Nuoo-Ting (Jassy) Molitor 1 Chris Jackson 2 With Nicky Best, Sylvia Richardson 1 1 Department of Epidemiology and Public Health Imperial College, London 2 MRC Biostatistics Unit, Cambridge [email protected][email protected]http://www.bias- project.org.uk Bayesian graphical models for combining mismatched administrative and survey data: application to low birth weight and water disinfection by-products
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Nuoo-Ting (Jassy) Molitor 1 Chris Jackson 2 With Nicky Best, Sylvia Richardson 1 1 Department of Epidemiology and Public Health Imperial College, London.
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Nuoo-Ting (Jassy) Molitor1
Chris Jackson2
With Nicky Best, Sylvia Richardson1
1Department of Epidemiology and Public Health
Imperial College, London2MRC Biostatistics Unit, [email protected]@mrc-bsu.cam.ac.uk
http://www.bias-project.org.uk
Bayesian graphical models for combining mismatched administrative and survey data:
application to low birth weight and water disinfection by-products
i: subject indexNm : group of subjects who had missing outcome (ymiss )r: regionu: index for the category of outcomeyobs: observed outcomeX: observed covariates
Y(1, 2, 3)
C (0/1)A
(aggre.) Missing Covariate Model
Missing Outcome Model
Investigating the performance of the unified model
Good Performance of model depended on1. How well the aggre. data can inform C (covariate)2. How strong C and Y are linked
We can examine the following 4 data scenarios1. Strong (A C) Strong (CY)2. Strong (A C) Weak (CY)3. Weak (A C) Strong (CY)4. Weak (A C) Weak (CY)
Step 1: Create data (N=1333) under the scenarios:
Step 3: Compare the prediction based on an analysis using fully observed data (no imputation)with an analysis using partially observed data (imputation).
Note: partially observed data were analyzed under various models1. Covariate sub-model (examining A C)2. Outcome sub-model (examining C Y)3. Unified Model (examining AC and CY)4. Unified Model with cut
Step 2: Missing assignment: - randomly chose 80% of subjects and treat their C as missing - only 10% of individuals with outcomes in categories 2 or 3 were assigned to be missing
Repeat step 2 : generate 20 replicate samples
Simulation Study
Examining the Imputation of missing covariateone level (AC)
Strong AC
Weak AC
Assign higher probability of covariate pattern to subjects whose true covariates corresponding to that pattern than to those whose true pattern is different
Ability to discriminateture covariate pattern
decrease
Examining the Imputation of missing covariatetwo level (AC & C Y)
Feedback form outcome model is beneficial to covariate imputation.
The predicted probabilities of covariate patter (C=0,0) are betterable to discriminate between subjects whose true covariates are C=0,0 or not.
In particular, weak C scenarios.
Examining the impact of the imputation modelon the Y-C association