Trial reportTrials2026
Applying a hypothetical strategy to the intercurrent event of non-adherence with the parametric g-formula: a post hoc secondary analysis of the MET-PREVENT randomised controlled trial.
Trial report in Trials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- STROBE-causal machine learning for the human microbiome: systematic review on methodological innovations and validation frameworks.Frontiers in microbiology · 2026Pooled it
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe conducted a post hoc secondary analysis of the MET-PREVENT randomised placebo-controlled trial to target an estimand that uses a hypothetical strategy on the intercurrent event of non-adherence.
methodsWe viewed the targeting of this estimand that uses a hypothetical strategy to handle the intercurrent event of non-adherence as a type of causal mediation problem, where randomised treatment is a binary exposure, adherence is a binary mediator, there is an exposure-mediator interaction, and mediator-outcome confounders that are caused by the exposure (e.g. gastrointestinal symptoms) are present. We used the parametric g-formula to estimate the average controlled direct effect (CDE) of metformin (versus placebo) on 4-m walk speed, which is interpreted as the average treatment effect under the hypothetical scenario that all trial participants adhered to assigned treatment. Variables identified as confounders were informed by a literature review and discussions with an expert; assumptions about the causal structure were represented in a directed acyclic graph. We applied a probabilistic bias analysis (PBA) to understand the potential for bias assuming adherence had been misclassified; observed adherence based on returned tablet count may be an inaccurate version of "true adherence" based on actual consumption.
resultsOur sample size was 70 trial participants (34 metformin, 36 placebo). Our estimate of the CDE was 0.072 m/s (percentile-based bootstrap 95% CI - 0.292, 0.445). Results from PBA indicated that the greater the extent of misclassification, the more the CDE may be estimated with bias and over-optimistic precision.
conclusionsOur study provided supporting information on metformin's potential role as a repurposed medication to improve physical performance in nondiabetic older adult patients with physical prefrailty/frailty and probable sarcopenia. Unlike the main trial results, our results do not rule out the possibility of either a meaningful benefit or meaningful harm of metformin, provided that full adherence can be assured. We highlighted the parametric g-formula as a useful method in trials to target estimands with a hypothetical strategy to handle treatment non-adherence.
trial registrationISRCTN ISRCTN29932357.
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