# Executing the TITE-CRM `titecrm` is used to compute a dose for the next patient in a phase I trial according to the TITE-CRM. ```r titecrm(prior, target, tox, level, n = length(level), weights = NULL, followup = NULL, entry = NULL, exit = NULL, obswin = NULL, scheme = "linear", conf.level = 0.9, dosename = NULL, include = 1:n, pid = 1:n, method = "bayes", model = "empiric", var.est = TRUE, scale = sqrt(1.34), intcpt = 3, model.detail = TRUE, patient.detail = TRUE, tite = TRUE) ``` ## Arguments - `prior`: A vector of initial guesses 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`. - `n`: The number of patients enrolled. - `weights`: A vector of weights assigned to observations. A weight must be between 0 and 1. If given, the arguments `followup`, `entry`, `exit`, `obswin`, and `scheme` will be ignored. If not supplied, users must provide follow-up information via the argument `followup` or `entry` and `exit`, as well as the observation window `obswin`. The length of `weights` must be equal to that of `tox`. - `followup`: A vector of follow-up times of patients. If given, the arguments `entry` and `exit` will be ignored. - `entry`: A vector of entry times of the patients. - `exit`: A vector of exit times of the patients due to either end of follow-up or toxicity. - `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''. An adaptive weight function is specified by ``adaptive''. - `conf.level`: Confidence level for the probability/confidence interval of the returned dose-toxicity curve. - `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. Maximum likelihood estimation is specified by ``mle''. - `model`: A character string to specify the working model used in the method. The default model is ``empiric''. A one-parameter logistic model is specified by ``logistic''. - `var.est`: If TRUE, variance of the estimate of the model parameter and probability/confidence interval for the dose-toxicity curve will be computed. - `scale`: Standard deviation of the normal prior of the model parameter. Default is sqrt(1.34). - `intcpt`: The intercept of the working logistic model. The default is 3. If `model`=``empiric'', this argument will be ignored. - `model.detail`: If FALSE, the model content of an ``mtd'' object will not be displayed. Default is TRUE. - `patient.detail`: If FALSE, patient summary of an ``mtd'' object will not be displayed. Default is TRUE. - `tite`: If FALSE, the time components in patient summary of an ``mtd'' object will be omitted. Default in TRUE. ## Returns 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 guesses of toxicity rates. - **target**: The target probability of toxicity at the MTD. - **ptox**: Updated estimates of toxicity rates. - **ptoxL**: Lower confidence/probability limits of toxicity rates. - **ptoxU**: Upper confidence/probability limits of toxicity rates. - **mtd**: The updated estimate of the MTD. - **prior.var**: The variance of the normal prior. - **post.var**: The posterior variance of the model parameter. - **estimate**: Estimate of the model parameter. - **method**: The method of estimation. - **model**: The working model. - **dosescaled**: The scaled doses obtained via backward substitution. - **tox**: Patients' toxicity indications. - **level**: Dose levels assigned to patients. - **followup**: Follow-up times of patients. - **obswin**: Observation window with respect to which the MTD is defined. - **weights**: Weights assigned to patients. - **entry**: Entry times of patients. - **exit**: Exit times of patients. - **scheme**: Weighting scheme. ## Details The adaptive weighting scheme is given in Cheung and Chappell (2000) given in the reference list. ## See Also `crm` ## References 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. (2011). Dose Finding by the Continual Reassessment Method. New York: Chapman & Hall/CRC Press. ## Examples ```r # 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, 3, 3, 4, 4, 3, 2, 2, 2, 3) y <- c(0, 0, 1, 0, 1, 0, 0, 0, 0, 0) u <- c(178, 181, 168, 181, 24, 181, 179, 102, 42, 3) tau <- 180 foo <- titecrm(prior, target, y, level, followup=u, obswin=tau) rec <- foo$mtd # recommend a dose level for next patient # An example with adaptive weight foo2 <- titecrm(prior, target, y, level, followup=u, obswin=tau, scheme="adaptive") wts <- foo2$weights # The `weights' argument makes `followup' and `obswin' obsolete foo3 <- titecrm(prior, target, y, level, weights=wts, followup=u, obswin=tau) ## Not run: plot(foo3, ask=T) ## Patient time information via `entry' and `exit' arguments # entry time (days since study begins) entry <- c(7, 29, 49, 76, 92, 133, 241, 303, 363, 402) # exit time (days since study begins) exit <- c(185, 210, 217, 257, 116, 314, 420, 405, 405, 405) foo4 <- titecrm(prior, target, y, level, exit=exit, entry=entry, obswin=tau) ## Not run: plot(foo4, ask=T) ```

titecrm function