ArticleBMC medical research methodology2026
Approaches in analyzing predictors of trial failure: a scoping review and meta-epidemiological study.
Article in BMC medical research methodology, 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
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough there are numerous studies exploring predictors of clinical trial failure, no comprehensive review of their methodological specificities and findings exists. We performed a scoping review with the aim of exploring the methodological approaches and findings of studies analysing predictors of clinical trial failure.
methodsThe Ovid Medline and Embase databases were systematically searched from inception to December 13, 2024, for studies employing frequentist statistics or machine learning (ML) approaches to assess predictors of trial failure across multiple clinical trials. A generalized linear model (GLM) was employed to assess the impact of certain methodological factors (failure and non-failure definitions, study types included and trial phases included) on reported failure proportions. To estimate the effects of the predictors included in the model on failure proportions, odds ratios (OR) with 95% confidence interval (95% CI) were calculated from model coefficients.
resultsThe literature search identified 17,961 records, 81 of which were included in the review. Most of the studies used Clinicaltrials.gov data (73 studies, 90.1%). Frequentist statistics were used to analyze predictors of trial failure in 73 studies (90.1%), and remaining 8 studies employed ML techniques (9.9%). The GLM showed a 27.5% deviance reduction, indicating that certain methodological factors substantially contribute to observed differences in failure proportions. Studies including trials with both completed and ongoing statuses when calculating failure proportions had lower odds of failure compared to those just including completed statuses (OR = 0.44, 95% CI: 0.29-0.67, p < 0.001).
conclusionsThere has been a recent expansion of ML approaches, potentially signaling the beginning of a paradigm shift. Methodological variations substantially influence reported failure proportions, implicating the need for adoption of standardized definitions of failure and calculation approach. We recommend categorizing terminated and withdrawn studies as failed and completed ones as non-failed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.