weightedKM            package:risksetROC            R Documentation

_I_n_c_i_d_e_n_t/_D_y_n_a_m_i_c (_I/_D) _R_O_C _c_u_r_v_e, _A_U_C _a_n_d _i_n_t_e_g_r_a_t_e_d _A_U_C (_i_A_U_C)
_e_s_t_i_m_a_t_i_o_n _o_f _c_e_n_s_o_r_e_d _s_u_r_v_i_v_a_l _d_a_t_a

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

     This function estimates S(t) where sampling weights are permitted.

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

     weightedKM(Stime, status, wt=NULL, entry=NULL) 

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

   Stime: Survival times when right censored data is considered. In
          case of interval censored data this is the end point for the
          time interval.

  status: Survival status

      wt: weight, default is unweighted

   entry: entry times in case of interval censored data, default is
          _NULL_ when right censored data is considered

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

     This function obtains survival function estimate where sampling
     weights are permitted.

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

     Returns a list of following items: 

    time: ordered unique failure times

survival: survival estimate at the unique failure times

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

     Patrick J. Heagerty

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

     Heagerty, P.J., Zheng Y. (2005) Survival Model Predictive Accuracy
     and ROC curves _Biometrics_, *61*, 92 - 105

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

     data(pbc)
     ## considering only randomized patients
     pbc1 <- pbc[1:312,]
     ## create new censoring variable combine 0,1 as 0, 2 as 1
     survival.status <- ifelse( pbc1$status==2, 1, 0)
     survival.time <- pbc1$fudays
     kout <- weightedKM(Stime=survival.time, status=survival.status)
     KM.plot(kout$time,kout$survival)

