Asset Details
Do you wish to reserve the book?
In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome
Do you wish to request the book?
In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome
2023
Overview
Late-stage cancer immunotherapy trials often lead to unusual survival curve shapes, like delayed curve separation or a plateauing curve in the treatment arm. It is critical for trial success to anticipate such effects in advance and adjust the design accordingly. Here, we use in silico cancer immunotherapy trials – simulated trials based on three different mathematical models – to assemble virtual patient cohorts undergoing late-stage immunotherapy, chemotherapy, or combination therapies. We find that all three simulation models predict the distinctive survival curve shapes commonly associated with immunotherapies. Considering four aspects of clinical trial design – sample size, endpoint, randomization rate, and interim analyses – we demonstrate how, by simulating various possible scenarios, the robustness of trial design choices can be scrutinized, and possible pitfalls can be identified in advance. We provide readily usable, web-based implementations of our three trial simulation models to facilitate their use by biomedical researchers, doctors, and trialists.
Conventional clinical trial design methods are not necessarily tailored for the unique characteristics of immunotherapies. Here the authors use late-stage in silico cancer immunotherapy trials to investigate how design decisions affect the trial outcome.
