Approaches in analyzing predictors of trial failure: a scoping review and meta-epidemiological study

TL;DR

This study reviews how researchers analyze factors that predict clinical trial failure and highlights the need for standardized methods.

Contribution

The paper provides a comprehensive scoping review and meta-epidemiological analysis of methodological approaches to predicting clinical trial failure.

Findings

Most studies used Clinicaltrials.gov data and frequentist statistics, with a small fraction using machine learning.

Methodological factors significantly affect reported failure proportions, with inclusion of ongoing trials lowering failure odds.

The study recommends standardizing definitions of trial failure for more consistent results.

Abstract

Although 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.

The 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.

The 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).

There 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.

The online version contains supplementary material available at 10.1186/s12874-026-02774-8.

Figures