Dataset Information

Commentary: considerations for using the 'Trials within Cohorts' design in a clinical trial of an investigational medicinal product.

ABSTRACT: BACKGROUND:The 'trials within cohorts' (TwiC) design is a pragmatic approach to randomised trials in which trial participants are randomly selected from an existing cohort. The design has multiple potential benefits, including the option of conducting multiple trials within the same cohort. MAIN TEXT:To date, the TwiC design methodology been used in numerous clinical settings but has never been applied to a clinical trial of an investigational medicinal product (CTIMP). We have recently secured the necessary approvals to undertake the first CTIMP using the TwiC design. In this paper, we describe some of the considerations and modifications required to ensure such a trial is compliant with Good Clinical Practice and international clinical trials regulations. We advocate using a two-stage consent process and using the consent stages to explicitly differentiate between trial participants and cohort participants who are providing control data. This distinction ensured compliance but had consequences with respect to costings, recruitment and the trial assessment schedule. CONCLUSION:We have demonstrated that it is possible to secure ethical and regulatory approval for a CTIMP TwiC. By including certain considerations at the trial design stage, we believe this pragmatic and efficient methodology could be utilised in other CTIMPs in future.

SUBMITTER: Bibby AC

PROVIDER: S-EPMC5759253 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Commentary: considerations for using the 'Trials within Cohorts' design in a clinical trial of an investigational medicinal product.

Bibby Anna C AC Torgerson David J DJ Leach Samantha S Lewis-White Helen H Maskell Nick A NA

Trials 20180108 1

<h4>Background</h4>The 'trials within cohorts' (TwiC) design is a pragmatic approach to randomised trials in which trial participants are randomly selected from an existing cohort. The design has multiple potential benefits, including the option of conducting multiple trials within the same cohort.<h4>Main text</h4>To date, the TwiC design methodology been used in numerous clinical settings but has never been applied to a clinical trial of an investigational medicinal product (CTIMP). We have re ...[more]

PMID: 29310706

Similar Datasets

Project description:BackgroundThere are many methodological challenges in the conduct and analysis of cluster randomised controlled trials, but one that has received little attention is that of post-randomisation changes to cluster composition. To illustrate this, we focus on the issue of cluster merging, considering the impact on the design, analysis and interpretation of trial outcomes.MethodsWe explored the effects of merging clusters on study power using standard methods of power calculation. We assessed the potential impacts on study findings of both homogeneous cluster merges (involving clusters randomised to the same arm of a trial) and heterogeneous merges (involving clusters randomised to different arms of a trial) by simulation. To determine the impact on bias and precision of treatment effect estimates, we applied standard methods of analysis to different populations under analysis.ResultsCluster merging produced a systematic reduction in study power. This effect depended on the number of merges and was most pronounced when variability in cluster size was at its greatest. Simulations demonstrate that the impact on analysis was minimal when cluster merges were homogeneous, with impact on study power being balanced by a change in observed intracluster correlation coefficient (ICC). We found a decrease in study power when cluster merges were heterogeneous, and the estimate of treatment effect was attenuated.ConclusionsExamples of cluster merges found in previously published reports of cluster randomised trials were typically homogeneous rather than heterogeneous. Simulations demonstrated that trial findings in such cases would be unbiased. However, simulations also showed that any heterogeneous cluster merges would introduce bias that would be hard to quantify, as well as having negative impacts on the precision of estimates obtained. Further methodological development is warranted to better determine how to analyse such trials appropriately. Interim recommendations include avoidance of cluster merges where possible, discontinuation of clusters following heterogeneous merges, allowance for potential loss of clusters and additional variability in cluster size in the original sample size calculation, and use of appropriate ICC estimates that reflect cluster size.

Project description:BACKGROUND/AIMS:Clinical trials for Alzheimer's disease have been aimed primarily at persons who have cognitive symptoms at enrollment. However, researchers are now recognizing that the pathophysiological process of Alzheimer's disease begins years, if not decades, prior to the onset of clinical symptoms. Successful intervention may require intervening early in the disease process. Critical issues arise in designing clinical trials for primary and secondary prevention of Alzheimer's disease including determination of sample sizes and follow-up duration. We address a number of these issues through application of a unifying multistate model for the preclinical course of Alzheimer's disease. A multistate model allows us to specify at which points during the long disease process the intervention exerts its effects. METHODS:We used a nonhomogeneous Markov multistate model for the progression of Alzheimer's disease through preclinical disease states defined by biomarkers, mild cognitive impairment and Alzheimer's disease dementia. We used transition probabilities based on several published cohort studies. Sample size methods were developed that account for factors including the initial preclinical disease state of trial participants, the primary endpoint, age-dependent transition and mortality rates and specifications of which transition rates are the targets of the intervention. RESULTS:We find that Alzheimer's disease prevention trials with a clinical primary endpoint of mild cognitive impairment or Alzheimer's disease dementia will require sample sizes of the order many thousands of individuals with at least 5 years of follow-up, which is larger than most Alzheimer's disease therapeutic trials conducted to date. The reasons for the large trial sizes include the long and variable preclinical period that spans decades, high rates of attrition among elderly populations due to mortality and losses to follow-up and potential selection effects, whereby healthier subjects enroll in prevention trials. A web application is available to perform sample size calculations using the methods reported here. CONCLUSION:Sample sizes based on multistate models can account for the points in the disease process when interventions exert their effects and may lead to more accurate sample size determinations. We will need innovative strategies to help design Alzheimer's disease prevention trials with feasible sample size requirements and durations of follow-up.

Dataset Information

Commentary: considerations for using the 'Trials within Cohorts' design in a clinical trial of an investigational medicinal product.

Publications

Commentary: considerations for using the 'Trials within Cohorts' design in a clinical trial of an investigational medicinal product.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure