METHODS AND SYSTEM FOR REDUCING COMPUTATIONAL COMPLEXITY OF CLINICAL TRIAL DESIGN SIMULATIONS

1 . A method comprising:

generating a plurality of base datasets using a random number generator;

determining scenario specifications;

identifying at least one transformation function for at least one of the plurality of base datasets based on the scenario specifications;

transforming the at least one of the plurality of base datasets using the at least one transformation function; and

generating scenario parameters based on the at least one transformed datasets.

2 . The method of claim 1 , wherein the plurality of base datasets include at least one of an enrollment time dataset, a survival time dataset, a treatment ID dataset, or a dropout time dataset.

3 . The method of claim 1 , wherein the plurality of base datasets are generated according to a predefined value distribution and range.

4 . The method of claim 3 , wherein the at least one transformation function changes a value distribution of a dataset.

5 . The method of claim 3 , wherein the at least one transformation function changes the range of the values of a dataset.

6 . The method of claim 1 , wherein generating scenario parameters comprises combining a plurality of transformed datasets.

7 . The method of claim 1 , wherein the plurality of base datasets are generated for all scenarios of a trial design analysis.

8 . The method of claim 7 , further comprising:

evaluating historical trial design selections to identify one or more parameters of a set of trial designs based at least in part on a first set of user interactions;

obtaining trial design simulation results based at least in part on a quick search data structure and the one or more parameters;

generating a substitute for at least some of the trial design simulation results based at least in part on a relationship between the trial design simulation results and supplemental data;

generating a performance surface based at least in part on a set of trial designs corresponding to the trial design simulation results;

evaluating one or more trial designs based at least in part on the performance surface; and

calculating a score based on normalized score component values corresponding to the trial design simulation results.

9 . A method comprising:

determining information fraction sets for a set of a plurality of designs;

precomputing, for each information fraction set, a boundary condition for a test statistic;

simulating a design to determine test statistics for the design; and

generating a decision to stop or proceed with sample size re-estimation for the simulated design based on the test statistics and the precomputed boundary condition.

10 . The method of claim 9 , further comprising determining spending function parameters for each information fraction set.

11 . The method of claim 10 , further comprising precomputing a boundary conditions for each spending family parameter.

12 . The method of claim 9 , further comprising determining duplicate information fractions and filtering duplicate information fractions.

13 . The method of claim 12 , wherein precomputing comprises, precomputing only for unique information fraction sets.

14 . The method of claim 12 , wherein determining information fraction sets comprises:

determining look positions for each of the plurality of designs;

determining available information at each look position;

determining total expected information for a trial design; and

determining a ratio of the available information to the total expected information.

15 . The method of claim 14 , wherein the ratio is indicative of a number of patients.

16 . The method of claim 14 , wherein the ratio is indicative of a number of deaths.

17 . The method of claim 9 , wherein the plurality of designs is all the designs in a trial design study.

18 . The method of claim 9 , further comprising:

determining a distribution of simulations for the designs; and

determining a subset of the designs based on the distribution of simulations.

19 . The method of claim 18 , wherein determining information fraction sets comprises determining information fraction sets for the subset of the designs.

20 . The method of claim 18 , wherein the distribution of simulations is based on a time of the simulations or a location of the simulations.

21 . A method comprising:

generating a set of scenario parameters from base data;

identifying look positions for a plurality of designs;

determining information fraction set for the look positions for the plurality of designs;

computing, for each information fraction set, a boundary for a test statistic; and

evaluating designs at the look positions using the computed boundaries.