ArticleStatistical methods in medical research2025
Jointly assessing multiple endpoints in pilot and feasibility studies.
Article in Statistical methods in medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
- Sample size justification in feasibility studies: moving beyond published guidance.Pilot and feasibility studies · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Pilot and feasibility studies are routinely used to determine whether a definitive trial should be pursued; however, the methodologies used to assess feasibility endpoints are often basic and are rarely informed by the requirements of the planned future trial. We propose a new method for analyzing feasibility outcomes which can incorporate relationships between endpoints, utilize a preliminary study design for a future trial and allow for multiple types of feasibility endpoints. The approach specifies a Joint Feasibility Space (JFS) which is the combination of feasibility outcomes that would render a future trial feasible. We estimate the probability of being in the JFS using Bayesian methods and use simulation to create a decision rule based on frequentist operating characteristics. We compare our approach to other general-purpose methods in the literature with simulation and show that our approach has approximately the same performance when analyzing a single feasibility endpoint but is more efficient with more than one endpoint. Feasibility endpoints should be the focus of pilot and feasibility studies. The analyses of these endpoints deserve more attention than they are given, and we have provided a new, effective method their assessment.
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Registered trials
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