psh.fit               package:survrec               R Documentation

_S_u_r_v_i_v_a_l _f_u_n_c_t_i_o_n _e_s_t_i_m_a_t_o_r _f_o_r _r_e_c_u_r_r_e_n_c_e _t_i_m_e _d_a_t_a _u_s_i_n_g _t_h_e _e_s_t_i_m_a_t_o_r 
_d_e_v_e_l_o_p_e_d _b_y _P_e_a, _S_t_r_a_w_d_e_r_m_a_n _a_n_d _H_o_l_l_a_n_d_e_r

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

     Estimation of survival function for recurrence time data by means 
     the generalized product limit estimator (PLE) method developed by 
     Pe a, Strawderman and Hollander. The resulting object of class
     "survfitr"  is plotted by `plot.survfitr', before it is returned.

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

        psh.fit(x,tvals) 

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

       x: a survival recurrent event object

   tvals: vector of times where the survival function can be estimated. 

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

     The estimator computed by this object is the nonparametric
     estimator of the inter-event time survivor function under the
     assumption of a renewal or IID model. This generalizes the
     product-limit estimator to the situation where the event is
     recurrent. For details and the theory behind this estimator,
     please refer to Pe\~na, Strawderman and Hollander (2001, JASA).

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

       n: number of unit or subjects observed.

       m: vector of number of recurrences in each subject (length n).

  failed: vector of number of recurrences in each subject (length n*m).
          Vector ordered (e.g. times of first unit, times of second
          unit, ..., times of n-unit)

censored: vector of times of censorship for each subject (length n).

numdistinct: number of distinct failures times

distinct: vector of distinct failures times

  AtRisk: matrix of number of persons-at-risk at each distinct time and
          for each subject

survfunc: vector of survival estimated in distinct times

   tvals: copy of argument

PSHpleAttvals: vector of survival estimated in tvals times

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

     Pea, E.A., Strawderman, R. and Hollander, M. (2001).
     Nonparametric Estimation with Recurrent Event Data.  _J. Amer.
     Statist. Assoc_ *96*, 1299-1315.

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

     'survfitr' 'Survr'

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

     data(MMC)
     fit<-psh.fit(Survr(MMC$id,MMC$time,MMC$event))
     fit
     plot(fit,conf.int=FALSE)

     # compare with MLE Frailty

     fit<-mlefrailty.fit(Survr(MMC$id,MMC$time,MMC$event))
     fit
     lines(fit,lty=2)

     # and with wang-chang

     fit<-wc.fit(Survr(MMC$id,MMC$time,MMC$event))
     fit
     lines(fit,lty=3)

