Clinical Trial Subject Allocation by Dementia Progression Speed
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Solution Overview
Problem
Clinical trials for anti-dementia drugs face inefficiencies due to bias in subject allocation, leading to overestimation or underestimation of drug efficacy, resulting in wasted resources and potential discontinuation of promising drugs.
Innovation Solution
A clinical trial support apparatus and method that predicts the progression speed of dementia using medical images and a trained model to allocate subjects into treatment and placebo groups based on progression speed, employing block randomization to ensure fair evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjects are allocated to treatment and placebo groups without considering progression speed, then the trial process is simple, but the evaluation of drug efficacy becomes inaccurate due to bias between groups
Solution Approach 1:
The system performs preliminary prediction of dementia progression speed using a trained model before subject allocation. By predicting the progression speed in advance and using it as a basis for grouping, the system ensures balanced distribution of subjects with similar progression characteristics across treatment and placebo groups, thereby eliminating selection bias and improving the accuracy of drug efficacy evaluation.
2Measurement precision
If subjects with slow progression are allocated to treatment group, then the drug efficacy appears suppressed, but this leads to overestimation of drug effectiveness
Solution Approach 1:
The system performs preliminary prediction of dementia progression speed using a trained model before subject allocation. By predicting the progression speed in advance and using it as a basis for grouping, the system ensures balanced distribution of subjects with similar progression characteristics across treatment and placebo groups, thereby eliminating selection bias and improving the accuracy of drug efficacy evaluation.
3Measurement precision
If subjects with fast progression are allocated to treatment group, then the drug efficacy appears enhanced, but this leads to underestimation of drug effectiveness
Solution Approach 1:
The system performs preliminary prediction of dementia progression speed using a trained model before subject allocation. By predicting the progression speed in advance and using it as a basis for grouping, the system ensures balanced distribution of subjects with similar progression characteristics across treatment and placebo groups, thereby eliminating selection bias and improving the accuracy of drug efficacy evaluation.
4Measurement precision
If progression speed prediction and grouping are performed, then the allocation accuracy is improved, but the computational complexity increases
Solution Approach 1:
The system uses a trained prediction model that has been previously trained on historical data containing imaging results and progression speeds. During actual subject allocation, the model copies the learned patterns from training data to predict progression speed for new subjects, avoiding the need to perform complex training computations in real-time and thus reducing computational complexity while maintaining prediction accuracy.
Data Source
AI summary
A clinical trial support apparatus includes a processor, in which the processor predicts a progression speed of a target disease for subjects of a clinical trial, divides the subjects into a plurality of groups according to a prediction result of the progression speed of the target disease, and allocates the subjects to a treatment group to which a test drug is administered and a placebo group to which a placebo is administered for each of the plurality of groups.