Sample size calculations in pediatric clinical trials conducted in an ICU: a systematic review

Nikolakopoulos, Stavros; Roes, Kit C. B.; Lee, Johanna H. van der; Tweel, Ingeborg van der

doi:10.1186/1745-6215-15-274

Cited by 12 publications

(9 citation statements)

References 30 publications

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“…More specifically, assessing the feasibility of a trial, which is directly connected with robust sample size calculations, becomes a difficult task. When the sample size is calculated based on limited information, this increases the risk of under- or overestimation of the sample size, and possibly a failed trial [ 2 , 3 ]. Furthermore, such scarcity of information can have considerable impact on the regulatory process and evaluation of the available evidence for the risk/benefit assessment of a new drug, and possible market authorization [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Rare disease registries: potential applications towards impact on development of new drug treatments

Weide

Gaasterland

Roes

et al. 2018

Orphanet J Rare Dis

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BackgroundLow prevalence, lack of knowledge about the disease course, and phenotype heterogeneity hamper the development of drugs for rare diseases. Rare disease registries (RDRs) can be helpful by playing a role in understanding the course of the disease, and providing information necessary for clinical trial design, if designed and maintained properly. We describe the potential applications of a RDR and what type of information should be incorporated to support the design of clinical trials in the process of drug development, based on a broad inventory of registry experience. We evaluated two existing RDRs in more detail to check the completeness of these RDRs for trial design.ResultsBefore and during the application for regulatory approval a RDR can improve the efficiency and quality in clinical trial design by informing the sample size calculation and expected disease course. In exceptional circumstances information from RDRs has been used as historical controls for a one-armed clinical trial, and high quality RDRs may be used for registry-based randomized controlled trials. In the post marketing phase of (conditional) drug approval a disease-specific RDR is likely to provide more relevant information than a product-specific registry.ConclusionsA RDR can be very helpful to improve the efficiency and quality of clinical trial design in several ways. To enable the applicability and optimal use of a RDR longitudinal data collection is indispensable, and specific data collection, prepared for repeated measurement, is needed. The developed checklist can help to define the appropriate variables to include. Attention should be paid to the inclusion of patient-relevant outcome measures in the RDR from the start. More research and experience is needed on the possibilities and limitations of combining RDR information with clinical trial data to maximize the availability of relevant evidence for regulatory decisions in rare diseases.

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Section: Introductionmentioning

confidence: 99%

Rare disease registries: potential applications towards impact on development of new drug treatments

Weide

Gaasterland

Roes

et al. 2018

Orphanet J Rare Dis

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“…6 Major deficiencies in sample size calculations were also reported by other specialties. [7][8][9][10][11] Previous reports indicated significant improvement in the frequency of reporting of sample size calculation from 52 to 86% in anaesthesia journals. 12 13 However, the integrity of sample size reporting for RCTs published in anaesthesia literature was not previously evaluated.…”

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confidence: 99%

Pitfalls in reporting sample size calculation in randomized controlled trials published in leading anaesthesia journals: a systematic review

Abdulatif

Mukhtar

Obayah

2015

British Journal of Anaesthesia

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We have evaluated the pitfalls in reporting sample size calculation in randomized controlled trials (RCTs) published in the 10 highest impact factor anaesthesia journals.Superiority RCTs published in 2013 were identified and checked for the basic components required for sample size calculation and replication. The difference between the reported and replicated sample size was estimated. The sources used for estimating the expected effect size (Δ) were identified, and the difference between the expected and observed effect sizes (Δ gap) was estimated.We enrolled 194 RCTs. Sample size calculation was reported in 91.7% of studies. Replication of sample size calculation was possible in 80.3% of studies. The original and replicated sample sizes were identical in 67.8% of studies. The difference between the replicated and reported sample sizes exceeded 10% in 28.7% of studies. The expected and observed effect sizes were comparable in RCTs with positive outcomes (P=0.1). Studies with negative outcome tended to overestimate the effect size (Δ gap 42%, 95% confidence interval 32-51%), P<0.001. Post hoc power of negative studies was 20.2% (95% confidence interval 13.4-27.1%). Studies using data derived from pilot studies for sample size calculation were associated with the smallest Δ gaps (P=0.008).Sample size calculation is frequently reported in anaesthesia journals, but the details of basic elements for calculation are not consistently provided. In almost one-third of RCTs, the reported and replicated sample sizes were not identical and the assumptions for the expected effect size and variance were not supported by relevant literature or pilot studies.

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“…Sample size calculations are, therefore, a balancing act, where inaccurate estimation can unnecessarily put patients at risk or lead to a large waste of resources. Sample size calculations require the investigator to make several assumptions, which are well known to be arbitrary with a high risk of mis-specification 32 33. Our results reveal a systematic mis-specification among ALS clinical trials with an 18.9% overestimation of the hazard rate.…”

Section: Discussionmentioning

confidence: 81%

Critical design considerations for time-to-event endpoints in amyotrophic lateral sclerosis clinical trials

Eijk

Nikolakopoulos

Roes

et al. 2019

J Neurol Neurosurg Psychiatry

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BackgroundFunding and resources for low prevalent neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS) are limited, and optimising their use is vital for efficient drug development. In this study, we review the design assumptions for pivotal ALS clinical trials with time-to-event endpoints and provide optimised settings for future trials.MethodsWe extracted design settings from 13 completed placebo-controlled trials. Optimal assumptions were estimated using parametric survival models in individual participant data (n=4991). Designs were compared in terms of sample size, trial duration, drug use and costs.ResultsPrevious trials overestimated the hazard rate by 18.9% (95% CI 3.4% to 34.5%, p=0.021). The median expected HR was 0.56 (range 0.33–0.66). Additionally, we found evidence for an increasing mean hazard rate over time (Weibull shape parameter of 2.03, 95% CI 1.93 to 2.15, p<0.001), which affects the design and planning of future clinical trials. Incorporating accrual time and assuming an increasing hazard rate at the design stage reduced sample size by 33.2% (95% CI 27.9 to 39.4), trial duration by 17.4% (95% CI 11.6 to 23.3), drug use by 14.3% (95% CI 9.6 to 19.0) and follow-up costs by 21.2% (95% CI 15.6 to 26.8).ConclusionsImplementing distributional knowledge and incorporating accrual at the design stage could achieve large gains in the efficiency of ALS clinical trials with time-to-event endpoints. We provide an open-source platform that helps investigators to make more accurate sample size calculations and optimise the use of their available resources.

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Sample size calculations in pediatric clinical trials conducted in an ICU: a systematic review

Cited by 12 publications

References 30 publications

Rare disease registries: potential applications towards impact on development of new drug treatments

Rare disease registries: potential applications towards impact on development of new drug treatments

Pitfalls in reporting sample size calculation in randomized controlled trials published in leading anaesthesia journals: a systematic review

Critical design considerations for time-to-event endpoints in amyotrophic lateral sclerosis clinical trials

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