Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data

Abstract

Acquiring real-world evidence is crucial to support health policy, but observational studies are prone to serious biases. An approach was recently proposed to overcome confounding and immortal-time biases within the emulated trial framework. This tutorial provides a step-by-step description of the design and analysis of emulated trials, as well as R and Stata code, to facilitate its use in practice. The steps consist in: (i) specifying the target trial and inclusion criteria; (ii) cloning patients; (iii) defining censoring and survival times; (iv) estimating the weights to account for informative censoring introduced by design; and (v) analysing these data. These steps are illustrated with observational data to assess the benefit of surgery among 70–89-year-old patients diagnosed with early-stage lung cancer. Because of the severe unbalance of the patient characteristics between treatment arms (surgery yes/no), a naïve Kaplan-Meier survival analysis of the initial cohort severely overestimated the benefit of surgery on 1-year survival (22% difference), as did a survival analysis of the cloned dataset when informative censoring was ignored (17% difference). By contrast, the estimated weights adequately removed the covariate imbalance. The weighted analysis still showed evidence of a benefit, though smaller (11% difference), of surgery among older lung cancer patients on 1-year survival. Complementing the CERBOT tool, this tutorial explains how to proceed to conduct emulated trials using observational data in the presence of immortal-time bias. The strength of this approach is its transparency and its principles that are easily understandable by non-specialists.

Age disparities in lung cancer survival in New Zealand: The role of patient and clinical factors

“…However, many are at high risk of bias, especially immortal time bias [35]. While waiting for clinical trials data, observational studies using appropriate methods to handle immortal time and selection biases are warranted to study the effect of treatment on age disparities in lung cancer survival [43].…”

Age disparities in lung cancer survival in New Zealand: the role of patient and clinical factors

“…However, many are at high risk of bias, especially immortal time bias 24 . While waiting for clinical trials data, observational studies using appropriate methods to handle immortal time and selection biases are warranted to study the effect of treatment on age disparities in lung cancer survival 32 .…”

Caregiving burden of informal caregivers of older adults with advanced cancer: The effects of rurality and education

“…Further observational studies to investigate how treatment may influence age disparities in colon cancer survival at population-level are needed. However, these studies should be properly designed to remove the risk of immortal-time bias, arising when the time of treatment initiation does not coincide with time of cancer diagnosis [34].…”