Abstract: A method for trial design includes presenting on a graphical interface, a set of cards, wherein each card in the set is representative of a different trial design from a set of trial designs. The method further includes: monitoring a first set of user interactions with the set of cards; determining a user preference for one or more values of one or more parameters of the set of trial designs from the first set of user interactions; and presenting on the graphical interface a new card that is representative of a trial design consistent with the determined user preference. The method may also include monitoring a second set of user interactions with the new card and refining the determined user preference based at least in part on the second set of user interactions with the new card.

Abstract: A method for determining trial designs is provided. The method includes receiving, via at least one processor, one or more trial design criteria and one or more scenarios corresponding to a set of trial designs, and generating, via the at least one processor, simulation data based at least in part on replicating each of the set of trial designs with the one or more trial design criteria and the one or more scenarios. The simulation data includes performance parameters and performance parameter values associated with each design in the set of designs for a set of criteria. The method further includes determining, via the at least one processor, an optimality criteria for evaluating the trial designs, searching, within the set of trial designs, via the at least one processor, for globally optimum designs based on the optimality criteria, and transmitting, via the at least one processor, globally optimum designs.

Abstract: A method, according to some implementations, includes generating a plurality of base datasets using a random number generator and determining scenario specifications. The method may further include identifying at least one transformation function for at least one of the plurality of base datasets based on the scenario specifications and transforming the at least one of the plurality of base datasets using the at least one transformation function. In some cases, the method may also include generating scenario parameters based on the at least one transformed datasets.

Abstract: A method, according to some implementations, includes determining, via at least one processor, a plurality of possible sites for recruiting patients from for a trial and determining, via the at least one processor and for each of one or more subgroupings of the plurality of possible sites, a predicted patient recruitment value. The method may include determining, via the at least one processor, a candidate subgrouping of the plurality of possible sites having a predicted patient recruitment value that globally optimizes a desired site selection criteria, and transmitting, via the at least one processor, data corresponding to the candidate subgrouping.

Abstract: A method for determining trial designs is provided. The method includes obtaining simulation data for a set of trial designs. The simulation data includes performance parameters and performance parameter values associated with each design in the set of designs for a set of criteria; determining an optimality criteria for evaluating the trial designs; searching, within the set of trial designs, for globally optimum designs based on the optimality criteria; and recommending globally optimum designs.

Abstract: A method, according to some implementations, includes obtaining a criteria for a trial design study, determining permutations for designs in response to the criteria, and determining permutations for scenarios in response to the criteria. The method may further include generating combinations of the permutations for the designs and the permutations for the scenarios, simulating designs corresponding to the generated combinations, and determining performance of the simulated designs.

Abstract: A method, according to some implementations, includes receiving, via at least one processor, output data of a plurality of trial design simulations for a plurality of scenarios and evaluating, via the at least one processor, the output data to determine changes in performance over the plurality of scenarios. The method may further include generating, via the at least one processor, visual tornado diagram data structured to generate a visual tornado diagram on an electronic display of an electronic device and transmitting, via the at least one processor, the visual tornado diagram data to the electronic device.

Abstract: A method, according to some implementations, determining, via at least one processor, a plurality of possible sites for recruiting patients from for a trial and determining, via the at least one processor and for each of one or more subgroupings of the plurality of possible sites, a predicted available resources value. The method may further include determining, via the at least one processor, which subgrouping of the plurality of possible sites has a predicted available resources value that globally optimizes a desired site resource criteria.

Abstract: A method, according to some implementations, includes providing inputs to a plurality of trial design simulation engines, receiving first outputs of the plurality of trial design simulation engines in response to the inputs, and providing variations of the inputs to the plurality of trial design simulation engines. The method may further include receiving second outputs of the plurality of trial design simulation engines in response to the variations evaluating the first and the second outputs to determine delta values, and determining, based in part on the delta values, a plurality of normalization factors for the plurality of trial design simulation engines.

Abstract: A method for trial design includes presenting on a graphical interface, a set of cards, wherein each card in the set is representative of a different trial design from a set of trial designs. The method further includes: monitoring a first set of user interactions with the set of cards; determining a user preference for one or more values of one or more parameters of the set of trial designs from the first set of user interactions; and presenting on the graphical interface a new card that is representative of a trial design consistent with the determined user preference. The method may also include monitoring a second set of user interactions with the new card and refining the determined user preference based at least in part on the second set of user interactions with the new card.

Abstract: A method, according to some implementations, includes displaying an interface structured to evaluate design data by a group of users and identifying user parameters for each user in the group. The method ma further include configuring the interface for each user in the group based at least in part on the user parameters, receiving, via the interface, user input data from one or more users in the group, and scoring designs based on the user input and user parameters.

Abstract: A method for trial design with simulated annealing includes obtaining, via at least one processor of a trial design platform, initial trial design simulation results data for a set of trial designs. The method may further include identifying an initial design from the design simulation results data, predicting, performance data of designs for variations of parameters corresponding to the set of trial designs, and identifying a new design for simulation by varying parameters of the initial simulated design based on the predicting. The method may also include simulating the new design, determining performance data of the new design, and identifying a second new design for simulation by varying parameters of the new design based on the simulated performance of the new design.

Abstract: A method for determining trial designs is provided. The method includes receiving, via at least one processor, one or more trial design criteria and one or more scenarios corresponding to a set of trial designs, and generating, via the at least one processor, simulation data based at least in part on replicating each of the set of trial designs with the one or more trial design criteria and the one or more scenarios. The simulation data includes performance parameters and performance parameter values associated with each design in the set of designs for a set of criteria. The method further includes determining, via the at least one processor, an optimality criteria for evaluating the trial designs, searching, within the set of trial designs, via the at least one processor, for globally optimum designs based on the optimality criteria, and transmitting, via the at least one processor, globally optimum designs.

Abstract: A method, according to some implementations, includes obtaining trial design simulation results for a set of trial designs and determining a score for each trial design based on a performance criteria. The method may further include evaluating the designs in the set of trial designs to determine a convex hull for the set of trial designs, filtering designs based on the convex hull, and communicating the convex hull designs.

Abstract: A method for trial design with pareto techniques is provided. The method includes obtaining trial design simulation results for a set of trial designs and determining a score for each trial design based on a performance criteria. The method further includes evaluating Pareto optimality for each design in the set of trial designs to determine a Pareto frontier, filtering designs that are not on the Pareto frontier, and communicating the Pareto frontier designs.

Abstract: A method, according to some implementations, includes obtaining trial design simulation results for a set of trial designs and determining a set of Pareto designs in the set of trial designs based at least in part on the trial design simulation results and one or more performance parameters. The method further includes determining a set of convex hull designs in the set of trial designs, determining a set of recommended designs based at least in part on the set of Pareto designs and the set of convex hull designs, and transmitting the set of recommended designs.

Abstract: A method and system providing an improved technology for efficiently computing experimental design in strict statistically controlled settings. The method and system utilize a unique combination of statistical algorithms for designing, comparing the performance, and conducting multi-arm, multistage experiments efficiently, while radically reducing the computation time required on a personal computing device, thereby improving computer technology and performance. In particular, the method and system enable personal computing devices to compute very large multi-arm multistage experiments in a significantly reduced period of time. The present invention allows experimentalists to examine in a single statistically controlled experiment a vastly greater range of experimental options, with the possibility of dropping uninformative arms of the experiment, changing goals and parameters during the course of the experiment.