progdismodel            package:changeLOS            R Documentation

_p_r_o_g_r_e_s_s_i_v_e _d_i_s_a_b_i_l_i_t_y _m_o_d_e_l

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

     Change in LOS and impact of an intermediate event on mortality can
     also be investigated in a so-called progressive disability model.
     This multi-state model can be described and the Aalen-Johansen
     estimator for transition probabilities can be computed. Further
     will be computed the mortality, attributable mortality and the
     population attributable mortality.

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

     progdismodel(model,observ, max.time)

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

  observ: a data.frame of the form data.frame(id, from, to, time, oid):

          _i_d: id (patient id, admision id)

          _f_r_o_m: the state from where a transition occurs

          _t_o: the state to which a transition occurs

          _t_i_m_e: time of the transition

          _o_i_d: the observation id

   model: an object of class 'msmodel', which describes the progressive
          disability model with two transient and four absorbing
          states.

max.time: the last time point of interest

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

     mortality: P( death, t ), P(death | risk factor absent,t) and
     P(death | risk factor present,t).

     attributable mortality: P(death | risk factor present,t) - P(death
     | risk factor absent,t).

     population attributable mortality: (P(death,t) - P(death | risk
     factor absent,t))/P(death,t).

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

     A list of 

   trans: an object of class 'trans'

      aj: an object of class 'aj'

times.par: the time points for which the following values are computed.

     PAR: population attributable mortality

      AR: attributable mortality

   death: P(death,t)

death.given.rfa: P(death | risk factor absent,t)

death.given.rfp: P(death | risk factor present,t)

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

     Matthias Wangler mw@imbi.uni-freiburg.de

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

     data(los.data)

     p <- prepare.progdismodel(data=los.data)

     pdm <- progdismodel(p$model, p$observ)

