Identifying, handling and impact of immortal time bias on addressing treatment effects in observational studies using routinely collected data

Dec 11, 2025BMC medical research methodology

Recognizing and managing timing bias when studying treatment effects in observational health data

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GLP-1 Therapies on OpenScience ↗PubMed ↗DOI ↗OA ↗

Abstract

Among 256 initially identified studies, 162 cohort studies were included for analysis of immortal time bias (ITB).

8.0% of studies lacked sufficient reporting to assess ITB.
21.6% of the included studies were classified as high risk for ITB.
70.4% were classified as low risk, with 15 studies having naturally synchronized time points.
Common approaches to synchronize time points included the active comparator new-user design and the time-varying exposure definition, used in 56.6% and 19.2% of low-risk studies, respectively.
25% of high-risk studies showed statistically significant differences between ITB-controlled and original estimates.
Only 14.3% of high-risk studies acknowledged the potential impact of ITB on their results.

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BACKGROUND: Immortal time bias (ITB) represents a methodological challenge in evaluating treatment effects in observational studies using routinely collected data (RCD). However, the prevalence of ITB, the strategies used to address ITB and its impact remain inadequate. This study aimed to investigate how ITB was identified and handled in observational studies using RCD, and to assess its impact on treatment effect estimates.

METHODS: A systematic search was performed in PubMed for observational studies published from 2018 to 2020 that used RCD to evaluate drug treatment effects. We examined the synchronization of three time points (eligibility, treatment assignment, and the start of follow-up) to identify ITB and assessed the risk of ITB. For low-risk studies, we summarized the handling approaches. For high-risk studies, we conducted quantitative bias analyses to correct for ITB and calculate ITB-controlled estimates. These ITB-controlled estimates were then compared with original estimates to quantify the impact of ITB.

RESULTS: Among the 256 studies initially identified, 162 cohort studies with time-to-event outcomes were included. 13 studies (8.0%) lacked sufficient reporting to assess ITB. Of the remaining studies, 35 studies (21.6%) were classified as high risk for ITB, while 114 studies (70.4%) were classified as low risk, with 15 having naturally synchronized time points and 99 using design or analytical approaches to synchronize them. For the 99 low-risk studies, the commonly employed approaches were the active comparator new-user design and the time-varying exposure definition, accounting for 56.6% and 19.2%, respectively. Of the 35 high-risk studies, 16 studies that provided sufficient information for correction were included in the quantitative bias analyses. Among these, 4 studies (25%) showed statistically significant differences between ITB-controlled and original estimates, and 4 studies (25%) yielded conflicting conclusions regarding the statistical significance of these two estimates. Only 5 of the 35 high-risk studies (14.3%) discussed that the results may be affected by ITB.

CONCLUSIONS: ITB is a critical methodological issue in observational studies using RCD, with the potential to significantly distort conclusions. To enhance the validity of treatment effect estimates, researchers should thoroughly examine the presence of ITB and employ appropriate strategies to mitigate its impact.

Key numbers

High-Risk Studies

of 162 included cohort studies were classified as high risk for ITB.

Statistically Significant Differences

of 16 high-risk studies showed significant differences between ITB-controlled and original estimates.

Underreported Studies

of 162 studies lacked complete reporting of essential time points.

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Identifying, handling and impact of immortal time bias on addressing treatment effects in observational studies using routinely collected data

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