td_opt: Calculate optimal locally D-optimal design for td2pLL model

In jcduda/td2pLL: Fitting a time-dose two-parameter log-logistic model to time-dose-response data

Calculate optimal locally D-optimal design for td2pLL model

Description

If prior knowledge on the (rough) model shape is available, an optimal experimental design for a td2pLL model can be calculated. The numerical back bone is the Imperialist Competitive Algorithm implemented by Masoudi et al (2017).

Usage

td_opt(
  param,
  Emax_known = FALSE,
  lx = c(1, exp(-8)),
  ux = c(10, 1),
  iter = 1000,
  ICA.control = NULL
)

Arguments

param	(numeric(4)) Assumed parameters for h, delta, gamma and c0 in this order.
Emax_known	(logical(1)) Whether or not the maximal effect is known and hence, if it is required to test for larger doses. If FALSE (default) then the maximal effect (which is -100 in a td2pLL model for typical cytotoxicity data) is assumed unknown and hence the optimal design will propose to make measurement at the largest possible dose in order to gather information on the unknown, maximal effect Emax. Note that the mean control level e0 which is assumed e0=100 in the typical td2pLL model is, for optima design planning, always assumed to be unknown as otherwise no control measurements in dose=0 would be porposed by the optimal design.
lx	(numeric(2)) Minimal value for time and dose. Note that for dose, you cannot give a lower bound of zero due to technical reasons. Instead, provide a very small lower bound (default is exp(-8) = 0.00034) that represents zero.
ux	(numeric(2)) Maximal value for time and dose.
iter	(integer(1)) Number of iterations in the Imperialist Competitive Algorithm. Default is 1000.
ICA.control	(list()) Additional control parameters passed to the locally() function. See ICA.control() for details.

Value

An object of class minimax generated with locally(). For Emax_known = TRUE, the design has 7 support points. Otherwise, it has 8.

Examples

 td_opt_1 <- td_opt(param = c(h = 2, delta = 0.2, gamma = 1.3, c0 = 0.2),
   Emax_known = TRUE,
    ICA.control = list(ncount = 300, rseed = 1905, trace = FALSE),
    iter = 600)
 td_opt_1
 plot_td_des(td_opt_1)
 plot_td_dcrit_equ(td_opt_1)
 td_opt_2 <- td_opt(param = c(h = 2, delta = 0.2, gamma = 1.3, c0 = 0.2),
   Emax_known = FALSE,
    ICA.control = list(ncount = 300, rseed = 1905, revol_rate = 0.5, trace = FALSE),
    iter = 600)
 td_opt_2
 plot_td_des(td_opt_2)
 plot_td_dcrit_equ(td_opt_2)
 td_opt_3 <- td_opt(param = c(h = 1, delta = 0.5, gamma = 2, c0 = 0.01),
   Emax_known = TRUE, ICA.control = list(ncount = 300, rseed = 1905, trace = FALSE),
    iter = 1000)
 plot_td_des(td_opt_3)
 plot_td_dcrit_equ(td_opt_3, plot_theta = 100, n_grid = 100, dose_lim = c(0, 0.2))

jcduda/td2pLL documentation built on May 14, 2022, 6:48 p.m.