Estimating treatment effects from non-overlapping cohorts with application to antimicrobial resistance

The positivity assumption, also known as overlap, is one of the key assumptions for causal inference. However, in certain observational studies it does not hold. This work is motivated by such a scenario, where the goal was to compare the effect of ceftriaxone versus cefuroxime treatment on subsequent 3rd generation cephalosporin resistance within one year post-treatment in Gram-negative bacteria among hospitalized patients. Our data consisted of electronic medical records from 24,969 patients hospitalized between 2015-2025 in two hospitals with distinct policies: one prescribed cefuroxime, while the other prescribed ceftriaxone. These prescription policies created two non-overlapping patient cohorts, as each hospital treated patients with either cefuroxime or ceftriaxone. We developed and applied a statistical framework that leverages additional data, for which positivity holds, and clinically driven assumptions, to identify the average treatment effect among these non-overlapping cohorts, without removing any observations.

Our framework is based on a logistic regression model for the conditional expected bacterial resistance outcome, and it can be applied to any generalized linear model. The model is identifiable under a clinically driven assumption that the effect modification of the hospital on cefazolin equals that of cefuroxime and ceftriaxone, when compared to penicillins. We rely on cefazolin and penicillins being prescribed in both hospitals, and on penicillins inducing negligible resistance to 3rd generation cephalosporins. We also implemented inverse probability of censoring weighting for censored outcomes, and utilized aminoglycoside resistance as a negative control outcome to assess sufficient confounding adjustment.

The estimated average treatment effect, on the risk difference scale, of ceftriaxone compared to cefuroxime was 5.83% (95% CI: -2.06%, 13.39%). The conditional odds ratio for ceftriaxone compared to cefuroxime, adjusted for confounders, was 1.31 (95% CI: 0.91, 1.89). No evidence of unmeasured confounding was found using the negative control outcome falsification test.

We develop and apply a statistical framework applicable in settings of non-overlapping cohorts that violate the positivity assumption. Our framework allows identification of the average treatment effect thanks to parametric assumptions about the conditional expected outcome, additional data and clinically driven assumptions. The empirical findings, though not statistically significant, may point to a modest resistance increase with ceftriaxone compared to cefuroxime use; and suggest further research about the preferential use of cefuroxime is needed.