2. Matching methods for estimating causal effects Danilo Fusco Rome, October 15, 2012

3. Y E C ? Confounding C is a confounder if: it is associated with the exposure (E) of interest; It is causally related to the outcome (Y); AND... It is not part of the exposure  outcome causal pathway By study design: Matching Methods During estimation of exposure effect: Risk adjustment

4. Objective To compare propensity score and genetic matching methods

5. Match Each exposed to One or More Non exposed on Propensity Score  Nearest neighbor matching  Caliper matching  Mahalanobis metric matching in conjunction with PSM  Stratification matching  Difference-in-differences matching (kernel & local linear weights) Run Logistic Regression: Dependent variable: Y=1, if exposed; Y = 0, otherwise. Choose appropriate covariates. Obtain propensity score: predicted probability (p) or log[p/(1-p)]. Multivariate analysis based on new sample to estimate the effect of the exposure on the outcome Propensity adjustment (PS): general procedure

6. Most of the suggested criteria for a correct specification of a PS model investigate the estimated PS’s ability to balance baseline differences in observed covariates. That means that for each unique value of the estimated PS, the distribution of covariates is the same for the exposed and non exposed group. Propensity score balance

7. –Each treated unit matched to the nearest control –Check the balance –Repeat –Stop when balance achieved Genetic Matching Method Iterated nearest-neighbor matching across all covariates Multivariate analysis based on new sample to estimate the effect of the exposure on the outcome

8. Materials and methods The study population. All patients recruited in the EURHOBOP Project and discharged for STEMI were included in the analyses (n=3628). EURHOBOP is a project funded by the European Commission. It contributed to benchmark the hospital performance in terms of management of coronary heart disease patients. The Work Package 6 “Gender inequalities assessment” has the objective to compare in-hospital mortality and access to Percutaneous Coronary Intervention (PCI) by gender in patients with AMI.

9. STEMI: demographics and past history of CV disease by gender MaleFemale N. patients2,5311,097 Age (years, median)6473 Past history of stroke (%)4.315.83 Past history of heart failure (%)2.816.29 Past history of atrial fibrillation (%)5.2210.57 Past history of peripheral artery disease (%) 5.255.93 Paste history of MI (%)12.8010.03 Past history of PCI (%)10.087.29 Past history of CABG (%)3.322.46

10. STEMI: Clinical characteristics by gender MaleFemale Alzheimer/Other dementia (%)1.465.01 Hypertension (%)38.6050.50 Diabetes (%)16.0022.61 Renal failure (%)3.995.47 Acute pulmonary edema on admission (%) 2.885.38

11. STEMI: Outcomes by gender MaleFemale In-hospital mortality8.8115.77 PCI within 90 minutes34.0224.34 PCI within 6 hours45.2835.46 PCI within 12 hours48.3237.83 PCI within 24 hours53.0641.57 PCI whenever61.9149.13

12. STEMI patients: propensity score model Odds Ratio 95% CIP-value Age1,051,05 - 1,06<.0001 Alzheimer/Other dementia1,291,02 - 1,640,031 Diabetes1,171,04 - 1,310,007 Previous CABG0,580,46 - 0,71<.0001 Previous heart failure1,271,07 - 1,510,006 Previous MI0,710,63 - 0,81<.0001 Hypertension1,341,21- 1,48<.0001

13. Original number of observations....... 3628 Original number of females............... 1097 Matched number of males............... 1097 Age Before Matching After Matching mean females72.898 mean males63.56872.861 std mean diff69.570.27816 var ratio (Tr/Co).1.01491.0029 T-test p-value< 2.22e-16 0.82846 KS Bootstrap p- value< 2.22e-16 0.192 KS Naive p-value< 2.22e-160.33194 Previous heart failure Before Matching After Matching mean treatment 0.062899 mean contro0.028052 0.045091 std mean diff14.3477.3315 var ratio (Tr/Co).2.16291.3689 T-test p-value1,54E-01 0.028444 Propensity score balance for two covariates

14. Original number of observations....... 3628 Original number of females............... 1097 Matched number of males............... 1097 Age Before Matching After Matching mean females72.898 mean males63.56872.827 std mean diff69.570.53131 var ratio (Tr/Co).1.01491.0425 T-test p-value< 2.22e-161 KS Bootstrap p- value< 2.22e-161 KS Naive p-value< 2.22e-161 Previous heart failure Before Matching After Matching mean treatment 0.062899 mean contro0.028052 0.061076 std mean diff14.3470.7506 var ratio (Tr/Co).2.16291.0279 T-test p-value1,54E-01 0.1572 Genetic matching balance for two covariates

15. Outcomes: gender effect by Genetic Matching Outcomes Odds Ratio* (Female vs Males) 95% CIP-value In hospital mortality 1,030,79 - 1,350,496 PCI within 90 ms 0,710,59 - 0,83<,0001 PCI within 6 hs 0,760,64 - 0,89<,0001 PCI within 12 hs 0,750,63 - 0,88<,0001 PCI within 24 hs 0,720,61 - 0,84<,0001 PCI whenever 0,660,56 - 0,76<,0001 *: Adjusted for hospital level characteristics.

16. What about Risk adjustment……….? Risk adjustment methodology. Potential confounders were selected by a bootstrap stepwise procedure. Since a confounder must not be an intermediate step in the causal pathway between the exposure and the disease, patient characteristics measured during the hospitalization were not considered as potential confounders because these are likely to be complications after admission.

17. Outcomes: gender effect by genetic matching and risk adjustment Outcomes Genetic matching adjustment Risk adjustment Odds Ratio* (Female vs Males) P-value Odds Ratio* (Female vs Males) P-value In hospital mortality 1,030,4961,050,672 PCI within 90 ms 0,71<,00010,700,001 PCI within 6 hs 0,76<,00010,760,007 PCI within 12 hs 0,75<,00010,730,002 PCI within 24 hs 0,72<,00010,710,001 PCI yes/no 0,66<,00010,66<,0001

18. Outcomes: gender effect by genetic matching and risk adjustment (Figure)

19. The Horror of Waiting Lorna D. Keach Genetic Matching computational time 48 hours Risk adjustment computational time 45 minutes