The plot gives
     a Brownian bridge for each of the variables. The horizontal lines
     are the 95 of the Brownian bridge

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

     Stare J., Pohar M., Henderson R. "Goodness of fit for relative
     survival models." _Statistics in Medicine_. To appear in 2005.

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

     'rsadd', 'rsmul', 'rstrans', 'resid'.

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

     data(slopop)
     data(rdata)
     fit <- rsadd(Surv(time,cens)~sex+ratetable(age=age*365,sex=sex,year=year),
            ratetable=slopop,data=rdata,int=5)
     rsbr <- rs.br(fit)
     print(rsbr)
     plot(rsbr)

