Hypertonic Saline in Bronchiolitis and Type I Error: A Trial Sequential Analysis

Harrison, Wade N; Angoulvant, François; House, Samantha A.; Gajdos, Vincent; Ralston, Shawn L.

doi:10.1542/peds.2018-1144

Cited by 24 publications

(16 citation statements)

References 45 publications

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“…Conventional meta-analysis methods do not consider the amount of the available evidence in relation to the required sample size (9)(10)(11). The reliability of a statistically signi cant intervention effect generated by meta-analysis is often overvalued, particularly where sparse data (number of events and participants) or repetitive analyses (type I errors) are seen (8,(12)(13)(14). In other situations, intervention effects that are not statistically signi cant are often interpreted as showing that the intervention has no effect, and it is assumed that no more evidence is required (type II errors) (15,16).…”

Section: Conventional Meta-analysismentioning

confidence: 99%

Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention

Claire

Gluud

Berlin

et al. 2020

Preprint

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Background Assessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately powered randomised clinical trials. When a randomised clinical trial is conducted, results from feasibility and pilot trials may be disregarded in terms of benefits and harms. Methods We describe using feasibility and pilot trial data in the Trial Sequential Analysis program to estimate the required sample size for one or more trials investigating a behavioural smoking cessation intervention. We show how data from a new, planned trial can be combined with data from the earlier trials using Trial Sequential Analysis to assess the intervention’s effects. Results We provide a worked example to illustrate how we successfully used Trial Sequential Analysis methods to argue for the research funds needed to undertake a randomised clinical trial. Conclusions Trial Sequential Analysis can utilise data from feasibility and pilot trials as well as other trials, to estimate a sample size for one or more future randomised clinical trials. As this method uses available data, estimated sample sizes may be smaller than they would have been using conventional sample size estimation methods.

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Section: Conventional Meta-analysismentioning

confidence: 99%

Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention

Claire

Gluud

Berlin

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…W tym samym roku ukazała się praca, w której wykorzystano metodę analizy sekwencyjnej (TSA, Trial Sequential Analysis) w celu oceny, czy dostępne metaanalizy są wystarczające do wyciągnięcia jednoznacznych wniosków na temat skuteczności HRS w terapii zapalenia oskrzelików u dzieci. Zgodnie z TSA metaanalizy wykazujące wpływ nebulizacji HRS na zmniejszenie ryzyka hospitalizacji oraz skrócenie czasu hospitalizacji mogą dostarczać wyników fałszywie dodatnich [49].…”

Section: Zapalenie Oskrzelików U Dzieciunclassified

Hipertoniczne roztwory soli w terapii chorób dróg oddechowych

et al. 2020

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“…Conventional metaanalysis methods do not consider the amount of the available evidence in relation to the required sample size (11)(12)(13). The reliability of a statistically signi cant intervention effect generated by meta-analysis is often overvalued, particularly where sparse data (number of events and participants) or repetitive analyses (type I errors) are employed (6,10,14,15). In other situations, intervention effects that are not statistically signi cant are often interpreted as showing that the intervention has no effect, and it is assumed that no more evidence is required (type II errors) (16,17).…”

Section: Conventional Meta-analysismentioning

confidence: 99%

Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention

Claire

Gluud

Berlin

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Assessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately powered randomised clinical trials. When a randomised clinical trial is conducted, results from feasibility and pilot trials may be disregarded in terms of benefits and harms.Methods: We describe using feasibility and pilot trial data in the Trial Sequential Analysis software to estimate the required sample size for one or more trials investigating a behavioural smoking cessation intervention. We show how data from a new, planned trial can be combined with data from the earlier trials using trial sequential analysis methods to assess the intervention's effects. Results: We provide a worked example to illustrate how we successfully used the Trial Sequential Analysis software to arrive at a sensible sample size for a new randomised clinical trial and use it in the argumentation for research funds for the trial. Conclusions: Trial Sequential Analysis can utilise data from feasibility and pilot trials as well as other trials, to estimate a sample size for one or more, similarly designed, future randomised clinical trials. As this method uses available data, estimated sample sizes may be smaller than they would have been using conventional sample size estimation methods.

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Hypertonic Saline in Bronchiolitis and Type I Error: A Trial Sequential Analysis

Abstract: TSA reveals that concluding benefit from HS for children with bronchiolitis potentially represents type I error.

Cited by 24 publications

References 45 publications

Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention

Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention

Hipertoniczne roztwory soli w terapii chorób dróg oddechowych

Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention

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