titesim2               package:titecrm               R Documentation

_T_w_o-_s_t_a_g_e _T_I_T_E-_C_R_M _S_i_m_u_l_a_t_o_r

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

     Returns an object of class ``sim'' that generates and summarizes
     the dose assignments of a simulated trial by a two-stage TITE-CRM.

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

     titesim2(PI, prior, target, n, x0, obswin=1, tgrp=obswin, rate=1, 
     accrual="fixed", surv="uniform", scheme="linear", method="bayes",
     scale=sqrt(1.34), seed=1099)

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

      PI: A vector of the true toxicity probabilites associated with
          the doses.

   prior: A vector of initial estimates of toxicity probabilities
          associated with the doses.  Must be of same length as 'PI'.

  target: The target DLT rate.

       n: Sample size of the trial.

      x0: A vector of treatment sequence according to the initial
          design.  Must be of length 'n'.

  obswin: The observation window with respect to which the MTD is
          defined.

    tgrp: The minimum waiting time between two dose cohorts at the
          initial stage.  Default is set as 'obswin', i.e., complete
          follow-up in all current patients is required before
          escalation to the next dose group.

    rate: Patient arrival rate: Expected number of arrivals per
          observation window.  Example: obswin=6 and rate=3 means
          expecting 3 patients arrive in 6 time units.

 accrual: Patient arrival scheme.  Default is ``fixed'' whereby
          inter-patient arrival is fixed.  Alternatively, use
          ``poisson'' to simulate patient arrivals by the Poisson
          process.

    surv: Distribution for time-to-toxicity.  Default is ``uniform''
          where toxicity, if occurs, occurs uniformly on the interval
          [0,'obswin'].  Other survival distributions including
          exponential and Weibull are to be made available.

  scheme: A character string to specify the method for assigning
          weights.  Default is ``linear''.  Adaptive weight using
          Kaplan-Meier ``KM'' is to be made available.

  method: A character string to specify the method for parameter
          estimation.  The default method ``bayes'' estimates the model
          parameter by the posterior mean.  Estimation using ``mle'' is
          to be made available.

   scale: Standard deviation of the normal prior of the model
          parameter.  Default is sqrt(1.34).

    seed: Seed of the random number generator.

_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
     probability of dose level i and the model parameter $beta$ has a
     normal prior with mean 0 and scale to be provided by users.

     The simulation of a trial run by a two-stage TITE-CRM.  Users need
     to provide an initial sequence before switching the TITE-CRM.

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

     An object of class ``mtd'' is returned, consisting of the summary
     of dose assignments and the final dose recommendation in a
     simulated trial.

      PI: True toxicity probabilites.

   prior: Initial estimates of toxicity probabilities.

  target: The target probability of toxicity at the MTD.

recommend: The recommended dose level for the next patient.

   scale: The standard deviation of the normal prior.

estimate: Estimate of the model parameter.

   level: Dose levels assigned to patients.

     tox: Patients' toxicity indications.

 arrival: Patients' arrival times.

 ttox.pt: Patients' times-to-toxicity.

ttox.cal: Patients' times-to-toxicity on study time.

  obswin: Observation window with respect to which the MTD is defined.

 weights: Weights assigned to patients.

  scheme: Weighting scheme.

 accrual: Patient's arrival pattern.

    rate: Rate of patient's arrival.

    surv: Distribution of time-to-toxicity.

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

     Cheung, Y. K. and Chappell, R. (2000). Sequential designs for
     phase I clinical trials with late-onset toxicities.  Biometrics
     56:1177-1182.

     Cheung, Y. K. (2005). Coherence principles in dose-finding
     studies. Biometrika 92:863-873.

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

     PI <- c(0.10,0.20,0.40,0.50,0.60,0.65)
     prior <- c(0.05,0.10,0.20,0.35,0.50,0.70)
     target <- 0.2
     x0 <- c(rep(1,3),rep(2,3),rep(3,3),rep(4,3),rep(5,3),rep(6,9))
     # Generate a trial of size 24
     foo <- titesim2(PI,prior,target,24,x0, obswin=6,rate=4,accrual="poisson")
     rec <- foo$recommend  # recommend a dose level for next patient
     plot(foo)  # summarize trial graphically

