縦横に偏った調査データに関する3部構成のランダム効果モデルで",適用できない"と回答した.
Eugenia Buta1, Patricia Simon2, Ralitza Gueorguieva3
1Department of Biostatistics, Yale University, New Haven, CT.
まとめ
この研究では,回答が欠落している調査データとフロア効果を正確に分析するために,新しい3部構成の統計モデルを導入しています. このモデルは,たばこと健康 (PATH) の人口の評価研究のような複雑な健康調査の公正な傾向推定を改善します.
科学分野:
- 統計局 統計局 統計局 統計局 統計局
- バイオ統計学 バイオ統計学
- 調査方法論 調査方法論
背景:
- 調査データには,多くの場合,参加者のサブセットにのみ質問が含まれています.
- 応答が最も低いスケール値で集まるフロア効果は一般的です.
- そのようなデータを分析するには,選択と応答パターンを考慮する方法が必要です.
研究 の 目的:
- 欠落率とフロア効果を含む調査データを分析するための新しい3部構成の統計モデルを提案する.
- 時間の経過におけるトレンドの公正で効率的な見積もりを改善する.
- PATH研究のような複雑な縦断調査を分析する際の課題に対処するためです.
主な方法:
- 2つのロジスティックサブモデルと断片化された通常のモデルからなる3つの部分のモデル.
- ランダムな効果を組み込み,繰り返し観察における相関を処理する.
- SAS PROC NLMIXEDを使用した最大確率推定.
主要な成果:
- 提案された3つの部分からなるモデルは,よりシンプルなモデルと比較して,バイアスが著しく低いことを実証しました.
- このモデルは,シミュレーションにおける回帰係数のカバー確率を向上させました.
- PATHの若者データへの適用は,その実用的な有用性を示しました.
結論:
- 3つの部分からなるモデルは,欠落とフロア効果を含む複雑な調査データを分析するための堅実なアプローチを提供します.
- 従来の方法よりもバイアスと効率の面で優れたパフォーマンスを提供します.
- この方法論は,縦断的な健康研究における傾向分析の精度を高めます.
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