Abstract

BackgroundRandomised controlled trials (RCT) may be hindered by slow recruitment rates, particularly in critically ill patients. While statistical models to predict recruitment rates have been described, no systematic assessment has been conducted of the distribution of recruitment across sites, temporal trends in site participation and impact of competing trials on patient recruitment.MethodsWe used recruitment and screening logs from the SAFE, NICE-SUGAR, RENAL, CHEST and ADRENAL trials, five of the largest critical care RCTs. We quantified the extent of recruitment asymmetry between sites using Lorenz curves and Gini coefficients and assessed whether the recruitment distribution across sites follow the Pareto principle, which states that 80% of effects come from 20% of causes. Peak recruitment rates and growth in participating sites were calculated.ResultsIn total, 25,412 patients were randomised in 99 intensive care units (ICUs) for the five trials. Distribution of recruitment was asymmetric, with a small number of ICUs recruiting a large proportion of the patients. The Gini coefficients ranged from 0.14 to 0.52. The time to peak recruitment rate ranged from 7 to 41 months and was variable (7, 31, 41, 10 and 40 months). Over time, the proportion of recruitment at non-tertiary ICUs increased from 15% to 34%.ConclusionsThere is asymmetry of recruitment with a small proportion of ICUs recruiting a large proportion of patients. The distributions of recruitment were not consistent with the Pareto principle. There has been increasing participation of non-tertiary ICUs in clinical trials.

Highlights

  • Randomised controlled trials (RCT) may be hindered by slow recruitment rates, in critically ill patients

  • The trials recruited a total of 25,412 patients from 99 intensive care units (ICU), mainly in Australia and New Zealand (ANZ) and from Canada, the United Kingdom, Ireland, Saudi Arabia and Denmark

  • Key findings We have described a novel use of the Pareto principle by applying the Lorenz curve and Gini coefficient to analyse the distribution of patient recruitment into clinical trials

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Summary

Introduction

Randomised controlled trials (RCT) may be hindered by slow recruitment rates, in critically ill patients. Evidence from multicentre heart failure trials has shown that slow enrolment rates have remained unchanged over a 16-year period. Statistical models aimed at predicting recruitment rates and guiding adaptive adjustments in patient recruitment in pharmaceutical trials have been described [7, 8]. These models have been refined to predict recruitment rates at multiple levels including trial-level, region-level and site-level recruitment [8, 9]

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