Abstract: Systems and methods for deriving composite scores are illustrated. One embodiment includes a method for optimizing a clinical trial configuration. The method derives item scores for each of a plurality of subjects where each: is based subject data corresponding to a randomized control trial; and answers items from a medical evaluation. The method identifies a parameter to optimize vectors of item weights, wherein: the vectors of item weights are derived using a mean-variance analysis; and each includes a non-negative number. The method determines, for each of the plurality of subjects, an initial composite score, from: one of the at least one vector of item weights; and the plurality of item scores. A resulting collection of composite scores includes the initial composite score determined for each of the plurality of subjects. The method applies the resulting collection of composite scores to implementing a clinical trial.

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 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: 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 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: 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 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.

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, computing a first correlation between the set of prognostic scores and a set of outcomes for the set of samples, computing a first variance for the set of outcomes for the set of samples, estimating a second correlation and a second variance for a target random control trial, and determining a set of target trial parameters based on the first and second correlations and the first and second variances.

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: 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.