Standard 4: Determining Adequate Sample Sizes

Tweel, Ingeborg van der; Askie, Lisa; Vandermeer, Ben; Ellenberg, Susan S.; Fernandes, Ricardo M; Saloojee, Haroon; Bassler, Dirk; Altman, Douglas G.; Offringa, Martin; Lee, Johanna H. van der

doi:10.1542/peds.2012-0055g

Cited by 32 publications

(23 citation statements)

References 38 publications

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“…The Ntotal is determined for several standardised differences for 0.8 power. It should be noted that the normality assumption in this approach matches those of many other methods (Van Der Lee et al, 2009, Kim and Seo, 2013, van der Tweel et al, 2012, Lachin, 1981. Thus, these methods may not be suitable to estimate Ntotal for clinical surrogates that are severely skewed and heavy upper-tailed, like LoMV and many others despite their frequent use.…”

Section: Model-based Altman Nomogrammentioning

confidence: 96%

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Reducing the Length of Mechanical Ventilation with Significance: A Case Study of Sample Size Estimation Trial Design Using Monte-Carlo Simulation

et al. 2015

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The power of a study can be established with estimation of total effective sample size (Ntotal). In this study, the impact of the length of mechanical ventilation (LoMV) distribution shape in intensive care on the estimated Ntotal is investigated. This study provides an overview on the study design involving LoMV, the resulting potential limitations, and the criteria for a 'successful' design. Data from mechanical ventilated patients in a single-center intensive care unit were used in this study. The Ntotal was estimated using two methods: 1) Model-based Altman's nomogram (a standard); and 2) Monte-Carlo simulation. Using Monte-Carlo simulation, a patient selection criteria is imposed to estimate Ntotal from 'realistic' patient cohorts. The Altman nomogram shows that the Ntotal to detect a 25% change in LoMV (∆LoMV) at power of 0.8 is ≥1000 patients. For the Monte-Carlo simulation, a Ntotal ≥260 patients is needed to detect similar changes. It is important to consider the LoMV distribution shape and variability, particularly relative to target patient groups who might benefit from the intervention. Assessment of ∆LoMV in response to treatment should be carefully considered to avoid an under-powered studies. The Monte-Carlo simulation combined with objective patient selection provides better design of such studies.

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Section: Model-based Altman Nomogrammentioning

confidence: 96%

“…There are various guides on how to calculate an effective sample size (Van Der Lee et al, 2009, Kim and Seo, 2013, van der Tweel et al, 2012, Lachin, 1981. These studies emphasise the need for normally distributed population and use a standardised difference.…”

Section: Sample Size Estimation Using Monte-carlo Simulationmentioning

confidence: 99%

Reducing the Length of Mechanical Ventilation with Significance: A Case Study of Sample Size Estimation Trial Design Using Monte-Carlo Simulation

et al. 2015

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“…When convened, DMC membership should be broad enough to include individuals with clinical and methodological expertise and knowledge of local context, and the operations of these committees should be guided by a detailed charter. The authors provide recommendations on reporting on the DMC and its operations in manuscripts as this will inform the reader in interpretation of trial results. Determining adequate sample sizes :27 Unsurprisingly, most paediatric trials are smaller than adult trials, are more often single-centre and rarely report sample size calculations 28. A key reason for smaller sample sizes in paediatric research is the relative scarcity of many medical conditions in children.…”

Section: The First Six Star Child Health Reportsmentioning

confidence: 99%

“…Determining adequate sample sizes :27 Unsurprisingly, most paediatric trials are smaller than adult trials, are more often single-centre and rarely report sample size calculations 28. A key reason for smaller sample sizes in paediatric research is the relative scarcity of many medical conditions in children.…”

Section: The First Six Star Child Health Reportsmentioning

confidence: 99%

StaR Child Health: developing evidence-based guidance for the design, conduct and reporting of paediatric trials

Hoff

Offringa

2014

Arch Dis Child

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There has been a huge upsurge in clinical research in children in the last decade, stimulated in England by dedicated research infrastructure and support through the National Institute for Health Research. This infrastructure offering research design, expert review, trial management, research nurse, data support and dedicated facilities enables paediatricians to conduct more and better research. The challenge is how to design and conduct trials that will make a real difference to children's health. Standards for Research (StaR) in Child Health was founded in 2009 to address the paucity and shortcomings of paediatric clinical trials. This global initiative involves methodologists, clinicians, patient advocacy groups and policy makers dedicated to developing practical, evidence-based standards for enhancing the reliability and relevance of paediatric clinical research. In this overview, we highlight the contribution of StaR to this agenda, describe the international context, and suggest how StaR's future plans could be integrated with new and existing support for research.

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“…120 Second, the quality of pediatric research should be improved by adhering to the recently published Standards for Research in Child Health group and the International Headache Society guidelines (Table 10). [121][122][123][124][125][126][127] Future randomized trials should examine the comparative effectiveness of multimodal drug and disease management; long-term benefits, safety, and adherence with preventive treatments; and the role of specific characteristics of children that could modify benefits and harms with preventive drugs (Table 11).…”

mentioning

confidence: 99%

Episodic Migraines in Children

et al. 2013

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The authors conducted a systematic literature review of preventive pharmacological treatments for episodic childhood migraines searching several databases through May 20, 2012. Episodic migraine prevention was examined in 24 publications of randomized controlled trials that enrolled 1578 children in 16 nonrandomized studies. Single randomized controlled trials provided low-strength evidence that propranolol would result in complete cessation of migraine attacks in 713 per 1000 children treated (95% confidence interval, 452-974); trazodone and nimodipine decreased migraine days, while topiramate, divalproex, and clonidine were no more effective than placebo in preventing migraines. Migraine prevention with multidisciplinary drug management was not sustained at 6 months. Divalproex resulted in treatment discontinuation due to adverse effects, and topiramate increased the risk of paresthesia, upper respiratory tract infection, and weight loss. Long-term preventive benefits and improvement in disability and quality of life are unknown. No studies examined quality of life or provided evidence for individualized treatment decisions.

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Standard 4: Determining Adequate Sample Sizes

Cited by 32 publications

References 38 publications

Reducing the Length of Mechanical Ventilation with Significance: A Case Study of Sample Size Estimation Trial Design Using Monte-Carlo Simulation

Reducing the Length of Mechanical Ventilation with Significance: A Case Study of Sample Size Estimation Trial Design Using Monte-Carlo Simulation

StaR Child Health: developing evidence-based guidance for the design, conduct and reporting of paediatric trials

Episodic Migraines in Children

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