コホートPK/PDモデルにおける共変量選択のための正則化ホースシューによる利便性と予測性能の向上
Arya Pourzanjani1, Casey Davis1
1Daiichi Sankyo, Inc., Basking Ridge, New Jersey, USA.
CPT: pharmacometrics & systems pharmacology
|January 30, 2026
まとめ
正則化ホースシュー(RHS)法は、コホート薬物動態/薬力学(PK/PD)モデルにおける共変量選択を改善します。これは、従来の段階的アプローチとは異なり、過大評価と過剰適合を回避することで、より優れた予測性能を提供します。
科学分野:
- 薬力学モデリング、統計モデリング、機械学習
背景:
- 共変量選択は、コホート薬物動態/薬力学(PK/PD)モデルにおいて極めて重要です。段階的アプローチは一般的ですが、過大評価や過剰適合につながる可能性があります。PK/PDモデリングでは、パラメータと共変量の間のスパースな関係が典型的です。
主な方法:
- パラメータと共変量の関係を同時に評価するための正則化ホースシュー(RHS)法を開発しました。関係のスパース性を利用して効率的なモデルフィッティングを行いました。実用的なアプリケーションのためにStanコードと一般的なハイパーパラメータを提供しました。広範なシミュレーション研究を実施し、実際のPK/PDデータセットを分析しました。
結論:
- 正則化ホースシュー(RHS)は、コホートPK/PDモデリングにおける共変量選択のための強力で統計的に健全な代替手段を提供します。RHSは、モデルの解釈可能性と予測精度を向上させます。提案された方法と付随するコードは、PK/PD分析におけるより広範な採用を促進します。
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