titecrm               package:titecrm               R Documentation

_T_I_T_E-_C_R_M

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

     Returns an object of class 'mtd' that summarizes the dose
     assignments and recommends a dose for the next patient in a phase
     I trial using TITE-CRM.

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

     titecrm(prior, target, tox, level, n=length(level), weights=NULL, 
     followup=NULL, obswin=NULL, scheme="linear", dosename=NULL, include=1:n, 
     pid=1:n, method="bayes", scale=sqrt(1.34), model.detail=TRUE, 
     patient.detail=TRUE)

_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.

     tox: A vector of patient outcomes; 1 indicates a toxicity, 0
          otherwise.

   level: A vector of dose levels assigned to patients.  The length of
          'level' must be equal to that of 'tox'.

 weights: A vector of weights assigned to observations.  A weight must
          be between 0 and 1.  If given, the arguments 'followup',
          'obswin', and 'scheme' will be ignored. If not supplied,
          users must provide 'followup' and 'obswin'.  The length of
          'weights' must be equal to that of 'tox'.

       n: The number of enrollments.

followup: A vector of follow-up times of patients.  If not supplied,
          users must provide 'weights'.

  obswin: The observation window with respect to which the MTD is
          defined.  If not supplied, users must provide 'weights'.

  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.

dosename: A vector containing the names of the regimens/doses used. 
          The length of 'dosename' must be equal to that of 'prior'.

 include: A subset of patients included in the dose calculation.

     pid: Patient ID provided in the study.  Its length must be equal
          to that of 'level'.

  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).

model.detail: If TRUE, the model content of an ``mtd'' object will be
          displayed in detail.

patient.detail: If TRUE, patient summary will be given in detail.

_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.

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

     An object of class ``mtd'' is returned, consisting of the summary
     of dose assignments thus far and the recommendation of dose for
     the next patient.

   prior: Initial estimates of toxicity probabilities.

    ptox: Updated 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.

followup: Follow-up times of patients.

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

 weights: Weights assigned to patients.

  scheme: Weighting scheme.

_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.

_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
     level <- c(3,4,4,3,3,4,3,2,2,2)
     y <- c(0,0,1,0,0,1,1,0,0,0)
     u <- c(1,1,0.8,1,1,0.6,0.45,0.25,1/6,1/12)
     tau <- 1
     foo <- titecrm(prior,target,y,level,followup=u,obswin=1)
     rec <- foo$recommend  # recommend a dose level for next patient

