Clinical trial optimization: Monte Carlo simulation Markov model for planning clinical trials recruitment

Abbas, Ismail; Rovira, Joan; Garcia, Josep Casanovas

doi:10.1016/j.cct.2006.08.002

Cited by 24 publications

(14 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Abbas [10] uses a number of Markov models to explore the maximisation of recruitment of patients in a minimum amount of time. He starts from the assumption of a fixed set of centres.…”

Section: Resultsmentioning

confidence: 99%

A systematic review of models to predict recruitment to multicentre clinical trials

Barnard

Dent

Cook

2010

BMC Med Res Methodol

View full text Add to dashboard Cite

BackgroundLess than one third of publicly funded trials managed to recruit according to their original plan often resulting in request for additional funding and/or time extensions. The aim was to identify models which might be useful to a major public funder of randomised controlled trials when estimating likely time requirements for recruiting trial participants. The requirements of a useful model were identified as usability, based on experience, able to reflect time trends, accounting for centre recruitment and contribution to a commissioning decision.MethodsA systematic review of English language articles using MEDLINE and EMBASE. Search terms included: randomised controlled trial, patient, accrual, predict, enrol, models, statistical; Bayes Theorem; Decision Theory; Monte Carlo Method and Poisson. Only studies discussing prediction of recruitment to trials using a modelling approach were included. Information was extracted from articles by one author, and checked by a second, using a pre-defined form.ResultsOut of 326 identified abstracts, only 8 met all the inclusion criteria. Of these 8 studies examined, there are five major classes of model discussed: the unconditional model, the conditional model, the Poisson model, Bayesian models and Monte Carlo simulation of Markov models. None of these meet all the pre-identified needs of the funder.ConclusionsTo meet the needs of a number of research programmes, a new model is required as a matter of importance. Any model chosen should be validated against both retrospective and prospective data, to ensure the predictions it gives are superior to those currently used.

show abstract

“…Abbas [10] uses a number of Markov models to explore the maximisation of recruitment of patients in a minimum amount of time. He starts from the assumption of a fixed set of centres.…”

Section: Resultsmentioning

confidence: 99%

A systematic review of models to predict recruitment to multicentre clinical trials

Barnard

Dent

Cook

2010

BMC Med Res Methodol

View full text Add to dashboard Cite

show abstract

“…Williford [46] advocated a Bayesian approach in the context of a homogeneous Poisson model, which he argued represented uncertainty better than the Lee model. Some other articles discussing applications of the Poisson model in accrual prediction include [10,11], and [1]. Gajewski in 2008 [21] proposed the use of interim data to refine enrollment predictions, a technique that appeared earlier in [46] (explicitly) and in [6] (implicitly).…”

Section: Poisson Process Modelsmentioning

confidence: 98%

Real-time prediction of clinical trial enrollment and event counts: A review

Heitjan

Ying

2015

Contemporary Clinical Trials

View full text Add to dashboard Cite

“…Simulation models. Abbas et al [26] used the Monte Carlo simulation Markov models to design different recruitment patterns using time as a discrete or continuous variable. For each simulation, the time to achieve target sample size is recorded with mean and standard deviation estimated across simulation.…”

Section: Poisson-gamma Model (P-g)mentioning

confidence: 99%

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

Gkioni

Rius

Dodd

et al. 2019

Journal of Clinical Epidemiology

View full text Add to dashboard Cite

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.

show abstract

Clinical trial optimization: Monte Carlo simulation Markov model for planning clinical trials recruitment

Cited by 24 publications

References 22 publications

A systematic review of models to predict recruitment to multicentre clinical trials

A systematic review of models to predict recruitment to multicentre clinical trials

Real-time prediction of clinical trial enrollment and event counts: A review

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

Contact Info

Product

Resources

About