GENERALIZED PROPENSITY SCORE MATCHING WITH MULTIPLE COHORTS: A CASE STUDY OF COMPARATIVE EFFECTIVENESS OF COMMON SECOND-LINE REGIMENS FOR NON-SMALL CELL LUNG CANCER IN THE US
Method: IMS Oncology patient-level EMR data were used for this study. Eligible patients were those with a diagnosis of lung cancer (ICD-9-CM 162.2-162.9) from 1/1/2007-6/30/2013 who received at least two lines of treatment. Generalized propensity scores were estimated using multinomial logistic regression. A region of common support with sufficient overlap in the covariate distribution and minimum variance of the covariate space was identified. Generalized PSM with replacement was performed on the common support to obtain estimated outcomes under each regimen for each patient. Balance among the cohorts was assessed by using absolute standardized differences (ASD) in covariates. Cox proportional hazards model was used for survival analysis after the generalized PSM and compared to outputs after conventional pairwise PSM. Bootstrapping was conducted as a sensitivity analysis.
Result: The five most common lung cancer regimens were identified, resulting in a total sample size of 5,222 patients. Generalized PSM used 61.2% of the patient sample while the conventional pairwise PSM used 24.1-77.1% of the patient sample across the 10 comparisons. Perfect balance (ASD=0) among the regimens was achieved on each covariate after generalized PSM by definition; acceptable balance was achieved in the conventional pairwise PSM with ASDs<0.1. Using the generalized PSM, median overall survival ranged from 5.6-8.9 months among the top 5 regimens; 8 out of the 10 survival comparisons achieved statistical significance (p<0.05). Similar results were obtained from bootstrapping. Using the conventional pairwise PSM, the median overall survival ranged from 5.6-9.5 months among the top 5 regimens and only 1 out of the 10 survival comparisons achieved statistical significance (p<0.05). The noted differences arose from different matched patient samples and the size of the samples.
Conclusion: The generalized PSM allows for comparisons across multiple cohorts using a common support while removing bias from observed covariates under the ‘no unmeasured confounding’ assumption and may have potential applications in observational studies with multiple cohorts.