Abstract: Systems and methods for Bayesian PROCOVA operations are illustrated. One embodiment includes a method for updating predictive models. The method trains a set of one or more generative models based on RCT data. The method defines a mixture prior distribution that includes: an informative component that follows an informative prior distribution defined, at least in part, on the RCT data; and a flat component that follows a flat prior distribution defined independently of the RCT data. The method generates, using the set of one or more generative models, predicted panel data for a plurality of digital subjects. The method derives a mixture posterior distribution corresponding to the unknown parameters of the set of one or more generative models, based on the predicted panel data. The method determines, based on at least one of the predicted panel data or the mixture posterior distribution, a set of one or more decision rules.

Abstract: Systems and method for estimating treatment effects for a target trial in accordance with embodiments of the invention are illustrated. One embodiment includes a method. The method defines a skedastic function model, wherein defining the skedastic function model depends, at least in part, on target trial data. The method designs trial parameters for the target trial based in part on the skedastic function model. The method applies the trial parameters to a loss function to derive at least one minimizing coefficient, wherein a minimizing coefficient corresponds to a regression coefficient for an expected outcome to the target trial based on the trial parameters. The method computes standard errors for the at least one minimizing coefficient. The method quantifies, using the standard errors, values for uncertainty associated with the target trial. The method updates the trial parameters according to the uncertainty.

Abstract: Systems and techniques for time-series forecasting are illustrated. One embodiment includes a method for refining time-series forecasts, the method obtains timestep information including baseline information, a time gap, and context information. The baseline information includes information known about the system at a time when the multivariate time-series is generated. The context information includes at least one vector of time-independent background variables related to the system. The method determines, based on the timestep information, parameter predictions for the system at a first timestep and a second timestep. The method derives actual state values for the system at the first timestep. The method updates the parameter predictions for the system at the second timestep, using a gating function, based on a discrepancy between: the parameter predictions for the system at the first timestep, and the actual state values for the system at the first timestep.

Abstract: Systems and methods for determining treatment effects of a randomized control trial (RCT) in accordance with embodiments of the invention are illustrated. One embodiment includes a method for determining treatment effects. The method includes steps for receiving data from a RCT, generating result data using a set of one or more generative models, and determining treatment effects for the RCT using the generated result data.

Abstract: One embodiment includes a method for predicting the progression of a current state. The method obtains input information concerning time-series forecasts of a state of an entity. The input information includes baseline information known about the state of the entity at a start time; and context information that includes a vector of time-independent background variables related to the entity. The method determines a first forecast for the entity at a first timestep that is separated from the start time by a time gap. The first forecast is determined, by a point prediction model, based on the baseline information and the context information. The method derives, from an autoregressive function, a mean parameter for a probabilistic function. The mean parameter is derived based on: the first forecast; and a learnable function trained based on the time gap and context information. The method parameterizes the probabilistic function based on the mean parameter.

Abstract: Systems and methods for designing random control trials in accordance with embodiments of the invention are illustrated. One embodiment includes a method for designing a target random control trial. The method includes steps for generating a set of prognostic scores for a set of samples. The set of prognostic scores includes prognostic scores at each of several points in time for each sample. The method includes assessing discrimination and bias metrics for the set of generative models based on a set of outcomes for the set of samples that includes outcomes at each of several points in time for each sample. The method includes determining a set of target trial parameters for a randomized control trial (RCT) based on the assessed discrimination and bias metrics, generating result data using the set of generative models, and determining treatment effects for the RCT using the generated result data.

Abstract: Systems and methods for designing random control trials in accordance with embodiments of the invention are illustrated. One embodiment includes a method for designing a target random control trial. The method includes steps for generating a set of prognostic scores for a set of samples. The set of prognostic scores includes prognostic scores at each of several points in time for each sample. The method includes assessing discrimination and bias metrics for the set of generative models based on a set of outcomes for the set of samples that includes outcomes at each of several points in time for each sample. The method includes determining a set of target trial parameters for a randomized control trial (RCT) based on the assessed discrimination and bias metrics, generating result data using the set of generative models, and determining treatment effects for the RCT using the generated result data.

Abstract: Systems and techniques for time-series forecasting are illustrated. One embodiment includes a method for refining time-series forecasts, the method obtains timestep information including baseline information, a time gap, and context information. The baseline information includes information known about the system at a time when the multivariate time-series is generated. The context information includes at least one vector of time-independent background variables related to the system. The method determines, based on the timestep information, parameter predictions for the system at a first timestep and a second timestep. The method derives actual state values for the system at the first timestep. The method updates the parameter predictions for the system at the second timestep, using a gating function, based on a discrepancy between: the parameter predictions for the system at the first timestep, and the actual state values for the system at the first timestep.

Abstract: Systems and methods for training and utilizing constrained generative models in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training a constrained generative model. The method includes steps for receiving a set of data samples from a first distribution, identifying a set of constraints from a second distribution, and training a generative model based on the set of data samples and the set of constraints.

Abstract: Systems and techniques for adjusting experiment parameters are illustrated. One embodiment includes a method that defines a joint distribution, wherein the joint distribution corresponds to a combination of a probabilistic model and a point prediction model, and wherein the point prediction model is configured to obtain a measurement of regression accuracy. The method derives an energy function for the joint distribution. The method obtains, from the energy function for the joint distribution, an approximation for a conditional distribution, wherein an output of the point prediction model is a parameter of the approximation. The method determines, from a loss function, at least one training parameter. The method trains the probabilistic based on the at least one parameter to operate as a conditional generative model, wherein the trained probabilistic model follows the conditional distribution. The method applies the trained probabilistic model to a dataset corresponding to a randomized trial.

