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R
package GUIP1
D. Dinart(1), J. Fraisse(2), D. Tosi(2), A. Mauguen(3), Y. Laghzali(2), C. Touraine(2), M.C. Le Deley(4), C. Bellera(1), C. Mollevi(2) (1) Inserm CIC1401, Module Epidemiologie clinique, Institut Bergonie, Bordeaux, France (2) Institut du Cancer Montpellier (ICM), Montpellier, France (3) Memorial Sloan Kettering Cancer Center, New York, USA (4) Centre Oscar Lambret, Lille, France
The first objective of GUIP1
package is to simplify the use of model-based designs used in phase I trial.
GUIP1
package allows the user to:
To use GUIP1 package the following tools have to be installed:
Currently, five model-guided adaptive (MGA) designs are implemented in GUIP1
package:
library(GUIP1)
### Launch GUIP1
GUIP1()
#select one of the 5 MGA designs
Once the model selected, the user is invited to choose between either the study of the operating characteristics of the underlying model via simulations or the management of a trial.
To do this, it's needed to click either on interactive option or simulator option. In the following, an example with CRMB design is provided.
To run simulations, all fields must be completed. The following input parameters are required:
Model:
Uncertainty prior distribution:
Only normal distribution with mean=0 is available for CRMB, CRML and TITE-CRM
Once the simulations run, it's possible to visualize the output in the Results tab.
The user can retrieve:
All of these results can be either export in an excel file or being saved into R file.
Fig5. and Fig6. are dedicated to input parameters needed to manage a trial. Overall, the same parameters than those presented in simulation section are required except those related to simulation (seed etc.). Depending on the design chosen, all parameters required can be displayed on the same tab. The following input parameters are required:
Model:
Uncertainty prior distribution:
Only normal distribution with mean=0 is available for CRMB, CRML and TITE-CRM
The Include tab contains a short summary of trial inputs and the current state of inclusions and recommendations.
The dose level recommended for the next patient corresponds to dose level displayed in estimated MTD. This is the dose level estimated as being closest to the targeted DLT rate. From this tab, the user can either include a patient with New patient button or complete information for a patient with Pending patient button or modify information already entered with Modif data patient button. The last 2 buttons are not available for all designs. Basically, Modif data patient button is reserved for TIme-To-Event designs.
To include a patient it's needed to click on the New patient button displayed in Include tab and entered information from previous patient. The information required are:
The two options Pending patient and Modif data patient work the same way. The user is invited to select the patient concerned and in the first case provide the toxicity response (Y/N). In the second case, the user can modify (update) all information provided about a patient. This option is particularly handy for TITE designs where patients entered in a staggered fashion.
Results tab is visible from the first inclusion. In this tab, the user will be able to visualize outputs at patient and dose level, as well as graphics describing dose-toxicity and patient-toxicity relastionship and export his results in excel and pdf files.
By clicking on Patient summary results a table summarizing the data entered for each patient:
Depending on the selected design, the information provided by Dose level summary results are quite different, not all of the following information are displayed. The following is given for each dose level:
Fig 13. is a graphic describing the relationship between the patient number and the presence or not of toxicity. Presence of toxicity is represented by a red circle.
Fig 14., Fig 15. and Fig 16. are graphics describing dose-toxiciy relationship either with the help of curves and confidence intervals or boxplots. The graphics proposed vary with the selected design.
O'Quigley, J., Pepe, M., and Fisher, L. (1990). Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics 46, 33-48.
O'Quigley, J., and Shen, L.Z. (1996). Continual reassessment method: a likelihood approach. Biometrics 52, 673-684.
Babb, J., Rogatko, A., and Zacks, S. (1998). Cancer phase I clinical trials: efficient dose escalation with overdose control. Stat. Med. 17, 1103-1120.
Cheung, Y.K., and Chappell, R. (2000). Sequential designs for phase I clinical trials with late-onset toxicities. Biometrics 56, 1177-1182.
Mauguen, A., Le Deley, M.C., and Zohar, S. (2011). Dose-finding approach for dose escalation with overdose control considering incomplete observations. Stat. Med. 30, 1584-1594.
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