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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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Discussion