223 citations · 248 across the 9 of their papers we have counts for
9 papers
A Weighted Prognostic Covariate Adjustment Method for Efficient and Powerful Treatment Effect Inferences in Randomized Controlled Trials
Alyssa M. Vanderbeek, Anna A. Vidovszky, Jessica L. Ross +8
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. A novel…
A Rule of Thumb for the Power Gain due to Covariate Adjustment in Randomized Controlled Trials with Continuous Outcomes
Charles K. Fisher
Randomized Controlled Trials (RCTs) often adjust for baseline covariates in order to increase power. This technical note provides a short derivation of a simple rule of thumb for a…
Neural Boltzmann Machines
Alex H. Lang, Anton D. Loukianov, Charles K. Fisher
Conditional generative models are capable of using contextual information as input to create new imaginative outputs. Conditional Restricted Boltzmann Machines (CRBMs) are one clas…
Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins
Daniele Bertolini, Anton D. Loukianov, Aaron M. Smith +4
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…
Bayesian prognostic covariate adjustment
David Walsh, Alejandro Schuler, Diana Hall +2
Historical data about disease outcomes can be integrated into the analysis of clinical trials in many ways. We build on existing literature that uses prognostic scores from a predi…
Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score
Alejandro Schuler, David Walsh, Diana Hall +2
Estimating causal effects from randomized experiments is central to clinical research. Reducing the statistical uncertainty in these analyses is an important objective for statisti…
Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks
Jonathan R. Walsh, Aaron M. Smith, Yannick Pouliot +3
Multiple Sclerosis (MS) is a neurodegenerative disorder characterized by a complex set of clinical assessments. We use an unsupervised machine learning model called a Conditional R…
Deep learning for comprehensive forecasting of Alzheimer's Disease progression
Charles K. Fisher, Aaron M. Smith, Jonathan R. Walsh +1
Most approaches to machine learning from electronic health data can only predict a single endpoint. Here, we present an alternative that uses unsupervised deep learning to simulate…
Boltzmann Encoded Adversarial Machines
Charles K. Fisher, Aaron M. Smith, Jonathan R. Walsh
Restricted Boltzmann Machines (RBMs) are a class of generative neural network that are typically trained to maximize a log-likelihood objective function. We argue that likelihood-b…