crmsens               package:titecrm               R Documentation

_M_o_d_e_l _S_e_n_s_i_t_i_v_i_t_y _i_n _t_h_e _C_R_M

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

     Returns an object of class ``homesets'' that summarizes the
     sensitvity (robustness) of the dose-toxicity model used in the
     CRM.

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

     crmsens(prior, target, eps=1e-8, maxit=100, incr=0.1, LB=0, UB=Inf, 
     detail=FALSE)

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

   prior: A vector of initial estimates of toxicity probabilities
          associated the doses.

  target: The target DLT rate.

  detail: If TRUE, the details of the home sets will also be shown. 
          Default is set as FALSE.

     eps: A control parameter for absolute tolerance.  Defaults to
          `1e-8'.

   maxit: Maximum number of iterations allowed.  Defaults to 100.

    incr: Step size.  Defaults to 0.1.

      LB: Lower bound of the search space.  Defaults to 0.

      UB: Upper bound of the search space.  Defaults to `Inf'.

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

     Dose-toxicity relationship is assumed as an empiric power model
     $a_i^{\exp(beta)}$ where $a_i$ is the initial estimate of
     toxicity. Different choice of the initial probabilities results in
     various level of robustness of the CRM, which can be summarized by
     the indifference intervals ('iint').  See Cheung and Chappell
     (2002) in the reference section.

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

     An object of class ``homesets'' is returned, describing the model
     sensitivity by indifference intervals and a summary statement.

 homeset: The home sets of the model parameter $\exp(beta)$.

    iint: The indifference intervals of the dose-toxicity model.

   prior: Initial estimates of toxicity probabilities.

  target: The target probability of toxicity at the MTD.

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

     Cheung, Y. K. and Chappell, R. (2002). A simple technique to
     evaluate model sensitivity in the continual reassessment method. 
     Biometrics 58:671-674.

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

     # Create a simple data set
     prior <- c(0.05,0.10,0.20,0.35,0.50,0.70)
     target <- 0.2
     foo <- crmsens(prior,target)
     print(foo)

