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・G-methods for time-fixed treatments 本日の内容 ・The g-formula for time-varying treatments ・IP weighting for time-varying treatments ・A doubly robust estimator for time-varying treatments
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・G-methods for time-fixed treatments 本日の内容 ・The g-formula for time-varying treatments ・IP weighting for time-varying treatments ・A doubly robust estimator for time-varying treatments
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Stratification effect measure modification (-) effect measure modification (+) Mantel-Haenszel method 別々にオッズ比を報告
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Why model? effect measure modification (-) effect measure modification (+) 別々にオッズ比を報告(1つの効果を報告できない) g-methods
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g-formula A=1を代入 A=0を代入
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IP weighting marginal structural model
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Conditional or Marginal? outcome regression saturated parametric stratification g-formula IP weighting g-estimation or algebraically equivalent
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Time-varying treatment g-methods
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・G-methods for time-fixed treatments 本日の内容 ・The g-formula for time-varying treatments ・IP weighting for time-varying treatments ・A doubly robust estimator for time-varying treatments
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前提 ・本章ではidentifiability conditions(sequential exchangeability, positivity, and consistency)のviolationが ないものとする。 ・static treatment strategies (always treat vs. never treat) の効果を推定する。
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g-formula (weighted average) ・time-fixed treatment (A1 の反実アウトカム) ・time-varying treatment
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g-formula (weighted average)
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g-formula (weighted average)
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g-formula (simulation) のシミュレーション と
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g-formulaの注意点 ・DAGに基づいたcovariates L1 をモデルに含める ・static sequential exchangeabilityが成立すればstatic treatment strategyの効果はidentify可能
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g-formulaの一般化 ・static treatment strategy ・dynamic treatment strategy linear regression logistic regression
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・G-methods for time-fixed treatments 本日の内容 ・The g-formula for time-varying treatments ・IP weighting for time-varying treatments ・A doubly robust estimator for time-varying treatments
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IP weighting (weights) ・nonstabilized IP weights ・ stabilized IP weights
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IP weighting (non-stabilized)
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Stabilized weights non-stabilized weights: stabilized weights: Lと独立であればよい Lと独立であればよい
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IP weighting (stabilized)
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IP weightingの一般化 ・nonstabilized IP weights ・ stabilized IP weights logistic regression logistic regression (misspecifiedでも可)
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Marginal Structural Model ・2K > Nのときは推定できない ・marginal structural mean model stabilized IP weightsを使って推定 misspecified??
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Effect Measure Modification ・baseline variable VによるEMMがある場合、marginal structural modelは以下の通り(parametric) stabilized IP weightsを使って推定 Vに入れて良いのはbaseline variableだけ
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・G-methods for time-fixed treatments 本日の内容 ・The g-formula for time-varying treatments ・IP weighting for time-varying treatments ・A doubly robust estimator for time-varying treatments
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Doubly Robust Estimator ・g-formula ・ IP weighting
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1. Doubly Robust (time-fixed) 2. 3. A=1とA=0でそれぞれ を推定 を推定 , をLについて標準化
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1. Doubly Robust (time-varying) 2. 3. を推定 からパラメータ を求める。 を求めておく を推定し、Aの値に応じた を求める。 これを繰り返して を求める。 always treat
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