Adaptive Population Enrichment (APE)
Reporting
Reporting of APE trials
PANDA users may wish to read discussions on reporting issues in APE randomised trials with illustrations based on real-life examples 1, 2. Researchers should follow the Adaptive designs CONSORT Extension (ACE) guideline when reporting an APE trial results which aims to enhance interpretability of results 3, 4. Researchers are encouraged to read the explanatory and elaboration text of this guidance as well as across referenced examples. Specifically, the following key elements should be clearly described in a report of results:
- scientific rationale of the design;
- evidence to support beliefs that some subpopulations may benefit from the treatment more than others;
- how subpopulations were classed;
- criteria for selecting subpopulations at interim analyses and how evidence of benefit is claimed at interim analyses and the final analysis;
- hypotheses consistent with the research questions;
- statistical methods for the design (e.g. calculating sample sizes and/or assessing operating characteristics of the study), monitoring, and analysis;
- analytical derivation of any statistical quantities or information where relevant (e.g., see 5, 6);
- baseline characteristics by treatment group for each subpopulation and full population;
- patient flowchart reflecting all subpopulations;
- interim and final results by treatment arm for each subpopulation and the target full population, and;
- interpretation of the final results should focus on to whom the final results are applicable.
If population selection happens at the interim analysis, the final results may only apply to the selected subpopulations. As such, the overall interpretation should be clear to readers concerning which patient population(s) the results pertain to.
References
1. Wang et al. Adaptive enrichment with subpopulation selection at interim: Methodologies, applications and design considerations. Contemp Clin Trials. 2013;36(2):673-681.
2. Wassmer et al. Designing issues in confirmatory adaptive population enrichment trials. J Biopharm Stat. 2015;25(4):651-669.
3. Dimairo et al. The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. BMJ. 2020;369:m115.
4. Dimairo et al. The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. Trials. 2020;21:528.
5. Sugitani et al. Flexible alpha allocation strategies for confirmatory adaptive enrichment clinical trials with a prespecified subgroup. Stat Med. 2018;37(24):3387-3402.
6. Mehta et al. An adaptive population enrichment phase III trial of TRC105 and pazopanib versus pazopanib alone in patients with advanced angiosarcoma (TAPPAS trial). Ann Oncol. 2019;30(1):103-108.
2. Wassmer et al. Designing issues in confirmatory adaptive population enrichment trials. J Biopharm Stat. 2015;25(4):651-669.
3. Dimairo et al. The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. BMJ. 2020;369:m115.
4. Dimairo et al. The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. Trials. 2020;21:528.
5. Sugitani et al. Flexible alpha allocation strategies for confirmatory adaptive enrichment clinical trials with a prespecified subgroup. Stat Med. 2018;37(24):3387-3402.
6. Mehta et al. An adaptive population enrichment phase III trial of TRC105 and pazopanib versus pazopanib alone in patients with advanced angiosarcoma (TAPPAS trial). Ann Oncol. 2019;30(1):103-108.