Improving the pre-screening of eligible patients in order to increase enrollment in cancer clinical trials

Abstract

BackgroundThe performance of randomized controlled trials (RCTs) is often hindered by recruitment difficulties. This study aims to explore the pre-screening phase of four prostate cancer RCTs to identify the impact of a systematic pre-selection of eligible patients for RCT recruitment.MethodsThe pre-screening of four RCTs opened at the Comprehensive Cancer Center in Rennes was analyzed retrospectively (French Genitourinary Tumor Group (GETUG) 14, 15, 16, and 17). Data were extracted from electronic multidisciplinary cancer (MDC) reports and manually completed by physicians and medical secretaries. These data were the main source of information for clinicians to discuss treatment alternatives during MDC sessions. The pre-screening decisions made by the clinicians during these MDC meetings were compared with those made after a systematic review of the MDC reports by a clinical research assistant (CRA). Any inconsistencies in decisions between the CRA and the MDC physicians were corrected by the principal investigator (PI).ResultsThe pre-screening rate was 9.1% during the MDC meetings, while it was estimated to be 12.9% after the final review by the PI, and 29% after the systematic review by the CRA. The study showed that 77% and 67% of the MDC reports did not mention clinical and pathological Tumor, lymph node and metastasis classification of malignant tumors (TNM) staging, respectively, and that 35 of the CRA’s 47 proposals rejected by the PI concerned implicit information (not specified in the MDC reports). Only one patient was proposed by the PI, and none by the CRA.ConclusionsThese results confirm that pre-screening could be improved by a systematic review of the medical reports. They also highlight the fact that missing data in electronic MDC reports leads to over-enrollment of non-eligible patients, but not to over-exclusion of eligible patients. Thus, our study confirms the potential gain in using semi-automated pre-selection of MDC reports, in order to avoid missing out on patients eligible for RCTs.Trial registrationThe trials evaluated in this study were previously registered with clinicaltrials.gov (registration number: NCT00104741 on 3 March 2005; NCT00104715 on 3 March 2005; NCT00423475 on 16 January 2007; and NCT00667069 on 24 April 2008).

Randomised controlled trials as part of clinical care: A seven‐step routinisation framework proposal

“…Routinising pre‐screening of eligible patients in order to increase enrolment in RCTs was one of the suggestions to increase the recruitment rate and routinise RCTs in clinical care . The research and clinical nurses in our study said that they would routinely perform pre‐screening of patients' logs prior to the clinic.…”

“…The researchers highlighted that pre‐screening of eligible patients could be performed prospectively and retrospectively, and practicing both approaches helped to ensure that no eligible patients were missed. Some large centres use semi‐automated pre‐selection, which was found to be effective . There is evidence that the regular use of screening logs would optimise the recruitment …”

Sample size determinations for stepped-wedge clinical trials from a three-level data hierarchy perspective

“…The extension might be possible by modeling those outcomes with generalized estimating equations or non-linear mixed-effects models although derivations of closed forms could be intractable. For this reason, sample size determinations based on simulation approaches might be preferable as attempted by Baio et al 11 Nonetheless, we suspect that simulation of non-normal data with multi-level data hierarchy for a specified correlation structure would be challenging particularly because correlations may well vary with means on which variances depend unlike normal distributions. Therefore, it would also be interesting to examine if extensions based on normal approximations would be comparable.…”

“…Woertman et al 10 converted Hussey and Hughes’ formula to a design effect of the SW design in comparison with a conventional two parallel arm design. Baio et al 11 also suggested a design effect formula under a different setting and statistical model. Hemming et al 12 evaluated the impact of intra-cluster correlations on statistical power or sample size through design effects under various types of SW designs.…”

“…Hemming et al 12 evaluated the impact of intra-cluster correlations on statistical power or sample size through design effects under various types of SW designs. In addition, simulation studies for power analysis without explicit formula have also been conducted by Biao et al 11 and Van den Heuvel et al 13 …”