Cardinality matching versus propensity score matching for addressing cluster-level residual confounding in implantable medical device and surgical epidemiology: a parametric and plasmode simulation study.

Abstract

Background: Rapid innovation and new regulations lead to an increased need for post-marketing surveillance of implantable devices. However, complex multi-level confounding related not only to patient-level but also to surgeon or hospital covariates hampers observational studies of risks and benefits. We conducted parametric and plasmode simulations to compare the performance of cardinality matching (CM) vs propensity score matching (PSM) to reduce confounding bias in the presence of cluster-level confounding.

Methods: Two Monte Carlo simulation studies were carried out: 1) Parametric simulations (1,000 iterations) with patients nested in clusters (ratio 10:1, 50:1, 100:1, 200:1, 500:1) and sample size n = 10,000 were conducted with patient and cluster level confounders; 2) Plasmode simulations generated from a cohort of 9981 patients admitted for pancreatectomy between 2015 to 2019 from a US hospital database. CM with 0.1 standardised mean different constraint threshold (SMD) for confounders and PSM were used to balance the confounders for within-cluster and cross-cluster matching. Treatment effects were then estimated using logistic regression as the outcome model on the obtained matched sample.

Results: CM yielded higher sample retention but more bias than PSM for cross-cluster matching in most scenarios. For instance, with ratio of 100:1, sample retention and relative bias were 97.1% and 26.5% for CM, compared to 82.5% and 12.2% for PSM. The results for plasmode simulation were similar.

Conclusions: CM offered better sample retention but higher bias in most scenarios compared to PSM. More research is needed to guide the use of CM particularly in constraint setting for confounders for medical device and surgical epidemiology.