模擬データと実世界のデータを用いた時間系列の結合点分析の方法の評価
Lucie Noé1, Zaba Valtuille1, Emilie Lanoy2
1Center of Clinical Investigations, INSERM CIC1426, Robert Debré University Hospital, APHP.Nord, Paris, France; Université Paris Cité, UMR 1343, Perinatal and Pediatric Pharmacology and Therapeutic Assessment, Paris, France.
Journal of clinical epidemiology
|September 3, 2025
まとめ
この研究では,Joinpoint Regression Program (JRP) とトレンド分析のためのR"セグメント"パッケージを比較しました. Rは自己相関性なしでより高い特異性を示し,JRPは自己相関性のある医療データでより優れたパフォーマンスを示した.
科学分野:
- バイオ統計学
- 流行病学
- 医療サービス研究
背景:
- ジョイントポイント回帰 (JR) は,タイムシリーズデータにおけるトレンド変化を特定するために不可欠です.
- 医療の動向を正確に分析するには,JRに適したソフトウェアの選択が不可欠です.
研究 の 目的:
- 結合点回帰プログラム (JRP) と R "セグメント" パッケージの結合点検出の性能を比較する.
- 異なる特性のシミュレーションデータと現実の小児精神疾患の入院データを用いてソフトウェアのパフォーマンスを評価する.
主な方法:
- 制御された自動相関,傾向の変化,結合点の位置を模擬した1000のデータセット.
- 小児精神疾患の月間入院率 (2016年−2023年) を分析した.
- 評価された精度,特異性,信頼区間の範囲,月間変化率 (MPC) の範囲.
主要な成果:
- 自動相関性のないシミュレーションでは,RはJRP (92. 7%) よりも高い特異性を示した.
- 傾向の変化と自己相関により,JRPはRと比較してより高い精度と信頼区間の範囲を示した.
- 小児の精神疾患の入院を分析した結果,JRPとRの間の結合点と月間平均変化の違いが明らかになった.
結論:
- R"セグメンテッド"パッケージは,残留自己相関が欠けているデータセットに適しています.
- JRPは,自己相関した医療データまたは有意な傾向変化のないデータを分析するために推奨されます.
- 結合点回帰のためのソフトウェアの選択は,信頼性の高い傾向分析のための特定のデータセットの特性と整合する必要があります.
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