Everything is Missing… Data

A primer on causal inference and propensity scores Dan Chateau

Population- Based Health

Registry

Social Housing

Education

Healthy Child MB

Immunization

Medical Services

Lab

Nursing Home

Clinical

ProviderVital

StatisticsER

Health Links

Home Care

Hospital

Family Services

Justice

Income Assistance

• Families First• Healthy Baby• EDI

MCHP Houses the AnonymizedPopulation Health Research Data Repository

• ICU• FASD• Pediatric

Diabetes

• 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

BA

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--ReviewAssess: Did propensity score create comparable groups?

• Distribution of covariates in Group 1 comparable to distribution of covariates in Group 2?

-15% -10% -5% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60%

Maternal Substance Abuse

Social assistance

Smoke during pregnancy

Single parent

Socio Economic Status: SEFI2

Screened Prenatally

Maternal Schizophrenia

Violence

Relation distress

No prenatal care

Mentally disabled Mom

Low education-Mother

Social Isolation

Family disability

Drug use

Maternal Type II Diabetes

Maternal Depression

Child abuse Mom

Maternal Anxiety

Antisocial Mom

Antisocial Dad

Alcohol Use

Maternal Age at First Birth

The Propensity Score--ReviewAssess: 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 variableCONFOUNDER

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 variableCONFOUNDER

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 LBWCONFOUNDER

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., 2010.

Guo S, Fraser MW. Propensity Score Analysis: Statistical Methods and Applications. Sage Publications, 2009.

Jiang M, Foster EM, Gibson-Davis CM. Breastfeeding and the Child Cognitive Outcomes: A Propensity Score Matching Approach. Maternal and Child Health Journal 2011;15:1296-1307.

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?

Thank You / Questions

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