Abstract: Systems and techniques for adjusting experiment parameters are illustrated. One embodiment includes a method that defines a joint distribution, wherein the joint distribution corresponds to a combination of a probabilistic model and a point prediction model, and wherein the point prediction model is configured to obtain a measurement of regression accuracy. The method derives an energy function for the joint distribution. The method obtains, from the energy function for the joint distribution, an approximation for a conditional distribution, wherein an output of the point prediction model is a parameter of the approximation. The method determines, from a loss function, at least one training parameter. The method trains the probabilistic based on the at least one parameter to operate as a conditional generative model, wherein the trained probabilistic model follows the conditional distribution. The method applies the trained probabilistic model to a dataset corresponding to a randomized trial.

Abstract: Systems and methods for training and utilizing predictive models that ignore missing features in accordance with embodiments of the invention are illustrated. One embodiment includes a method for generating representations of inputs with missing values. The method includes steps for, at a single layer in a multi-layer model, receiving an input includes a set of one or more values for several features and identifying a missingness pattern of the input, wherein the missingness pattern indicates whether the set of values is missing a value for each of the several features. The method further includes determining a set of one or more transformation weights based on the missingness pattern and transforming the input based on the determined transformation weights.

Abstract: One embodiment includes a method for generating representations of inputs with missing values. The method includes steps for receiving an input includes a set of one or more values for several features, wherein the set of values for at least one of the several features includes values for each of several points in time, and for identifying a missingness pattern of the input, wherein the missingness pattern for the at least one feature indicates whether the set of values is missing a value for each of the several points in time. The method further includes steps for determining a set of one or more transformation weights based on the missingness pattern, and transforming the input based on the determined transformation weights.

Abstract: Systems and method for estimating treatment effects for a target trial in accordance with embodiments of the invention are illustrated. One embodiment includes a method. The method defines a skedastic function model, wherein defining the skedastic function model depends, at least in part, on target trial data. The method designs trial parameters for the target trial based in part on the skedastic function model. The method applies the trial parameters to a loss function to derive at least one minimizing coefficient, wherein a minimizing coefficient corresponds to a regression coefficient for an expected outcome to the target trial based on the trial parameters. The method computes standard errors for the at least one minimizing coefficient. The method quantifies, using the standard errors, values for uncertainty associated with the target trial. The method updates the trial parameters according to the uncertainty.

Abstract: Systems and method for estimating treatment effects for a target trial in accordance with embodiments of the invention are illustrated. One embodiment includes a method. The method defines a skedastic function model, wherein defining the skedastic function model is performed independently of data that will be applied to a target trial. The method designs trial parameters for the target trial based in part on the skedastic function model. The method applies the trial parameters to a loss function to derive at least one minimizing coefficient, wherein a minimizing coefficient corresponds to a regression coefficient for an expected outcome to the target trial based on the trial parameters. The method computes standard errors for the at least one minimizing coefficient. The method quantifies, using the standard errors, values for uncertainty associated with the target trial. The method updates the trial parameters according to the uncertainty.

Abstract: Systems and methods for modeling complex probability distributions are described. One embodiment includes a method for training a restricted Boltzmann machine (RBM), wherein the method includes generating, from a first set of visible values, a set of hidden values in a hidden layer of a RBM and generating a second set of visible values in a visible layer of the RBM based on the generated set of hidden values. The method includes computing a set of likelihood gradients based on the first set of visible values and the generated set of visible values, computing a set of adversarial gradients using an adversarial model based on at least one of the set of hidden values and the set of visible values, computing a set of compound gradients based on the set of likelihood gradients and the set of adversarial gradients, and updating the RBM based on the set of compound gradients.

Abstract: Systems and methods for estimating treatment effects in randomized controlled trials using covariate adjusted stratification and pseudovalue regression in accordance with embodiments of the invention are illustrated. One embodiment includes a method for estimating treatment effects in randomized controlled trials, where the method includes receiving external data of previous randomized clinical trials. The method further includes generating sets of one or more subject characteristics of a plurality of trial subjects, estimating binary outcomes of trial subjects using a stratification process, and estimating time-to-event (TTE) treatment effects of trial subjects using pseudovalue regression.

Abstract: Systems and method for designing efficient randomized trials using semiparametric efficient estimators for power and sample size calculation in accordance with embodiments of the invention are illustrated. One embodiment includes a method for sample size estimation using semiparametric efficient estimators. The method includes generating sets of one or more subject characteristics of a plurality of trial subjects based on data of prior trials and registry data, estimating sets of one or more population parameters based on the sets of one or more subject characteristics, estimating asymptotic variances of a plurality of estimators using the sets of one or more population parameters, setting a desired power level for the trial, and determining a sample size necessary to attain the desired power level for the trial based on the asymptotic variances and a treatment effect estimated by a semiparametric efficient estimator.

Abstract: Systems and methods for model selection in accordance with embodiments of the invention are illustrated. One embodiment includes a method for ranking candidate models. The method includes steps for identifying several candidate models and a set of one or more scoring models for each of the several candidate models and determining a rank distribution for each of several model pairs, where each model pair of the several model pairs includes a candidate model of the several candidate models and a scoring model of the set of scoring models. The rank distribution for each model pair can be determined based on scores for the candidate model generated by the scoring model and scores generated by the scoring model for other candidate models of the several candidate models. The method further includes ranking the several models based on the determined rank distributions.

Abstract: Systems and methods to account for uncertainties from missing covariates in generative model predictions. One embodiment includes a method for updating the values for uncertainty used in a generative model that is created using a set of known prognostically important baseline data. The method includes steps for determining a value, within the generative model, for the variance in outcome given the known prognostically important baseline data, wherein the steps include imputing values for a set of unknown prognostically important baseline data, and determining estimations for explained and unexplained variance in outcome for each subject when given both sets of data.