Prevalence and reporting of recruitment, randomisation and treatment errors in clinical trials: A systematic review

Yelland, Lisa N; Kahan, Brennan C; Dent, Elsa; Lee, Katherine J.; Voysey, Merryn; Forbes, Andrew Benjamin; Cook, Jonathan

doi:10.1177/1740774518761627

Cited by 19 publications

(24 citation statements)

References 13 publications

(40 reference statements)

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“…Of the remaining findings, a large proportion related to patient eligibility (recognised as a problem in other trials), 29 unreported serious adverse events and unreported time-to-event endpoints (in these RCTs, death or cancer progression). Processes to check eligibility prior to randomisation include collection of more detailed data on trial forms to verify eligibility (e.g.…”

Section: Discussionmentioning

confidence: 99%

Assessing the potential for prevention or earlier detection of on-site monitoring findings from randomised controlled trials: Further analyses of findings from the prospective TEMPER triggered monitoring study

et al. 2020

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Background/Aims: Clinical trials should be designed and managed to minimise important errors with potential to compromise patient safety or data integrity, employ monitoring practices that detect and correct important errors quickly, and take robust action to prevent repetition. Regulators highlight the use of risk-based monitoring, making greater use of centralised monitoring and reducing reliance on centre visits. The TEMPER study was a prospective evaluation of triggered monitoring (a risk-based monitoring method), whereby centres are prioritised for visits based on central monitoring results. Conducted in three UK-based randomised cancer treatment trials of investigational medicine products with time-to-event outcomes, it found high levels of serious findings at triggered centre visits but also at visits to matched control centres that, based on central monitoring, were not of concern. Here, we report a detailed review of the serious findings from TEMPER centre visits. We sought to identify feasible, centralised processes which might detect or prevent these findings without a centre visit. Methods: The primary outcome of this study was the proportion of all ‘major’ and ‘critical’ TEMPER centre visit findings theoretically detectable or preventable through a feasible, centralised process. To devise processes, we considered a representative example of each finding type through an internal consensus exercise. This involved (a) agreeing the potential, by some described process, for each finding type to be centrally detected or prevented and (b) agreeing a proposed feasibility score for each proposed process. To further assess feasibility, we ran a consultation exercise, whereby the proposed processes were reviewed and rated for feasibility by invited external trialists. Results: In TEMPER, 312 major or critical findings were identified at 94 visits. These findings comprised 120 distinct issues, for which we proposed 56 different centralised processes. Following independent review of the feasibility of the proposed processes by 87 consultation respondents across eight different trial stakeholder groups, we conclude that 306/312 (98%) findings could theoretically be prevented or identified centrally. Of the processes deemed feasible, those relating to informed consent could have the most impact. Of processes not currently deemed feasible, those involving use of electronic health records are among those with the largest potential benefit. Conclusions: This work presents a best-case scenario, where a large majority of monitoring findings were deemed theoretically preventable or detectable by central processes. Caveats include the cost of applying all necessary methods, and the resource implications of enhanced central monitoring for both centre and trials unit staff. Our results will inform future monitoring plans and emphasise the importance of continued critical review of monitoring processes and outcomes to ensure they remain appropriate.

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Section: Discussionmentioning

confidence: 99%

Assessing the potential for prevention or earlier detection of on-site monitoring findings from randomised controlled trials: Further analyses of findings from the prospective TEMPER triggered monitoring study

et al. 2020

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“…Of the remaining findings, a large proportion related to patient eligibility (recognised as a problem in other trials 28 ), unreported Serious Adverse Events and unreported time-to-event endpoints (in these RCTs, death or cancer progression). Processes to check eligibility prior to randomisation include collection of more detailed data on trial forms to verify eligibility (e.g.…”

Section: Discussionmentioning

confidence: 99%

Assessing the potential for prevention or earlier detection of on-site monitoring findings from randomised controlled trials: further analyses of findings from the prospective TEMPER triggered monitoring study

