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

Gwen Aubrac^1,2,3, Michael Webster-Clark^4,5, Robert W Platt⁶

¹Department of Epidemiology, Biostatistics, and of Occupational Health, McGill University, Montreal, Quebec, Canada.

Pharmacoepidemiology and Drug Safety

|October 12, 2025

Summary

This summary is machine-generated.

Adjusting for informative censoring using inverse probability of censoring weights (IPCW) can reduce bias in as-treated (AT) analyses. This method proved useful in comparing selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs).

Keywords:

CPRD IPCW as‐treated informative censoring intention‐to‐treat inverse probability weighting observational data

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Area of Science:

Pharmacoepidemiology
Biostatistics
Clinical Research Methodology

Background:

Observational studies use intention-to-treat (ITT) or as-treated (AT) analyses, but AT is prone to selection bias from informative censoring.
Methods exist to adjust for informative censoring, yet their practical application requires further documentation.

Purpose of the Study:

To compare intention-to-treat (ITT) and as-treated (AT) analyses for estimating treatment effects.
To evaluate the effectiveness of inverse probability of censoring weights (IPCW) in adjusting for informative censoring within AT analyses.
To assess the impact of IPCW on comparing new users of selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs).

Main Methods:

Compared marginal hazard ratios for all-cause mortality using ITT and AT analyses.
Applied inverse probability of treatment weights (IPTW) for AT analyses.
Utilized both non-lagged and lagged inverse probability of censoring weights (IPCW) models to adjust for informative censoring in AT analyses.
Replicated analyses with antihypertensive medications to validate IPCW impact.

Main Results:

As-treated (AT) estimates for SSRI vs. SNRI use showed higher mortality risk (HR: 1.50) compared to ITT estimates (HR: 1.22).
Applying IPCW attenuated the AT estimates, bringing them closer to ITT estimates (lagged HR: 1.24, non-lagged HR: 1.16).
IPCW did not significantly alter AT estimates in the antihypertensive medication comparison group.

Conclusions:

Younger patients' higher discontinuation rates of SSRIs compared to SNRIs introduced bias in AT estimates.
Inverse probability of censoring weights (IPCW) effectively mitigated this bias, demonstrating its utility when censoring is linked to patient characteristics.
Weighting methods are valuable for refining AT analyses in pharmacoepidemiological research.