A systematic review describes models for recruitment prediction at the design stage of a clinical trial

Gkioni, Efstathia ORCID: 0000-0002-0396-5460, Rius, Roser, Dodd, Susanna ORCID: 0000-0003-2851-3337 and Gamble, Carrol ORCID: 0000-0002-3021-1955
(2019) A systematic review describes models for recruitment prediction at the design stage of a clinical trial. Journal of Clinical Epidemiology, 115. 141 - 149.

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Objective Patient recruitment in clinical trials is challenging with failure to recruit to time and target sample size common. This may be caused by unanticipated problems or by overestimation of the recruitment rate. This study is a systematic review of statistical models to predict recruitment at the design stage of clinical trials. Study Design and Setting The Online Resource for Recruitment research in Clinical triAls database was searched to identify articles published between 2008 and 2016. Articles published before 2008 were identified from a relevant systematic review. Google search was used to find potential methods in gray literature. Results Thirteen eligible articles were identified of which, 11 focused on stochastic approaches, one on deterministic models, and one included both stochastic and deterministic methods. Models varied considerably in the factors included and in their complexity. Key aspects included their ability to condition on time; whether they used average or center-specific recruitment rates; and assumptions around center initiation rates. Lack of flexibility of some models restricts their implementation. Conclusion Deterministic models require specification of few parameters but are likely unrealistic although easy to implement. Increasingly, stochastic models require greater parameter specification, which, along with greater complexity may be a barrier to their implementation.

Item Type: Article
Uncontrolled Keywords: Clinical trials, Recruitment prediction, Statistical models, Design stage
Depositing User: Symplectic Admin
Date Deposited: 29 Aug 2019 13:20
Last Modified: 08 Jan 2022 12:56
DOI: 10.1016/j.jclinepi.2019.07.002
Open Access URL: https://doi.org/10.1016/j.jclinepi.2019.07.002
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URI: https://livrepository.liverpool.ac.uk/id/eprint/3052800