Dataset Information

Risk-proportionate clinical trial monitoring: an example approach from a non-commercial trials unit.

ABSTRACT:

Background

Some level of monitoring is usually required during a clinical trial to protect the rights and safety of trial participants and to safeguard the quality and reliability of trial results. Although there is increasing support for the use of risk-proportionate approaches to achieve these aims, the variety of methods and lack of an empirical evidence base can present challenges for clinical trial practitioners.

Methods

This paper describes the monitoring methods and procedures that are utilised by a non-commercial clinical trials unit which coordinates a range of clinical trials across a variety of clinical areas with different associated risks.

Results

Monitoring activities and approaches should be selected to be proportionate to the risks identified within a trial. A risk-proportionate approach to monitoring is described giving details of methods that may be considered by clinical trial practitioners during the development of a trial monitoring plan. An example risk assessment and corresponding monitoring plan for a low risk (type A in the Medicines and Healthcare Products Regulatory Agency (MHRA) classification system) pediatric trial is provided for illustration.

Conclusion

We present ideas for developing a monitoring plan for a clinical trial of an investigational medicinal product based on our experience. Alternative approaches may be relevant or preferable in other settings based on inherent risk.

SUBMITTER: Tudur Smith C

PROVIDER: S-EPMC4022377 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Publications

Risk-proportionate clinical trial monitoring: an example approach from a non-commercial trials unit.

Tudur Smith Catrin C Williamson Paula P Jones Ashley A Smyth Alan A Hewer Simon Langton SL Gamble Carrol C

Trials 20140416

<h4>Background</h4>Some level of monitoring is usually required during a clinical trial to protect the rights and safety of trial participants and to safeguard the quality and reliability of trial results. Although there is increasing support for the use of risk-proportionate approaches to achieve these aims, the variety of methods and lack of an empirical evidence base can present challenges for clinical trial practitioners.<h4>Methods</h4>This paper describes the monitoring methods and procedu ...[more]

PMID: 24739398

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Project description:BackgroundAccurate prediction of subject recruitment, which is critical to the success of a study, remains an ongoing challenge. Previous prediction models often rely on parametric assumptions which are not always met or may be difficult to implement. We aim to develop a novel method that is less sensitive to model assumptions and relatively easy to implement.MethodsWe create a weighted resampling-based approach to predict enrollment in year two based on recruitment data from year one of the completed GRIPS and PACE clinical trials. Different weight functions accounted for a range of potential enrollment trajectory patterns. Prediction accuracy was measured by Euclidean distance for enrollment sequence in year two, total enrollment over time, and total weeks to enroll a fixed number of subjects, against the actual year two enrollment data. We compare the performance of the proposed method with an existing Bayesian method.ResultsWeighted resampling using GRIPS data resulted in closer prediction evidenced by better coverage of observed enrollment with the prediction intervals and smaller Euclidean distance from actual enrollment in year 2, especially when enrollment gaps were filled prior to the weighted resampling. These scenarios also produced more accurate predictions for total enrollment and number of weeks to enroll 50 participants. These same scenarios outperformed an existing Bayesian method for all 3 accuracy measures. In PACE data, using a reduced year 1 enrollment resulted in closer prediction evidenced by better coverage of observed enrollment with the prediction intervals and smaller Euclidean distance from actual enrollment in year 2, with the weighted resampling scenarios better reflecting the seasonal variation seen in year (1) The reduced enrollment scenarios resulted in closer prediction for total enrollment over 6 and 12 months into year (2) These same scenarios also outperformed an existing Bayesian method for relevant accuracy measures.ConclusionThe results demonstrate the feasibility and flexibility for a resampling-based, non-parametric approach for prediction of clinical trial recruitment with limited early enrollment data. Application to a wider setting and long-term prediction accuracy require further investigation.

Project description:BACKGROUND:Investigator-led multicentre randomised trials are essential to generate evidence on the optimal use of medical interventions. These non-commercial trials are often hampered by underfunding, which may lead to difficulties in gathering a team with the necessary expertise, a delayed trial start, slow recruitment and even early trial discontinuation. As a new public funder of pragmatic clinical trials, the KCE Trials programme was committed to correctly pay all trial activities in order to assure timely delivery of high-quality trial results. As no appropriate trial budget tool was readily publicly available that took into account the costs for the sponsor as well as the costs for participating sites, we developed a tool to make the budgeting of a clinical trial efficient, transparent and fair across applicants. METHODS:All trial-related activities of the sponsor and sites were categorised, and cost drivers were identified. All elements were included in a spreadsheet tool allowing the sponsor team to calculate in detail the various activities of a clinical trial and to appreciate the budget impact of specific cost drivers, e.g. a delay in recruitment. Hourly fees by role were adapted from published data. Fixed amounts per activity were developed when appropriate. RESULTS:This publicly available tool has already been used for 17 trials funded since the start of the KCE Trials programme in 2016, and it continues to be used and improved. This budget tool is used together with additional risk-reducing measures such as a multistep selection process with advance payments, a recruitment feasibility check by sponsor and funder, a close monitoring of study progress and a milestone-based payment schedule with the last payment made when the manuscript is submitted. CONCLUSIONS:The budget tool helps the KCE Trials programme to answer relevant research questions in a timely way, within budget and with high quality, a necessary condition to achieve impact of this programme for patients, clinical practice and healthcare payers.

Dataset Information

Risk-proportionate clinical trial monitoring: an example approach from a non-commercial trials unit.

Background

Methods

Results

Conclusion

Publications

Risk-proportionate clinical trial monitoring: an example approach from a non-commercial trials unit.

Similar Datasets

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