Summary of Metrik: Measurement-efficient Randomized Controlled Trials Using Transformers with Input Masking, by Sayeri Lala (1) and Niraj K. Jha (1) ((1) Princeton University et al.
METRIK: Measurement-Efficient Randomized Controlled Trials using Transformers with Input Masking
by Sayeri Lala, Niraj K. Jha
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework for reducing data collection in randomized controlled trials (RCTs) is introduced, called Measurement EfficienT Randomized Controlled Trials using Transformers with Input MasKing (METRIK). METRIK calculates a planned missing design (PMD) specific to the RCT from a modest amount of prior data, leveraging correlations over time and across metrics. This approach increases sampling efficiency and imputation performance compared to traditional PMDs, which randomly sample data or require ample prior data for modeling. The proposed framework optimizes a learnable input masking layer with a state-of-the-art imputer based on the Transformer architecture. Results are evaluated across five real-world clinical RCT datasets, demonstrating improved performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to reduce the cost of clinical trials is being developed. Right now, trials take lots of data from many people, but this can be expensive and time-consuming. A planned missing design (PMD) is a method that helps reduce the amount of data collected without removing any important information. This can make it easier to study new treatments and find answers faster. However, current PMDs are not very good because they require a lot of prior data or are random. The new framework, called METRIK, uses a special algorithm to create a PMD that is tailored to the specific trial being done. This helps improve the quality of the data collected and reduces the need for manual removal of unimportant metrics from the trial. |
Keywords
* Artificial intelligence * Transformer
