Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error
Abstract Background Retrospective exploratory analyses of randomised controlled trials (RCTs) seeking to identify treatment effect heterogeneity (TEH) are prone to bias and false positives. Yet the desire to learn all we can from exhaustive data measurements on trial participants motivates the inclu...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2020-02-01
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| Series: | Trials |
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| Online Access: | https://doi.org/10.1186/s13063-020-4076-y |