Theoph                 package:stats                 R Documentation

_P_h_a_r_m_a_c_o_k_i_n_e_t_i_c_s _o_f _t_h_e_o_p_h_y_l_l_i_n_e

_D_e_s_c_r_i_p_t_i_o_n:

     The 'Theoph' data frame has 132 rows and 5 columns of data from an
     experiment on the pharmacokinetics of theophylline.

_U_s_a_g_e:

     data(Theoph)

_F_o_r_m_a_t:

     This data frame contains the following columns:

     _S_u_b_j_e_c_t an ordered factor with levels '1', ..., '12' identifying
          the subject on whom the observation was made.  The ordering
          is by increasing maximum concentration of theophylline
          observed.

     _W_t weight of the subject (kg).

     _D_o_s_e dose of theophylline administered orally to the subject
          (mg/kg).

     _T_i_m_e time since drug administration when the sample was drawn
          (hr).

     _c_o_n_c theophylline concentration in the sample (mg/L).

_D_e_t_a_i_l_s:

     Boeckmann, Sheiner and Beal (1994) report data from a study by Dr.
     Robert Upton of the kinetics of the anti-asthmatic drug
     theophylline.  Twelve subjects were given oral doses of
     theophylline then serum concentrations were measured at 11 time
     points over the next 25 hours.

     These data are analyzed in Davidian and Giltinan (1995) and
     Pinheiro and Bates (2000) using a two-compartment open
     pharmacokinetic model, for which a self-starting model function,
     'SSfol', is available.

_S_o_u_r_c_e:

     Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994), _NONMEM
     Users Guide: Part V_, NONMEM Project Group, University of
     California, San Francisco.

     Davidian, M. and Giltinan, D. M. (1995) _Nonlinear Models for
     Repeated Measurement Data_, Chapman & Hall (section 5.5, p. 145
     and section 6.6, p. 176)

     Pinheiro, J. C. and Bates, D. M. (2000) _Mixed-effects Models in S
     and S-PLUS_, Springer (Appendix A.29)

_S_e_e _A_l_s_o:

     'SSfol'

_E_x_a_m_p_l_e_s:

     data(Theoph)
     coplot(conc ~ Time | Subject, data = Theoph, show = FALSE)
     Theoph.4 <- subset(Theoph, Subject == 4)
     fm1 <- nls(conc ~ SSfol(Dose, Time, lKe, lKa, lCl),
                data = Theoph.4)
     summary(fm1)
     plot(conc ~ Time, data = Theoph.4,
          xlab = "Time since drug administration (hr)",
          ylab = "Theophylline concentration (mg/L)",
          main = "Observed concentrations and fitted model",
          sub  = "Theophylline data - Subject 4 only",
          las = 1, col = 4)
     xvals <- seq(0, par("usr")[2], len = 55)
     lines(xvals, predict(fm1, newdata = list(Time = xvals)),
           col = 4)

