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Found 6 papers, 1 papers with code
Prognostic Covariate Adjustment for Logistic Regression in Randomized Controlled Trials
We demonstrate that prognostic score adjustment in logistic regression increases the power of the Wald test for the conditional odds ratio under a fixed sample size, or alternatively reduces the necessary sample size to achieve a desired power, compared to the unadjusted analysis.
A Weighted Prognostic Covariate Adjustment Method for Efficient and Powerful Treatment Effect Inferences in Randomized Controlled Trials
A crucial task for a randomized controlled trial (RCT) is to specify a statistical method that can yield an efficient estimator and powerful test for the treatment effect.
Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins
Alzheimer's Disease (AD) is a neurodegenerative disease that affects subjects in a broad range of severity and is assessed in clinical trials with multiple cognitive and functional instruments.
Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks
Multiple Sclerosis (MS) is a neurodegenerative disorder characterized by a complex set of clinical assessments.
Deep learning for comprehensive forecasting of Alzheimer's Disease progression
Most approaches to machine learning from electronic health data can only predict a single endpoint.
Boltzmann Encoded Adversarial Machines
Restricted Boltzmann Machines (RBMs) are a class of generative neural network that are typically trained to maximize a log-likelihood objective function.
