Dataset Information

The fragility index can be used for sample size calculations in clinical trials.

ABSTRACT:

Objective

The fragility index is a clinically interpretable metric increasingly used to interpret the robustness of clinical trials results that is generally not incorporated in sample size calculation and applied post-hoc. In this manuscript, we propose to base the sample size calculation on the fragility index in a way that supplements the classical prefixed alpha and power cutoffs and we provide a dedicated R software package for the design and analysis tools.

Study design and setting

This approach follows from a novel hypothesis testing framework that is based on the fragility index and builds on the classical testing approach. As case studies, we re-analyse the design of two important trials in cardiovascular medicine, the FAME and FAMOUS-NSTEMI trials.

Results

The analyses show that approach returns sample sizes which results in a higher power for the P value based test and most importantly a lower and context dependent Type I error rate for the fragility index based test compared to standard tests.

Conclusion

Our method allows clinicians to control for the fragility index during clinical trial design.

SUBMITTER: Baer BR

PROVIDER: S-EPMC8665025 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Publications

The fragility index can be used for sample size calculations in clinical trials.

Baer Benjamin R BR Gaudino Mario M Fremes Stephen E SE Charlson Mary M Wells Martin T MT

Journal of clinical epidemiology 20210815

<h4>Objective</h4>The fragility index is a clinically interpretable metric increasingly used to interpret the robustness of clinical trials results that is generally not incorporated in sample size calculation and applied post-hoc. In this manuscript, we propose to base the sample size calculation on the fragility index in a way that supplements the classical prefixed alpha and power cutoffs and we provide a dedicated R software package for the design and analysis tools.<h4>Study design and sett ...[more]

PMID: 34403756

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Project description:BackgroundAn adaptive design allows modifying the design based on accumulated data while maintaining trial validity and integrity. The final sample size may be unknown when designing an adaptive trial. It is therefore important to consider what sample size is used in the planning of the study and how that is communicated to add transparency to the understanding of the trial design and facilitate robust planning. In this paper, we reviewed trial protocols and grant applications on the sample size reporting for randomised adaptive trials.MethodWe searched protocols of randomised trials with comparative objectives on ClinicalTrials.gov (01/01/2010 to 31/12/2022). Contemporary eligible grant applications accessed from UK publicly funded researchers were also included. Suitable records of adaptive designs were reviewed, and key information was extracted and descriptively analysed.ResultsWe identified 439 records, and 265 trials were eligible. Of these, 164 (61.9%) and 101 (38.1%) were sponsored by industry and public sectors, respectively, with 169 (63.8%) of all trials using a group sequential design although trial adaptations used were diverse. The maximum and minimum sample sizes were the most reported or directly inferred (n = 199, 75.1%). The sample size assuming no adaptation would be triggered was usually set as the estimated target sample size in the protocol. However, of the 152 completed trials, 15 (9.9%) and 33 (21.7%) had their sample size increased or reduced triggered by trial adaptations, respectively. The sample size calculation process was generally well reported in most cases (n = 216, 81.5%); however, the justification for the sample size calculation parameters was missing in 116 (43.8%) trials. Less than half gave sufficient information on the study design operating characteristics (n = 119, 44.9%).ConclusionAlthough the reporting of sample sizes varied, the maximum and minimum sample sizes were usually reported. Most of the trials were planned for estimated enrolment assuming no adaptation would be triggered. This is despite the fact a third of reported trials changed their sample size. The sample size calculation was generally well reported, but the justification of sample size calculation parameters and the reporting of the statistical behaviour of the adaptive design could still be improved.

Project description:BackgroundEfficacy of an intervention is commonly evaluated using P values, in addition to effect size measures such as absolute risk reduction, relative risk reduction, and numbers needed to treat. However, these measures are not always intuitive to clinicians. The fragility index (FI) is a more intuitive number that can facilitate interpretation but can only be used with binary outcomes. FI is the minimum number of patients who must be moved from the nonevent group to the event group to turn a significant result nonsignificant. In this retrospective analysis, we assessed the robustness of cardiovascular randomized controlled trials (RCTs), which report a positive (statistically significant) primary outcome by using the FI.Methods and resultsWe searched Medline from 2007 to 2017 to identify cardiovascular RCTs published in 6 high impact journals (The Lancet, New England Journal of Medicine, Journal of the American Medical Association, Circulation, Journal of the American College of Cardiology and European Heart Journal). Only RCTs with sample sizes >500 and a 2-by-2 factorial design or dichotomous primary outcomes were selected. FI was calculated using a defined approach. Among the cohort of 123 RCTs that met inclusion criteria, median FI was 13 (interquartile range, 5-26). In 28 trials (22.8%), FI ranged between 1 and 4. In 37 trials (30.1%), number of patients lost to follow-up was higher than the FI. Pharmaceutical interventions had higher FI compared with other interventions, FI=19 (7-52; P=0.002). Median FI varied according to subspecialty (electrophysiology=2; heart failure=11; interventional cardiology=8; P=0.020) and multiregional RCTs had higher FI=22 (12-53.25; P=0.023). FI did not differ based on risk of bias indicators, funding, or publication year.ConclusionsConsiderable variations in FI were observed among cardiovascular trials, suggesting the need for careful interpretation of results, particularly when number of patients lost to follow-up exceeds FI.

Project description:BackgroundMulti-centre randomized controlled clinical trials play an important role in modern evidence-based medicine. Advantages of collecting data from more than one site are numerous, including accelerated recruitment and increased generalisability of results. Mixed models can be applied to account for potential clustering in the data, in particular when many small centres contribute patients to the study. Previously proposed methods on sample size calculation for mixed models only considered balanced treatment allocations which is an unlikely outcome in practice if block randomisation with reasonable choices of block length is used.MethodsWe propose a sample size determination procedure for multi-centre trials comparing two treatment groups for a continuous outcome, modelling centre differences using random effects and allowing for arbitrary sample sizes. It is assumed that block randomisation with fixed block length is used at each study site for subject allocation. Simulations are used to assess operation characteristics such as power of the sample size approach. The proposed method is illustrated by an example in disease management systems.ResultsA sample size formula as well as a lower and upper boundary for the required overall sample size are given. We demonstrate the superiority of the new sample size formula over the conventional approach of ignoring the multi-centre structure and show the influence of parameters such as block length or centre heterogeneity. The application of the procedure on the example data shows that large blocks require larger sample sizes, if centre heterogeneity is present.ConclusionUnbalanced treatment allocation can result in substantial power loss when centre heterogeneity is present but not considered at the planning stage. When only few patients by centre will be recruited, one has to weigh the risk of imbalance between treatment groups due to large blocks and the risk of unblinding due to small blocks. The proposed approach should be considered when planning multi-centre trials.

Dataset Information

The fragility index can be used for sample size calculations in clinical trials.

Objective

Study design and setting

Results

Conclusion

Publications

The fragility index can be used for sample size calculations in clinical trials.

Similar Datasets

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