1. Transparency in the Use of Propensity Score Methods
John D. Seeger, PharmD, DrPH
Chief Scientist, i3 Drug Safety
Adjunct Assistant Professor, Harvard School of Public Health
September 9, 2008
With thanks to: Alec Walker, Tobias Kurth, Jeanne Loughlin, Mona Eng,
and Alex Cole

2. Propensity Score Analysis – When?
Amenable to Propensity Techniques
Thanks to S. Schneeweiss

3. 102,400 potential matching groups
Motivation
Assume matching when comparing 2 treatments:
For every drug user with given characteristics
Find a comparator with identical characteristics
Example: Male, age 45, smoker, with HTN…
Matching fails:
Age (10 categories) x
Sex (2 categories) x
Prior diagnoses 2 categories each)
Prior drug therapy 2 categories each)
Preceding cost of care (5 categories)
102,400 potential matching groups

4. Propensity Score Collapses Exposure Predictors
Single value
Probability: subject will receive therapy vs comparator
Removes confounding by components of the score
Patient characteristics that favor one therapy over another
Permits
Restriction
Matching
Stratification
Modeling
Weighting

5. Should Propensity Scores Always be Used?
More than 8 events per covariate leads to unbiased estimates
So propensity score favored when:
Many more persons exposed to drug of interest than study outcomes
Common exposure
Rare outcome
Allows for richer model (more predictors) of exposure than outcome
Alternative hypotheses
Cepeda S, et al. Am J Epidemiol 2003;158:

6. Estimate Propensity Score
Predict treatment from baseline covariates within database
Inclusion of predictors
a-priori (what characteristics are used to prescribe?)
Empiric (what differentiates initiators?)
Generic (what patterns of healthcare predict initiation?)
Coefficients of propensity score
Interpretable and Informative

7. Propensity Score Restriction
Sturmer T, et al. J Clin Epidemiol 2006;59:

8. Propensity Score Restriction
Potential for serious adverse events from error (name confusion)
Amaryl (glimepiride an oral hypoglycemic)
Reminyl (galantamine for Alzheimer’s disease)
36,816 people with AD diagnosis (14,626 Reminyl dispensings)
236 Amaryl recipients
24 Amaryl recipients in the lowest decile of the propensity score
13 with a single dispensing of Amaryl or no diabetes diagnoses
2 with no diabetes-related claims across entire claim history
Medical record review suggested no error
Propensity score restriction may be used as a screening method to identify unusual patterns of healthcare for closer scrutiny
Possible medication dispensing errors
Others
Confirmation requires additional data, which could be obtained through medical record review.

9. Propensity Score Distribution and Strata
C-statistic = 0.739

10. Effect of Temazepam Relative to Zopiclone
Transparent analysis:
Within-stratum balance
Stratum-specific effect estimates as well as pooled estimate
Explicit evaluation of potential for effect measure modification

11. Matching on the Propensity Score
Matching can be performed by:
Standard automated case-control matching programs where the matching range is specified
Nearest available match based on the propensity score
Greedy matching techniques (

14. Characteristics Before Matching

15. Balance Achieved by Matching

16. Analysis by 2X2 Table

17. MI Outcome (After Matching)
31% (7%-48%)
Risk Reduction
HR=0.69 ( )
Cumulative Incidence
Statin Non-Initiators
Statin Initiators
Months of Follow-Up

18. Regression Adjustment with Propensity Scores
All study participants are used
Still a two-step approach (exposure and outcome)
More power compared to including all covariates into the model, since degrees of freedom are gained
However, assumes the underlying association between the score and the outcome is modeled appropriately

19. Weighting IPTW SMR 1/ê(X), in treated 1 in treated
1/(1- ê(X)), in untreated
1 in treated
(ê(X)/(1-ê(X)) in untreated
IPTW
SMR

20. Baseline Characteristics

21. Cohort Results

22. Are Divergent Results Possible?
Kurth T, et al. Am J Epidemiol 2006;163:

23. What About Unmeasured Confounding?
Obesity, Smoking, Exercise

24. Accounting for Variables had Little Effect

25. Conclusion
Propensity score can be useful for addressing confounding (by indication)
Allows for rich model of exposure to be developed
Advantageous when number of people with a study outcome is small relative to number of exposed persons and number of potential confounders is large
Drug effects (particularly adverse ones)
Consider transparency
When selecting propensity score
When building propensity score
When using propensity score

26. Thank-You