Propensity Score Model Selection Through Complexity-Based Stopping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for evaluating propensity score models in clinical trials are inefficient, consuming excessive computer resources and potentially leading to misinterpretation of results due to the evaluation of all available models without prioritization.
Innovation Solution
A computer system is configured to sequentially evaluate propensity score models, prioritizing their complexity and applying stop criteria to identify suitable models, thereby reducing resource consumption and improving analysis efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all available propensity score models are evaluated using traditional brute force methods, then comprehensive model selection is achieved, but computer resource consumption (processing utilization, memory utilization, data storage utilization) increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing complexity metrics for each propensity score model before the actual evaluation process. This allows the system to sort models by complexity in advance and evaluate them sequentially, avoiding the need to compute all models fully before selection. The preliminary sorting and incremental evaluation reduce overall computational resource consumption while maintaining comprehensive model selection capability.
2Use of energy by moving object
If propensity score models are evaluated sequentially with stop criteria, then computer resource consumption is reduced, but the time to identify suitable models may increase due to sequential processing
Solution Approach 1:
The system changes the parameter of evaluation order by sorting models based on pre-calculated complexity metrics. Instead of evaluating models in arbitrary or traditional order, the system processes them from least to most complex, enabling early termination when suitable models are found. This parameter change optimizes the balance between resource consumption and evaluation time by ensuring that simpler, potentially sufficient models are evaluated first.
3Productivity
If propensity score models are evaluated in order of complexity from least to most complex, then resource efficiency is improved, but the system complexity increases due to the need to calculate and sort by complexity metrics
Solution Approach 1:
The system applies partial action by calculating complexity metrics only for the purposes of sorting and selection, not for full model execution. The complexity calculation is performed partially (for sorting only) and incrementally, allowing the system to establish an evaluation order without fully committing to the overhead of complete model analysis. This reduces the effective system complexity while maintaining resource efficiency benefits.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
In an example method, a computer system accesses first data representing a plurality of characteristics of test subjects, and second data representing a plurality of candidate propensity score models that can be used for estimating an effectiveness of a treatment. The system selects a set of propensity score models by sequentially evaluating at least some of the candidate propensity score models until one or more stop criteria are satisfied. The effectiveness of the treatment can be estimated by utilizing the selected set of propensity score models. Further, the system stores a data structure representing the set of propensity score models, and outputs the data structure.