Obtaining personalized predictions from a randomized controlled trial on Alzheimer’s disease
Abstract The purpose of this article is to infer patient level outcomes from population level randomized control trials (RCTs). In this pursuit, we utilize the recently proposed synthetic nearest neighbors (SNN) estimator. At its core, SNN leverages information across patients to impute missing data...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84687-4 |
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| author | Dennis Shen Anish Agarwal Vishal Misra Bjoern Schelter Devavrat Shah Helen Shiells Claude Wischik |
| author_facet | Dennis Shen Anish Agarwal Vishal Misra Bjoern Schelter Devavrat Shah Helen Shiells Claude Wischik |
| author_sort | Dennis Shen |
| collection | DOAJ |
| description | Abstract The purpose of this article is to infer patient level outcomes from population level randomized control trials (RCTs). In this pursuit, we utilize the recently proposed synthetic nearest neighbors (SNN) estimator. At its core, SNN leverages information across patients to impute missing data associated with each patient of interest. We focus on two types of missing data: (i) unrecorded outcomes from discontinuing the assigned treatments and (ii) unobserved outcomes associated with unassigned treatments. Data imputation in the former powers and de-biases RCTs, while data imputation in the latter simulates “synthetic RCTs” to predict the outcomes for each patient under every treatment. The SNN estimator is interpretable, transparent, and causally justified under a broad class of missing data scenarios. Relative to several standard methods, we empirically find that SNN performs well for the above two applications using Phase 3 clinical trial data on patients with Alzheimer’s Disease. Our findings directly suggest that SNN can tackle a current pain point within the clinical trial workflow on patient dropouts and serve as a new tool towards the development of precision medicine. Building on our insights, we discuss how SNN can further generalize to real-world applications. |
| format | Article |
| id | doaj-art-5c0023e12b9a43bc95ef621242327224 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5c0023e12b9a43bc95ef6212423272242025-01-12T12:19:47ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-024-84687-4Obtaining personalized predictions from a randomized controlled trial on Alzheimer’s diseaseDennis Shen0Anish Agarwal1Vishal Misra2Bjoern Schelter3Devavrat Shah4Helen Shiells5Claude Wischik6Department of Data Sciences and Operations, USCDepartment of Industrial Engineering and Operations Research, Columbia UniversityDepartment of Computer Science, Columbia UniversityTauRx TherapeuticsDepartment of Electrical Engineering and Computer Science, MITTauRx TherapeuticsTauRx TherapeuticsAbstract The purpose of this article is to infer patient level outcomes from population level randomized control trials (RCTs). In this pursuit, we utilize the recently proposed synthetic nearest neighbors (SNN) estimator. At its core, SNN leverages information across patients to impute missing data associated with each patient of interest. We focus on two types of missing data: (i) unrecorded outcomes from discontinuing the assigned treatments and (ii) unobserved outcomes associated with unassigned treatments. Data imputation in the former powers and de-biases RCTs, while data imputation in the latter simulates “synthetic RCTs” to predict the outcomes for each patient under every treatment. The SNN estimator is interpretable, transparent, and causally justified under a broad class of missing data scenarios. Relative to several standard methods, we empirically find that SNN performs well for the above two applications using Phase 3 clinical trial data on patients with Alzheimer’s Disease. Our findings directly suggest that SNN can tackle a current pain point within the clinical trial workflow on patient dropouts and serve as a new tool towards the development of precision medicine. Building on our insights, we discuss how SNN can further generalize to real-world applications.https://doi.org/10.1038/s41598-024-84687-4Precision medicineDropoutsReal-world dataReal-world evidenceSynthetic trials |
| spellingShingle | Dennis Shen Anish Agarwal Vishal Misra Bjoern Schelter Devavrat Shah Helen Shiells Claude Wischik Obtaining personalized predictions from a randomized controlled trial on Alzheimer’s disease Scientific Reports Precision medicine Dropouts Real-world data Real-world evidence Synthetic trials |
| title | Obtaining personalized predictions from a randomized controlled trial on Alzheimer’s disease |
| title_full | Obtaining personalized predictions from a randomized controlled trial on Alzheimer’s disease |
| title_fullStr | Obtaining personalized predictions from a randomized controlled trial on Alzheimer’s disease |
| title_full_unstemmed | Obtaining personalized predictions from a randomized controlled trial on Alzheimer’s disease |
| title_short | Obtaining personalized predictions from a randomized controlled trial on Alzheimer’s disease |
| title_sort | obtaining personalized predictions from a randomized controlled trial on alzheimer s disease |
| topic | Precision medicine Dropouts Real-world data Real-world evidence Synthetic trials |
| url | https://doi.org/10.1038/s41598-024-84687-4 |
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