Everything is Missing… Data A primer on causal inference and propensity scores Dan Chateau

Families First Healthy Baby EDI Families First Healthy Baby EDI MCHP Houses the Anonymized Population Health Research Data Repository ICU FASD Pediatric Diabetes ICU FASD Pediatric Diabetes K to Grade 12 Post-Secondary (UofM) K to Grade 12 Post-Secondary (UofM) CancerCare Census Data at DA/EA Level Pharmaceuticals

How do we know if something worked? Ideally we have results from both worlds… alternate realities if you will

B A C

whole world untreated untreated whole world treated treated compare

The Propensity Score--Review Predict the likelihood of exposure… And Match on that Use Inverse Probability of Treatment Weights

The Propensity Score--Review Assess: Did propensity score create comparable groups? Distribution of covariates in Group 1 comparable to distribution of covariates in Group 2?

The Propensity Score--Review Assess: Did propensity score create comparable groups? Distribution of covariates in Group 1 comparable to distribution of covariates in Group 2? This and tests on higher moments suggested comparable Assess results

Likely, there exists some unmeasured confounding. How much confounding is needed to nullify our findings? Can we hang our hat on the results? Not Significant Impact of variable CONFOUNDER STRENGTH OF CONFOUNDER

Likely, there exists some unmeasured confounding. How much confounding is needed to nullify our findings? Can we hang our hat on the results? Not Significant Impact variable CONFOUNDER STRENGTH OF CONFOUNDER

Likely, there exists some unmeasured confounding. How much confounding is needed to nullify our findings? Can we hang our hat on the results? Not Significant Impact on LBW CONFOUNDER STRENGTH OF CONFOUNDER

Sensitivity Test quantifies the strength of this unmeasured confounding How strong of a confounder will nullify findings? –If a strong confounder is needed: robust to confounding –If a weak confounder is needed: sensitive to confounding Strength is a function of two things: –Size of the relationship Benefit  LBW –Precision of the relationship Benefit  LBW Can we hang our hat on these results? Rosenbaum P. Observational Studies. 2nd ed. New York, NY: Springer-Verlag New York, Inc., Guo S, Fraser MW. Propensity Score Analysis: Statistical Methods and Applications. Sage Publications, Jiang M, Foster EM, Gibson-Davis CM. Breastfeeding and the Child Cognitive Outcomes: A Propensity Score Matching Approach. Maternal and Child Health Journal 2011;15:

Without Healthy Baby Benefit Low-Income LBW rate HIGHER than High-Income LBW rate With Healthy Baby Benefit Low-Income LBW rate LOWER than High-Income LBW rate Inequality with and without benefit: Significantly Different Need confounder that accounts for 26% of this relationship Over and above balancing achieved through propensity score Is it likely that such a confounder exists? Can we hang our hat on these results?

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