Med - E9660A GY Zou Baron-Kenny framework Limitation of Baron-Kenny Causal mediation analysis Estimation by prediction(Standardization Estimation by

Med - E9660A GY Zou Baron-Kenny framework Limitation of...

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E9660A GY Zou Baron-Kenny framework Limitation of Baron-Kenny Causal mediation analysis Estimation by prediction (Standardization) Estimation by inverse prob weighting (MSM) Estimation by model fitting (SAS macro) E9660A: Advanced Statistical Methods for Epidemiology Causal Mediation Analysis GY Zou October 18, 2013
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E9660A GY Zou Baron-Kenny framework Limitation of Baron-Kenny Causal mediation analysis Estimation by prediction (Standardization) Estimation by inverse prob weighting (MSM) Estimation by model fitting (SAS macro) Let effect of X on Y be β 1 β 1 includes two parts direct effect of X on Y ; indirect effect of X on Y through M ; DAG Y M γ X β 2 β 3 3 tests Y = α 1 + β 1 X M = α 2 + β 2 X Y = α 3 + β 3 X + γ M decomposition of β 1 β 1 = β 3 + β 2 γ or β 1 - β 3 = β 2 γ First model redundant
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E9660A GY Zou Baron-Kenny framework Limitation of Baron-Kenny Causal mediation analysis Estimation by prediction (Standardization) Estimation by inverse prob weighting (MSM) Estimation by model fitting (SAS macro) Y = α 1 + β 1 X redundant Substituting M = α 2 + β 2 X into Y = α 3 + β 3 X + γ M yields Y = α 3 + β 3 X + γ ( M = α 2 + β 2 X ) = ( α 3 + α 2 γ ) + ( β 3 + β 2 γ ) X
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E9660A GY Zou Baron-Kenny framework Limitation of Baron-Kenny Causal mediation analysis Estimation by prediction (Standardization) Estimation by inverse prob weighting (MSM) Estimation by model fitting (SAS macro) Confounding DAG C Y M X Models ( M = α 2 + β 2 X + θ C Y = α 3 + β 3 X + γ M + φ C Substituting M into Y yields Y = ( α 3 + γα 2 )+( β 3 + γβ 2 ) X +( γθ + φ ) C
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E9660A GY Zou Baron-Kenny framework Limitation of Baron-Kenny Causal mediation analysis Estimation by prediction (Standardization) Estimation by inverse prob weighting (MSM) Estimation by model fitting (SAS macro) Estimation of mediation Fit two models to get b β 3 , b β 2 and b γ and their associated variance estimates (need delta methods for variance of b γ b β 2 ; Estimate the difference of coefficients for X without and with M in the model (need sandwich estimator to get the correct variance);
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E9660A GY Zou Baron-Kenny framework Limitation of Baron-Kenny Causal mediation analysis Estimation by prediction (Standardization) Estimation by inverse prob weighting (MSM) Estimation by model fitting (SAS macro) GEE approach for β 1 - β 3 Schluchter (2008) Flexible approaches to computing mediated effects in generalized linear models: Generalized estimating equations and bootstrapping. Multivariate Behavioral Research 43: 268–288. Data layout from y i , x i , c 1 , · · · , c p , m i : Y X C 1 · · · C p G M * Record 1 y i x i c 1 · · · c p 0 0 Record 2 y i x i c 1 · · · c p 1 m i Fit model of Y = β 0 + β 1 X + θ ( X × G ) + γ M * i.e., θ is mediation effect.
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E9660A GY Zou Baron-Kenny framework Limitation of Baron-Kenny Causal mediation analysis Estimation by prediction (Standardization) Estimation by inverse prob weighting (MSM) Estimation by model fitting (SAS macro) SAS code for using GEE approach for β 1 - β 3 data setup; data total; set yourdata(in=in0) yourdata(in=in1);
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  • Fall '13
  • MM
  • Epidemiology, Trigraph, Das Model, The Mediator, msm, GY Zou

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