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Enrollment Forecast for Clinical Trials at the Planning Phase with Study-Level Historical Data

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Abstract

Given progressive developments and demands on clinical trials, accurate enrollment timeline forecasting is increasingly crucial for both strategic decision-making and trial execution excellence. Naïve approach assumes flat rates on enrollment using average of historical data, while traditional statistical approach applies simple Poisson-Gamma model using time-invariant rates for site activation and subject recruitment. Both of them are lack of non-trivial factors such as time and location. We propose a novel two-segment statistical approach based on Quasi-Poisson regression for subject accrual rate and Poisson process for subject enrollment and site activation. The input study-level data are publicly accessible and it can be integrated with historical study data from user’s organization to prospectively predict enrollment timeline. The new framework is neat and accurate compared to preceding works. We validate the performance of our proposed enrollment model and compare the results with other frameworks on 7 curated studies.

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Acknowledgements

Special thanks go to the anonymous referee and the editor for their constructive comments, which led to a much improved version of the paper.

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Authors and Affiliations

  1. Statistical Innovation Group, Data and Statistical Sciences, AbbVie Inc., 1 N Waukegan Rd, North Chicago, IL, USA

    Mengjia Yu, Sheng Zhong, Yunzhao Xing & Li Wang

Authors
  1. Mengjia Yu
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  2. Sheng Zhong
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  3. Yunzhao Xing
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  4. Li Wang
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Contributions

This manuscript was sponsored by AbbVie. AbbVie contributed to the design, research, and interpretation of data, writing, reviewing, and approved the content. All authors are employees of AbbVie Inc. and may own AbbVie stock. LW, SZ and YX conceived and designed the analysis; MY and SZ implemented the code for the statistical models and algorithms; YX collected and pre-processed the data and MY conducted data analysis. All authors reviewed the results, drafted manuscripts and approved the final version of the manuscript.

Corresponding authors

Correspondence to Sheng Zhong or Li Wang.

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Yu, M., Zhong, S., Xing, Y. et al. Enrollment Forecast for Clinical Trials at the Planning Phase with Study-Level Historical Data. Ther Innov Regul Sci 58, 42–52 (2024). https://doi.org/10.1007/s43441-023-00564-8

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  • DOI: https://doi.org/10.1007/s43441-023-00564-8

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