治療と害に関する研究における調整された分析:医療文献のユーザーガイド
Thomas Agoritsas1, Arnaud Merglen2, Nilay D Shah3
1Divisions of Clinical Epidemiology and General Internal Medicine, University Hospitals of Geneva, Geneva, Switzerland2Department of Clinical Epidemiology and Biostatistics, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
JAMA
|February 28, 2017
まとめ
観察的研究はしばしば不平等な予後因子によるバイアスを有する. 偏差を減らすことができますが ランダム化は全ての要因をバランスさせるための 黄金基準です
科学分野:
- 流行病学
- バイオ統計学
背景:
- 観察研究は,被曝と非被曝のグループ間の予後要因の不平等な分布のためにしばしばバイアスを経験します.
- 調整された分析や階層化された分析のような伝統的な方法は,測定されたリスク要因に基づいて均質なグループを作成することを目的としています.
研究 の 目的:
- 観察研究における予測不均衡に対する調整戦略の基本概念を導入する.
- 各種の調整方法の原則と信頼性を議論する.
主な方法:
- 測定されたリスクファクターを用いた標準調整 (分層化/調整分析) について説明します.
- 介入の可能性に基づいてマッチングする個人の傾向スコア分析を説明します.
- 外部変数を用いてランダム化を模倣する インストゥルメンタル変数解析を導入する.
主要な成果:
- 傾向方法では,複数の要因と少数のイベントを処理できますが,測定されていないまたは不正確に測定された混同要因を逃すことがあります.
- ツール変数の分析は強い仮定に依存し,バイアスの排除を保証することはできません.
- 議論されたすべての方法はバイアスを軽減しますが,ランダム化によって達成された既知の要因と未知の要因のバランスを完全に複製することはできません.
結論:
- 観察的研究には様々な調整戦略が存在しますが,ランダム化に完全に代わるものはありません.
- それぞれの方法の限界を理解することは 研究結果を正確に解釈するために不可欠です
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