Cragg

Hurley

Yorke-Edwards

et al. 2020

Preprint

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Background/Aims Clinical trials should be designed and managed to minimise important errors with potential to compromise patient safety or data integrity, employ monitoring practices that detect and correct important errors quickly, and take robust action to prevent repetition. Regulators highlight the use of risk-based monitoring, making greater use of centralised monitoring and reducing reliance on centre visits. The TEMPER study was a prospective evaluation of triggered monitoring (a risk-based monitoring method), whereby centres are prioritised for visits based on central monitoring results. Conducted in three UK-based randomised cancer treatment trials of investigational medicine products with time-to-event outcomes, it found high levels of serious findings at triggered centre visits but also at visits to matched control centres that, based on central monitoring, were not of concern. Here, we report a detailed review of the serious findings from TEMPER centre visits. We sought to identify feasible, centralised processes which might detect or prevent these findings without a centre visit. Methods We considered a representative example of each finding type through a three-staged consensus exercise. 1: Three trialists independently reviewed the issues and graded their potential for detection by central monitoring or prevention by some described process, each on a five-point scale. 2. Results of round 1 were shared anonymously and each trialist re-reviewed the list choosing whether or not to change their grading. 3. The three trialists discussed and agreed grades for any issues without consensus, and proposed a feasibility score for each proposed process. In a consultation exercise, the proposed processes were reviewed and rated for feasibility by an invited external trialist group. The primary outcome was the proportion of all major and critical findings theoretically detectable or preventable through a feasible, centralised process. Results In TEMPER, 312 major or critical findings were identified at 94 visits. These findings comprised 120 distinct issues, for which we proposed 56 different centralised processes. Following independent review of the feasibility of the proposed processes by 87 consultation respondents across different stakeholder groups, we conclude that 306/312 (98%) findings could theoretically be prevented or identified centrally. Conclusions This work presents a best-case scenario, where a large majority of monitoring findings were deemed theoretically preventable or detectable by central processes. Caveats include the cost of applying all necessary methods, and the resource implications of enhanced central monitoring for both centre and trials unit staff. Of processes not currently deemed feasible, use of electronic health records has the largest potential benefit. Our results will inform future monitoring plans and emphasise the importance of continued critical review of monitoring processes and outcomes to ensure they remain appropriate. Key words: monitoring, central monitoring, on-site monitoring, risk-based monitoring, efficient trial conduct

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“…During data preprocessing for multi-omics data integration, our analyses suggested that samples of 12 patients in the MMRC dataset (5% of the total number of patients) were annotated incorrectly due to sample swaps and other unknown causes. Sample-labeling errors occur in clinics [87], clinical trials [88], and research databases [52]. Analyses based on error-containing datasets might decrease the power of the datasets, but may also lead to incorrect or contradictory scientific conclusions, or even harming patients [89,90].…”

Section: Discussionmentioning

confidence: 99%

A Network Analysis of Multiple Myeloma Related Gene Signatures

Liu

Yoo

et al. 2019

Cancers

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Multiple myeloma (MM) is the second most prevalent hematological cancer. MM is a complex and heterogeneous disease, and thus, it is essential to leverage omics data from large MM cohorts to understand the molecular mechanisms underlying MM tumorigenesis, progression, and drug responses, which may aid in the development of better treatments. In this study, we analyzed gene expression, copy number variation, and clinical data from the Multiple Myeloma Research Consortium (MMRC) dataset and constructed a multiple myeloma molecular causal network (M3CN). The M3CN was used to unify eight prognostic gene signatures in the literature that shared very few genes between them, resulting in a prognostic subnetwork of the M3CN, consisting of 178 genes that were enriched for genes involved in cell cycle (fold enrichment = 8.4, p value = 6.1 × 10−26). The M3CN was further used to characterize immunomodulators and proteasome inhibitors for MM, demonstrating the pleiotropic effects of these drugs, with drug-response signature genes enriched across multiple M3CN subnetworks. Network analyses indicated potential links between these drug-response subnetworks and the prognostic subnetwork. To elucidate the structure of these important MM subnetworks, we identified putative key regulators predicted to modulate the state of these subnetworks. Finally, to assess the predictive power of our network-based models, we stratified MM patients in an independent cohort, the MMRF-CoMMpass study, based on the prognostic subnetwork, and compared the performance of this subnetwork against other signatures in the literature. We show that the M3CN-derived prognostic subnetwork achieved the best separation between different risk groups in terms of log-rank test p-values and hazard ratios. In summary, this work demonstrates the power of a probabilistic causal network approach to understanding molecular mechanisms underlying the different MM signatures.

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Prevalence and reporting of recruitment, randomisation and treatment errors in clinical trials: A systematic review

Cited by 19 publications

References 13 publications

Assessing the potential for prevention or earlier detection of on-site monitoring findings from randomised controlled trials: Further analyses of findings from the prospective TEMPER triggered monitoring study

Assessing the potential for prevention or earlier detection of on-site monitoring findings from randomised controlled trials: Further analyses of findings from the prospective TEMPER triggered monitoring study

Assessing the potential for prevention or earlier detection of on-site monitoring findings from randomised controlled trials: further analyses of findings from the prospective TEMPER triggered monitoring study

A Network Analysis of Multiple Myeloma Related Gene Signatures

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