Systems and methods for designing clinical trials

A system for enrolling candidates in a clinical trial for a treatment, the system comprising one or more processors, memory, and one or more programs stored in the memory and executable by the one or more processors, the one or more programs including instructions for:

• computing, for a first group of patients that participated in a clinical trial for the treatment for the condition, predictions for at least one condition progress metric that indicate predicted progression of the condition for the first group of patients, wherein the predictions for the at least one condition progress metric are computed via a machine learning predictive model and clinical data associated with the first group of patients, wherein the machine learning predictive model was trained on clinical data associated with a second group of patients having the condition;
• grouping clinical trial data associated with the first group of patients into a plurality of subsets of clinical trial data based on the predictions for the at least one condition progress metric, wherein each subset of clinical trial data is associated with a corresponding subgroup of the first group of patients;
• for each subset of clinical trial data, computing a measure of efficacy of the treatment for a subgroup of patients corresponding to the respective subset;
• identifying at least one subset of the plurality of subsets that has a measure of efficacy that is higher than a measure of efficacy of the treatment for the full first group of patients;
• and
• determining a treatment for the condition based on a range of values for the predictions for the at least one condition progress metric that is associated with the identified at least one subset of the plurality of subsets that has a measure of efficacy that is higher than a measure of efficacy of the treatment for the full first group of patients.