Maringe, Camille; Benitez Majano, Sara; Exarchakou, Aimilia; Smith, Matthew; Rachet, Bernard; Belot, Aurélien; Leyrat, Clémence; (2020) 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. Int J Epidemiol, 49 (5). pp. 1719-1729. ISSN 0300-5771 DOI: https://doi.org/10.1093/ije/dyaa057
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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.
Item Type | Article |
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Faculty and Department |
Faculty of Public Health and Policy > Dept of Health Services Research and Policy Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology |
Research Centre |
Cancer Survival Group Inequalities in Cancer Outcomes Network ?? 208138 ?? |
PubMed ID | 32386426 |
Elements ID | 147584 |
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Filename: Maringe et al. 2020 IJE.pdf
Licence: Creative Commons: Attribution-Noncommercial 3.0
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