Predicting Phase 3 Clinical Trial Results by Modeling Phase 2 Clinical Trial Subject Level Data Using Deep Learning

Youran Qi, Qi Tang
Proceedings of the 4th Machine Learning for Healthcare Conference, PMLR 106:288-303, 2019.

Abstract

Predicting Phase 3 clinical trial results is a critical step of Go/No-Go decision making and Phase 3 trial design optimization. To predict the overall treatment effect for patients enrolled into a Phase 3 trial, we propose a framework consisting of two models. First, an individual trough pharmacokinetic concentration (Ctrough) model is developed to predict the trough pharmacokinetic concentration for a potentially new treatment regime planned for Phase 3. Second, an individual treatment effect model is built to model the relationship between patient baseline characteristics, Ctrough and clinical outcomes. These two models are combined together to predict Phase 3 clinical trial results. Since the clinical outcomes to be predicted are longitudinal and the predictors are a mix of time-invariant and time-variant variables, a novel neural network, Residual Semi-Recurrent Neural Network, is developed for both models. The proposed framework is applied in a post-hoc prediction of Phase 3 clinical trial results, and it outperforms the traditional method.

Cite this Paper

BibTeX
@InProceedings{pmlr-v106-qi19a, title = {Predicting Phase 3 Clinical Trial Results by Modeling Phase 2 Clinical Trial Subject Level Data Using Deep Learning}, author = {Qi, Youran and Tang, Qi}, booktitle = {Proceedings of the 4th Machine Learning for Healthcare Conference}, pages = {288--303}, year = {2019}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {106}, series = {Proceedings of Machine Learning Research}, month = {09--10 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v106/qi19a/qi19a.pdf}, url = {https://proceedings.mlr.press/v106/qi19a.html}, abstract = {Predicting Phase 3 clinical trial results is a critical step of Go/No-Go decision making and Phase 3 trial design optimization. To predict the overall treatment effect for patients enrolled into a Phase 3 trial, we propose a framework consisting of two models. First, an individual trough pharmacokinetic concentration (Ctrough) model is developed to predict the trough pharmacokinetic concentration for a potentially new treatment regime planned for Phase 3. Second, an individual treatment effect model is built to model the relationship between patient baseline characteristics, Ctrough and clinical outcomes. These two models are combined together to predict Phase 3 clinical trial results. Since the clinical outcomes to be predicted are longitudinal and the predictors are a mix of time-invariant and time-variant variables, a novel neural network, Residual Semi-Recurrent Neural Network, is developed for both models. The proposed framework is applied in a post-hoc prediction of Phase 3 clinical trial results, and it outperforms the traditional method.} }
Endnote
%0 Conference Paper %T Predicting Phase 3 Clinical Trial Results by Modeling Phase 2 Clinical Trial Subject Level Data Using Deep Learning %A Youran Qi %A Qi Tang %B Proceedings of the 4th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2019 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v106-qi19a %I PMLR %P 288--303 %U https://proceedings.mlr.press/v106/qi19a.html %V 106 %X Predicting Phase 3 clinical trial results is a critical step of Go/No-Go decision making and Phase 3 trial design optimization. To predict the overall treatment effect for patients enrolled into a Phase 3 trial, we propose a framework consisting of two models. First, an individual trough pharmacokinetic concentration (Ctrough) model is developed to predict the trough pharmacokinetic concentration for a potentially new treatment regime planned for Phase 3. Second, an individual treatment effect model is built to model the relationship between patient baseline characteristics, Ctrough and clinical outcomes. These two models are combined together to predict Phase 3 clinical trial results. Since the clinical outcomes to be predicted are longitudinal and the predictors are a mix of time-invariant and time-variant variables, a novel neural network, Residual Semi-Recurrent Neural Network, is developed for both models. The proposed framework is applied in a post-hoc prediction of Phase 3 clinical trial results, and it outperforms the traditional method.
APA
Qi, Y. & Tang, Q.. (2019). Predicting Phase 3 Clinical Trial Results by Modeling Phase 2 Clinical Trial Subject Level Data Using Deep Learning. Proceedings of the 4th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 106:288-303 Available from https://proceedings.mlr.press/v106/qi19a.html.

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