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Purpose Little information is available on the performance of high‐dimensional propensity scores (HDPS) in settings with more than two exposure levels. Our objective was to adapt the HDPS algorithm to allow for the inclusion of multilevel treatments and compare estimates obtained via this approach with those obtained via pairwise comparisons in a case study using real‐world data. Methods: We conducted a retrospective cohort study of cardiovascular events associated with three smoking cessation drugs (varenicline, bupropion, nicotine replacement therapy [NRT]) using the Clinical Practice Research Datalink. We applied the binary HDPS algorithm adjusted for pre‐specified and empirically‐selected covariates to cohorts formed by each treatment pair. We then constructed multinomial HDPS models on a cohort of new users of any of the three drugs, adjusting for predefined covariates and different combinations of empirically‐selected covariates. After trimming the area of non‐overlap of the HDPS distributions, the effects of the study drugs on cardiovascular events were estimated with the Cox proportional hazards models adjusted for propensity score category. Results: Outcome models adjusted for multinomial HDPS estimated treatment effects that were slightly more protective than those estimated in pairwise comparisons (varenicline vs NRT: HRMultinomial = 0.60‐0.62, HRPairwise = 0.64; bupropion vs NRT: HRMultinomial = 0.70‐0.72, HRPairwise = 0.76). Trimming rates were similar between the two approaches. Conclusions: The extension of HDPS to multilevel exposures is a valid and practical approach to confounder control that may be useful when comparing different classes of drugs prescribed for the same indication or different molecules within a given drug class. [ABSTRACT FROM AUTHOR]

Abstract 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.

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 <0.1) on all 18 characteristics. Conclusion: Our results indicate that the hdPS method was able to adjust for hidden confounders supporting the claim that the hdPS method can adjust for at least some unmeasured confounders. [ABSTRACT FROM AUTHOR]