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G-methods for time-varying treatments (Causal i...
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Shuntaro Sato
November 25, 2020
Science
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2.9k
G-methods for time-varying treatments (Causal inference: What if, Chapter 21-1)
Keywords: 因果推論, Time-varying, G-formula, IP weighting, Doubly robust estimation
Shuntaro Sato
November 25, 2020
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Transcript
None
・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
・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
Stratification effect measure modification (-) effect measure modification (+) Mantel-Haenszel
method 別々にオッズ比を報告
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
g-formula A=1を代入 A=0を代入
IP weighting marginal structural model
Conditional or Marginal? outcome regression saturated parametric stratification g-formula IP
weighting g-estimation or algebraically equivalent
Time-varying treatment g-methods
・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
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。
g-formula (weighted average) ・time-fixed treatment (A1 の反実アウトカム) ・time-varying treatment
g-formula (weighted average)
g-formula (weighted average)
g-formula (simulation) のシミュレーション と
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
g-formulaの一般化 ・static treatment strategy ・dynamic treatment strategy linear regression logistic
regression
・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
IP weighting (weights) ・nonstabilized IP weights ・ stabilized IP weights
IP weighting (non-stabilized)
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
IP weighting (stabilized)
IP weightingの一般化 ・nonstabilized IP weights ・ stabilized IP weights logistic
regression logistic regression (misspecifiedでも可)
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??
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
・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
Doubly Robust Estimator ・g-formula ・ IP weighting
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
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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