Efstathia Gkioni1, Susanna Dodd2, Roser Rius3, Carrol Gamble2. 1. Department of Biostatistics, University of Liverpool, A Member of Liverpool Health Partners, Liverpool, UK; Université de Paris, CRESS, INSERM, INRA, F-75004 Paris, France. Electronic address: e.gkioni@liverpool.ac.uk. 2. Department of Biostatistics, University of Liverpool, A Member of Liverpool Health Partners, Liverpool, UK. 3. Department of Statistics and Operations Research, School of Mathematics and Statistics, BarcelonaTech (UPC), Barcelona, Spain.
Abstract
OBJECTIVE: Identify the current practice for recruitment prediction and monitoring within clinical trials. STUDY DESIGN AND SETTING: Chief investigators (CIs) were surveyed to identify data sources and adjustments made to support recruitment prediction. Statisticians were surveyed to determine methods and adjustments used when predicting and monitoring recruitment. Participants were identified from the National Institute for Health Research recently funded studies, the UK Clinical Research Collaboration registered Clinical Trial Units network or by the European Clinical Research Infrastructure Network. RESULTS: A total of 51 CIs (UK = 32, ECRIN = 19) and 104 statisticians (UK = 51, ECRIN = 53) were contacted. Response rates varied (CIs UK = 53% ECRIN = 32%; statisticians UK = 98% ECRIN = 36%). Multiple data sources are used to support recruitment rates, most commonly audit data from multiple sites. Variation in individual site recruitment rates are frequently incorporated, but staggered site openings were featured more commonly among UK respondents. Simple prediction methods are preferred to rarely used statistical models. Lack of familiarity with statistical methods are barriers to their use with evidence needed to justify the time required to support their implementation. CONCLUSION: Simplistic methods will continue as the mainstay of prediction; however, generation of evidence supporting the benefits of complex statistical models should promote their implementations. Multiple data sources to support recruitment prediction are being used, and further work on the quality of these data is needed. Pressure to be optimistic about recruitment rates for the trial to be attractive to funders was felt by a sizable minority.
OBJECTIVE: Identify the current practice for recruitment prediction and monitoring within clinical trials. STUDY DESIGN AND SETTING: Chief investigators (CIs) were surveyed to identify data sources and adjustments made to support recruitment prediction. Statisticians were surveyed to determine methods and adjustments used when predicting and monitoring recruitment. Participants were identified from the National Institute for Health Research recently funded studies, the UK Clinical Research Collaboration registered Clinical Trial Units network or by the European Clinical Research Infrastructure Network. RESULTS: A total of 51 CIs (UK = 32, ECRIN = 19) and 104 statisticians (UK = 51, ECRIN = 53) were contacted. Response rates varied (CIs UK = 53% ECRIN = 32%; statisticians UK = 98% ECRIN = 36%). Multiple data sources are used to support recruitment rates, most commonly audit data from multiple sites. Variation in individual site recruitment rates are frequently incorporated, but staggered site openings were featured more commonly among UK respondents. Simple prediction methods are preferred to rarely used statistical models. Lack of familiarity with statistical methods are barriers to their use with evidence needed to justify the time required to support their implementation. CONCLUSION: Simplistic methods will continue as the mainstay of prediction; however, generation of evidence supporting the benefits of complex statistical models should promote their implementations. Multiple data sources to support recruitment prediction are being used, and further work on the quality of these data is needed. Pressure to be optimistic about recruitment rates for the trial to be attractive to funders was felt by a sizable minority.
Authors: Richard M Jacques; Rashida Ahmed; James Harper; Adya Ranjan; Isra Saeed; Rebecca M Simpson; Stephen J Walters Journal: BMJ Open Date: 2022-02-14 Impact factor: 2.692