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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.

Background The proportion of proposed new treatments that are ’successful’ is of ethical, scientific, and public importance. We investigated how often new, experimental treatments evaluated in randomized controlled trials (RCTs) are superior to established treatments. Objectives Our main question was: “On average how often are new treatments more effective, equally effective or less effective than established treatments?” Additionally, we wanted to explain the observed results, i.e. whether the observed distribution of outcomes is consistent with the ’uncertainty requirement’ for enrollment in RCTs. We also investigated the effect of choice of comparator (active versus no treatment/placebo) on the observed results. Search methods We searched the Cochrane Methodology Register (CMR) 2010, Issue 1 in The Cochrane Library (searched 31 March 2010); MEDLINE Ovid 1950 to March Week 2 2010 (searched 24 March 2010); and EMBASE Ovid 1980 to 2010 Week 11 (searched 24 March 2010). Selection criteria Cohorts of studies were eligible for the analysis if they met all of the following criteria: (i) consecutive series of RCTs, (ii) registered at or before study onset, and (iii) compared new against established treatments in humans. Data collection and analysis RCTs from four cohorts of RCTs met all inclusion criteria and provided data from 743 RCTs involving 297,744 patients. All four cohorts consisted of publicly funded trials. Two cohorts involved evaluations of new treatments in cancer, one in neurological disorders, and one for mixed types of diseases. We employed kernel density estimation, meta-analysis and meta-regression to assess the probability of new treatments being superior to established treatments in their effect on primary outcomes and overall survival. Main results The distribution of effects seen was generally symmetrical in the size of difference between new versus established treatments. Meta-analytic pooling indicated that, on average, new treatments were slightly more favorable both in terms of their effect on reducing the primary outcomes (hazard ratio (HR)/odds ratio (OR) 0.91, 99% confidence interval (CI) 0.88 to 0.95) and improving overall survival (HR 0.95, 99% CI 0.92 to 0.98). No heterogeneity was observed in the analysis based on primary outcomes or overall survival (I2 = 0%). Kernel density analysis was consistent with the meta-analysis, but showed a fairly symmetrical distribution of new versus established treatments indicating unpredictability in the results. This was consistent with the interpretation that new treatments are only slightly superior to established treatments when tested in RCTs. Additionally, meta-regression demonstrated that results have remained stable over time and that the success rate of new treatments has not changed over the last half century of clinical trials. The results were not significantly affected by the choice of comparator (active versus placebo/no therapy). Authors’ conclusions Society can expect that slightly more than half of new experimental treatm...