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Missing Data

Missing Data. ll. Multiple Imputation. Essentially, the replacement of one individual with another randomly selected individual from a defined population Works under MAR and MCAR assumptions Eg. Identifying predictive value of a diagnostic test. Response to MTX. Aim:

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Missing Data

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  1. Missing Data ll

  2. Multiple Imputation • Essentially, the replacement of one individual with another randomly selected individual from a defined population • Works under MAR and MCAR assumptions • Eg. Identifying predictive value of a diagnostic test

  3. Response to MTX • Aim: • Identify SNPs associated with MTX response

  4. Response to MTX • Data collected at baseline and at six months • Includes: • DNA • Clinical covariates: Active Joints, Limited Joints, General Well Being (GW), Physician’s Global assessment (Glob), CHAQ, ESR

  5. Response to MTX • Defining improvement: • Percentage change over 6 months (Negative Δ% indicates improvement) • ACR criteria: “At least 30% improvement from baseline in 3 of any 6 variables, with no more than 1 of the remaining variables worsening by 30%” • Extends to 50% and 70% • Four main outcomes • Improvement (30%, 50%, 70%) • Remain the same • No Response (30%) • Missing • Problems?

  6. Response to MTX Examples T1 – T0 T0

  7. Response to MTX Example: 6 COV’s + Missing data

  8. Response to MTX • Problems: • Division by 0 at baseline • Missing data at different t • Solutions?

  9. Analysis • Genes, COV • Multiple Imputation • Logistic regression

  10. Analysis • Genes • AMPD1 • ATIC • DHFR • ITPA • SLC19A1 • SLC16A7 • 50 SNPs • Carriage of the minor allele

  11. Multiple Imputation

  12. Results

  13. Conclusions • Limitation in response definition • Do not reflect clinical/overall improvement • Solutions: • New definition calculated using PCA or Factor analysis?

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