SchoenSmooth           package:risksetROC           R Documentation

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

     This function smooths the Schoenfeld residuals using
     Epanechnikov's optimal kernel.

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

     SchoenSmooth(fit, Stime, status, span=0.40, order=0, entry=NULL)

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

     fit: the result of fitting a Cox regression model, using the coxph
          function

   Stime: Survival times in case of right censored data and exit time
          for left truncated data

  status: Survival status

    span: bandwidth parameter that controls the size of a local
          neighborhood

   order: 0 or 1, locally mean if 0 and local linear if 1

   entry: entry time when left censored data is considered, default is
          NULL for only right censored data

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

     This function smooths the Schoenfeld residuals to get an estimate
     of time-varying effect of the marker using Epanechnikov's optimal
     kernel using either local mean or local linear smoother.

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

     Returns a list of following items: 

    time: failure times

    beta: estimate of time-varying parameter beta(t) at each unique
          failure time. 

_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
     pbc1$status1 <- survival.status
     fit <- coxph( Surv(fudays,status1) ~ log(bili) +
                                          log(protime) +
                                          edema +
                                          albumin +
                                          age,
                   data=pbc1 )
     eta5 <- fit$linear.predictors
     x <- eta5
     nobs <- length(survival.time[survival.status==1])
     span <- 1.5*(nobs^(-0.2))
     fitCox5 <- coxph( Surv(survival.time,survival.status) ~ x )
     bfnx1.1 <- SchoenSmooth( fit=fitCox5, Stime=survival.time, status=survival.status,
                            span=span, order=1)
     bfnx1.0 <- SchoenSmooth( fit=fitCox5, Stime=survival.time, status=survival.status,
                            span=span, order=0)
     plot(bfnx1.1$time, bfnx1.1$beta, type="l", xlab="Time", ylab="beta(t)")
     lines(bfnx1.0$time, bfnx1.0$beta, lty=3)

