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時間依存性治療（Time-varying treatments）の因果推論：周辺構造モデルにおけるIPTW法 – SAS Japan
注） 本コラムは『経時的に変化する治療（Time-varying treatments）に対する因果推論』と題した以前のコラムを、時間依存性治療に関する部分と周辺構造モデルにおけるIPTW法に関する部分に分割し、内容の追加と修正を行い再構成したものの一部となります。 以前のコラムでは、「時間依存性治療とはなにか」、「時間依存性治療の因果効果はどのように定義されるのか」、「定義した因果効果はどう推定すれば良いか」について紹介しました。時間依存性治療の因果効果の推定にあたっては、一般に条件付けに基づく手法（e.g., 回帰、層別化、マッチング）は不適であり、g-methods※1と総称される推定手法が広く用いられています。本コラムでは、それらの中でも直感的な理解や実装が最も容易である「周辺構造モデルにおけるIPTW法（inverse probability of treatment weighting (IPTW) of marginal structural models (MSMs)」の理論とSASでの実装方法について簡単に紹介します。コラム全体の流れは以下の通りです。 時間固定性治療（time-fixed treatments）※2に対する周辺構造モデルとIPTW法の紹介 IPTW法の概要 周辺構造モデルの設定がなぜ必要か 時間依存性治療（time-varying treatments）に対する周辺構造モデルとIPTW法の紹介 SASでの実装 まとめ なお、本コラムは統計的因果推論に関する基本的な理解があることを前提としております。また、文献や書籍によっては、IPTW（Inverse probability
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