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Background <p>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.</p> Methods <p>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.</p> Results <p>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, <i>p</i> &lt; 0.001).</p> Conclusions <p>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 We recommend categorizing terminated and withdrawn studies as failed and completed ones as non-failed.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月17日

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  • Aleksa Jovanovic,
  • Stojan Gavric,
  • Fabio Dennstädt,
  • Nikola Cihoric

摘要

Background

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.

Methods

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.

Results

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).

Conclusions

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.