ArticleESMO real world data and digital oncology2026
Impact of concomitant proton pump inhibitors in patients treated for advanced or metastatic NSCLC: a target trial emulation protocol.
Article in ESMO real world data and digital oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
10 authors.
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Abstract
Background: Proton pump inhibitors (PPIs) are commonly coprescribed in patients with advanced or metastatic non-small-cell lung cancer (NSCLC), including those treated with tyrosine kinase inhibitors (TKIs) or immune checkpoint inhibitors (ICIs), often without a well-defined indication. PPIs may reduce TKI bioavailability by increasing gastric pH and compromising ICI efficacy through alteration of the gut microbiota. Observational studies have reported an association between concomitant PPI use and poorer survival in these patients. However, causal inference remains limited by confounding and time-related biases, and conducting a dedicated randomized trial is unlikely to be feasible. Target trial emulation provides a structured methodological framework to estimate causal effects using real-world data (RWD). Materials and methods: Two target trials will be emulated using RWD from the nationwide UNICANCER Epidemiological Strategy and Medical Economics (ESME)-Lung Cancer database, linked to the French National Health Data System. Advanced or metastatic NSCLC patients initiating first-line treatment with a TKI or an ICI between 2015 and 2024 could be included. Two strategies will be compared: initiation of a TKI or an ICI with concomitant PPI dispensation within a 12-week grace period versus without PPIs during this period. Time zero is defined as TKI or ICI initiation. The primary endpoint is overall survival; secondary endpoints include real-world progression-free survival and real-world time to next treatment. Causal effects will be estimated using a cloning, censoring, and weighting approach with inverse probability of censoring weights to address confounding and immortal time bias. Sensitivity analyses will include G-computation, quantitative bias analysis, and
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