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Graphical abstract, Users of newly marketed drugs often differ from the patients included in randomized clinical trials, and from patients prescribed similar drugs. Cohorts of such users may be compared using propensity score adjustment, or similar user cohorts may be built using high-dimensional propensity score matching in large population databases. One such database is SNDS, the French nationwide claims and hospitalization database, which covers 99 % of the French population. It has yet been rarely used. To study the comparative effectiveness and safety in secondary coronary prevention of ticagrelor, compared to clopidogrel or prasugrel, we identified in SNDS patients who were dispensed any of the three antiplatelet agents of interest (± aspirin) within a month after discharge from hospital for acute coronary syndrome (ACS) and followed them one year for recurrence of ACS, stroke, acute bleeding, or death. High-dimensional propensity scores were developed to identify matched cohorts. Drug performances were also compared in the whole population using adjustment on the same parameters. Here we describe the database that was used, and the methods developed for the high-dimensional propensity score matching, resulting in standardized mean differences between the matched populations of less than 2 % for all of the 500+ variables included in the model. • This study was done in a newly available large-scale claims database, which may differ from other population databases, by it size and exhaustiveness • The methods elaborate on standard high-dimensional propensity scores as adapted to this claims database

Purpose High-dimensional propensity scores (hdPS) can adjust for measured confounders, but it remains unclear how well it can adjust for unmeasured confounders. Our goal was to identify if the hdPS method could adjust for confounders which were hidden to the hdPS algorithm. Method The hdPS algorithm was used to estimate two hdPS; the first version (hdPS-1) was estimated using data provided by 6 data dimensions and the second version (hdPS-2) was estimated using data provided from only two of the 6 data dimensions. Two matched sub-cohorts were created by matching one patient initiated on a high-dose statin to one patient initiated on a low-dose statin based on either hdPS-1 (Matched hdPS Full Info Sub-Cohort) or hdPS-2 (Matched hdPS Hidden Info Sub-Cohort). Performances of both hdPS were compared by means of the absolute standardized differences (ASDD) regarding 18 characteristics (data on seven of the 18 characteristics were hidden to the hdPS algorithm when estimating the hdPS-2). Results Eight out of the 18 characteristics were shown to be unbalanced within the unmatched cohort. Matching on either hdPS achieved adequate balance (i.e., ASDD

Background Comparative performance of the traditional propensity score (PS) and high-dimensional propensity score (hdPS) methods in the adjustment for confounding by indication remains unclear. We aimed to identify which method provided the best adjustment for confounding by indication within the context of the risk of diabetes among patients exposed to moderate versus high potency statins. Method A cohort of diabetes-free incident statins users was identified from the Quebec’s publicly funded medico-administrative database (Full Cohort). We created two matched sub-cohorts by matching one patient initiated on a lower potency to one patient initiated on a high potency either on patients’ PS or hdPS. Both methods’ performance were compared by means of the absolute standardized differences (ASDD) regarding relevant characteristics and by means of the obtained measures of association. Results Eight out of the 18 examined characteristics were shown to be unbalanced within the Full Cohort. Although matching on either method achieved balance within all examined characteristic, matching on patients’ hdPS created the most balanced sub-cohort. Measures of associations and confidence intervals obtained within the two matched sub-cohorts overlapped. Conclusion Although ASDD suggest better matching with hdPS than with PS, measures of association were almost identical when adjusted for either method. Use of the hdPS method in adjusting for confounding by indication within future studies should be recommended due to its ability to identify confounding variables which may be unknown to the investigators. Electronic supplementary material The online version of this article (doi:10.1186/s12874-016-0119-1) contains supplementary material, which is available to authorized users.