Clinical Trial Data Model for Cross-Over Treatment Sequencing
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Solution Overview
Problem
Clinical trial studies, especially those employing cross-over treatment designs, face challenges in accurately estimating costs and resource allocation due to complex operational parameters and variable subject factors, leading to inefficiencies and increased expenses.
Innovation Solution
A data model for clinical trial studies is developed to accurately estimate resources, effort, and costs by generating sequences of treatments and subject visit schedules, using operational parameters to forecast timelines, budgets, and resource demands, thereby optimizing study implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a cross-over treatment design is implemented to improve study efficiency, then the number of subjects required is reduced, but the complexity of treatment sequencing and operational management increases
Solution Approach 1:
The patent segments the clinical trial into distinct treatment periods and sequences, with subjects assigned to different sequences (e.g., Sequence AB, Sequence BA) that systematically vary the order of treatments. This segmentation allows for efficient use of subjects while maintaining manageable operational complexity through structured design.
Solution Approach 2:
The patent applies preliminary action by pre-defining multiple treatment sequences and assigning subjects to specific sequences before the trial begins. Operational parameters such as treatment duration, washout periods, and visit schedules are predetermined for each sequence, reducing in-trial complexity and enabling accurate cost forecasting.
2Measurement precision
If detailed operational parameters are specified to improve cost estimation accuracy, then the forecasting precision increases, but the time and resources required for model development increase
Solution Approach 1:
The patent performs preliminary action by establishing a comprehensive data model structure in advance that incorporates all necessary operational parameters (treatment duration, subject visits, monitoring frequency, etc.). This pre-built framework allows for rapid cost forecasting once specific trial details are input, reducing the time required for model development while maintaining high estimation accuracy.
Solution Approach 2:
The patent utilizes parameter changes by allowing flexible adjustment of operational parameters within the data model to match specific trial requirements. The model can accommodate variations in treatment duration, sequence design, subject enrollment rates, and resource allocation without requiring fundamental model restructuring, thus balancing precision with development efficiency.
3Reliability
If multiple treatment sequences are generated to account for variable subject factors, then the reliability of cost forecasting improves, but the device complexity and operational burden increase
Solution Approach 1:
The patent segments subjects into different groups assigned to different treatment sequences based on predetermined criteria. Each sequence is modeled separately with its own cost parameters, allowing for reliable forecasting that accounts for subject variability while maintaining clear, manageable structure through systematic segmentation.
Solution Approach 2:
The patent applies universality by creating a data model framework that can handle multiple treatment sequences and subject groups through a unified structure. The same modeling approach and parameter categories are applied across all sequences, reducing operational burden while maintaining forecasting reliability through consistent, multi-functional modeling capabilities.
Data Source
AI summary
The present disclosure provides for modeling a clinical trial study, which may implement a cross-over design. A plurality of treatments is generated for a clinical trial study, based on a first subset of operational parameters. A plurality of sequences is also generated for the clinical trial study, based on a second subset of the operational parameters. Each sequence of the plurality of sequences comprises a combination of ones of the plurality of treatments. A plurality of subject groups is assigned to the plurality of sequences, where one subject group of the plurality of subject groups is respectively assigned to one sequence of the plurality of sequences. The one sequence is administered to subjects of the one subject group during the clinical trial study.