What statistical methods are more appropriate for predicting recruitment at the design stage of a randomised controlled trial?

BACKGROUND: Effective prediction and monitoring of recruitment in randomised controlled trials (RCTs) is critical to ensuring the target sample size is met, with 37% of trials failing to do so. This research aimed to identify various statistical methods for predicting recruitment during the design stage of an RCT and implement them using case-study trials, with a view to determining which method is more appropriate. METHODS: Various deterministic, Poisson and Bayesian methods were identified and applied to data from six previously conducted RCTs, with varying numbers of participants recruited, sites opened, durations and recruitment outcomes. RESULTS: Poisson methods were found to be more appropriate for obtaining predictions at the design stage of a trial. They produced confidence intervals to model uncertainty, unlike deterministic methods, and exhibited narrower intervals than Bayesian methods using informative priors. For single-centre trials, a homogeneous Poisson process was recommended, whilst a Non-homogeneous Poisson Process could be more suitable for multicentre trials. However, the non-homogeneous approach yielded more conservative estimates and required site-specific start dates, which may be less readily available at the design stage. Limitations included a lack of usable software for method implementation, difficulties with parameter elicitation, and a lack of additional data required to implement more complex methods, all of which may hinder the application of statistical methods for predicting recruitment which occurs in only 10% of RCTs. CONCLUSIONS: To enhance transparency and reproducibility, it is recommended that RCTs publish the methods and parameters used in recruitment predictions. This may improve their accuracy, thereby reducing the cost and minimising the research waste associated with insufficient recruitment. Further research across a wider range of trials and methods may also be required due to the complexity of factors which influence recruitment and the variability in the accuracy of statistical methods across trials.