ReviewCritical care (London, England)2026
Assessment of the effectiveness of protein in critical illness: the role of statistical shortcomings in explaining discrepancies between observational studies and randomized controlled trials.
Review in Critical care (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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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2 authors.
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Abstract
backgroundUntil 2023, recommendations for prescribing protein to critically ill patients were largely based on observational studies and non-randomized interventions. Several large, multicentre non-randomized studies of intervention conducted between 2009 and 2017 consistently reported that higher protein intake during the acute phase of critical illness was associated with lower mortality. However, major randomized controlled trials published in recent years (the EFFORT Protein trial in 2023, the PRECISe trial in 2024, and the TARGET Protein trial in 2025) have found no clinical benefit of increased protein administration. The reasons for these conflicting results are unclear, but they may be related to statistical shortcomings in observational studies. MAIN BODY: The authors identified numerous types of statistical bias in observational nutrition research. These include violations of the time axis, confounding by indication, failure to account for a wash-in period, inappropriate methods for variable selection and modelling of numerical confounder variables, transfer bias, sample-size bias, competing-risk bias, bias by data not missing at random, immortal time bias, and collider bias. To improve reliability, modern observational studies should emulate the design of randomized controlled trials. Advanced statistical approaches, including competing-risk models, time-varying analyses, nonlinear modelling and lag-time adjustments, can better account for the complexity of nutrition therapy in critical illness.
conclusionsNumerous types of bias may have exaggerated the apparent benefits of higher protein intake in older observational studies possibly explaining discrepancies to newer randomized trials. Rigorous statistical planning, transparent analysis protocols, use of advanced statistical approaches and expert statistical review are required to ensure that observational nutrition research produces valid, clinically meaningful evidence.
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