Comparing IPCW Models to Adjust for Informative Censoring During COVID-19 Using Data From the Clinical Practice...

Observational comparative studies can be analyzed using intention-to-treat (ITT) (i.e., initial-treatment) or as-treated (AT) (i.e., per-protocol) approaches to estimate distinct treatment effects. Unfortunately, AT analyses have an increased vulnerability to selection bias from informative censoring. While methods for informative censoring adjustment are well established, the nuances of their implementation are less well documented.

We compared marginal hazard ratios for all-cause mortality from ITT and AT analyses comparing new users of selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) in the clinical practice research datalink from 2019 to 2022 using inverse probability of treatment weights. We created inverse probability of censoring weights (IPCW) using (A) non-lagged and (B) lagged models to adjust for informative censoring in the AT analyses. We replicated analyses comparing acetylcholinesterase inhibitor and angiotensin receptor blocker initiators to assess the impact of IPCW in a different context.

We identified 335 469 SSRI initiators and 24 318 SNRI initiators. While AT estimates (HR: 1.50, 95% CI: 1.30-1.74) were further from the null than ITT estimates (HR: 1.22, 95% CI: 1.12-1.32), applying IPCW attenuated AT estimates using both lagged and non-lagged models (lagged HR: 1.24, 95% CI: 1.08-1.44; non-lagged HR: 1.16, 95% CI: 1.00-1.33). In the 337 981 antihypertensive initiators, however, IPCW did not influence AT estimates.

Younger patients were more likely to discontinue SSRIs than SNRIs, resulting in biased AT estimates closer to estimates in older patients. IPCW attenuated this bias, highlighting the utility of weighting when censoring is linked to patient characteristics.