Effects of bisphosphonates after denosumab discontinuation and treatment effect heterogeneity using causal machine learning
Miryoung Kim, Nakyung Jeon, Hae Sun Suh · 2025
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Description
Miryoung Kim, Nakyung Jeon and Hae Sun Suh ask whether bisphosphonate drugs help after patients stop denosumab, a bone drug whose halt can bring a rebound in fracture risk. Korean insurance claims fed a nationwide cohort study. A causal machine learning model, the causal survival forest, estimated who gains and by how much, with follow-up to one year.
Bisphosphonates cut the risk of repeat osteoporotic fracture, with a fracture-free gain of about three points. Gains varied by age, past bisphosphonate use, diabetes, ulcer disease, steroid use and clinic type. Checks with standard methods backed the result. The authors present causal machine learning as a route to personal fracture prevention.
Catalog record
| CREATOR | Miryoung Kim, Nakyung Jeon, Hae Sun Suh |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |