Sample Delta Monitoring for Early Trial Skew Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Randomized controlled trials face challenges in maintaining the expected ratio of populations between test and control groups, leading to potential sample bias and increased false positive/negative errors due to sample skew, which can be costly to detect and correct.
Innovation Solution
Implementing a system that uses the sample delta, or the difference between expected and observed sample count ratios, with two independent hypothesis tests and sequential test algorithms to detect skew without a learning period, controlling false positive/negative rates through logarithm of likelihood ratios and critical region tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to detect population ratio differences, then false alerts are generated due to random sample variation at early trial stages, but early detection of actual skew is delayed
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the cumulative distribution function (CDF) of the maximum sample delta under the null hypothesis before the trial begins. This pre-computed reference distribution enables immediate comparison with observed sample deltas during the trial, allowing early detection of skew while accounting for random variation inherent in early sampling stages.
Solution Approach 2:
The patent transforms the monitoring approach by changing from directly monitoring the sample ratio to monitoring the maximum sample delta and comparing it against a pre-computed CDF. This parameter transformation allows the system to distinguish between random early-stage variation and genuine skew, reducing false alerts while maintaining detection sensitivity.
2Reliability
If monitoring is delayed until later trial stages, then false alerts are reduced, but the ability to detect and correct skew early is lost
Solution Approach 1:
The patent uses preliminary action by pre-computing the entire CDF of maximum sample delta before the trial starts. This allows real-time monitoring from the very beginning of the trial, enabling immediate detection of skew without waiting for later stages, while the pre-computed CDF ensures that early detections are statistically valid and not false alerts.
Solution Approach 2:
The patent implements continuous feedback monitoring by comparing the observed maximum sample delta against the pre-computed CDF at each trial stage. This real-time feedback mechanism provides immediate alerts when skew is detected, enabling timely intervention while the pre-computed CDF ensures the feedback thresholds are statistically sound.
3Device complexity
If simple ratio comparison is used, then the monitoring system is simple to implement, but it cannot distinguish between random variation and actual skew
Solution Approach 1:
The patent applies preliminary action by pre-computing the CDF of maximum sample delta under the null hypothesis before the trial begins. This pre-computed statistical reference enables the system to distinguish between random variation and actual skew without requiring complex real-time calculations, maintaining implementation simplicity while achieving high detection accuracy.
Solution Approach 2:
The patent introduces the maximum sample delta as an intermediary metric between the simple observed ratio and the complex statistical test. By monitoring this intermediary statistic against the pre-computed CDF, the system achieves accurate skew detection while keeping the monitoring logic relatively simple and computationally efficient.
Data Source
AI summary
Sample delta monitoring is described herein. In one or more implementations, first and second experimental groups are defined in a randomized controlled trial. A first set of data indicative of a first experimental group in the randomized controlled trial is received. A second set of data indicative of a second experimental group in the randomized controlled trial is received. Based on sample counts of the first and second sets of data, an indication is generated that the experimental groups in the randomized controlled trial are skewed.