On the efficiency of adaptive sample size design

Zhang, Lanju

doi:10.1002/sim.8034

Cited by 7 publications

(7 citation statements)

References 28 publications

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“…14 Second, we focus on GSDs and CHW designs with the same number of analyses, 16,18 not allowing GSDs with many more interim analyses as approximations to CHW designs. 8,14 Third, our performance evaluation criteria 18 are based on robust or stable statistical power for a range of 𝛿 values instead of the principle solely for pointwise power superiority. 14 It is worth pointing out that, contrary to the thought that a D CHW always downweights the data collected after the interim analysis for sample size determination, a D CHW can both downweight or upweight such later collected data.…”

Section: Discussionmentioning

confidence: 99%

“…14 This view was questioned later from different angles. [15][16][17] In a recent study, Cui and Zhang 18 introduced the concepts of uniformly more efficient (UME) and approximately uniformly more efficient (AUME) SSADs with respect to an interval of the parameter. Using simulations, they showed that given 2204 CUI a GSD, a fully adaptive SSAD could be obtained that was uniformly or approximately uniformly more efficient than the GSD.…”

Section: Introductionmentioning

confidence: 99%

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Sample size adaptation designs and efficiency comparison with group sequential designs

Cui

2024

Statistics in Medicine

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This study is to give a systematic account of sample size adaptation designs (SSADs) and to provide direct proof of the efficiency advantage of general SSADs over group sequential designs (GSDs) from a different perspective. For this purpose, a class of sample size mapping functions to define SSADs is introduced. Under the two‐stage adaptive clinical trial setting, theorems are developed to describe the properties of SSADs. Sufficient conditions are derived and used to prove analytically that SSADs based on the weighted combination test can be uniformly more efficient than GSDs in a range of likely values of the true treatment difference . As shown in various scenarios, given a GSD, a fully adaptive SSAD can be obtained that has sufficient statistical power similar to that of the GSD but has a smaller average sample size for all in the range. The associated sample size savings can be substantial. A practical design example and suggestions on the steps to find efficient SSADs are also provided.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sample size adaptation designs and efficiency comparison with group sequential designs

Cui

2024

Statistics in Medicine

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“…14 In such a situation, an adaptive trial design might be helpful to reduce the sample size required. 15 If this is not feasible, the laboratory confirmation of RSV infection is a minimal requirement in the prospective observational studies, as experienced in the case of cystic fibrosis, another condition at risk for severe RSV disease but not included in the standard indication. 16 In summary, our ITS analysis did not find that in Japan there was a population-level benefit in terms of reduced hospitalizations of an expanded program of palivizumab prophylaxis for children with DS without a standard indication.…”

Section: Respiratory Syncytial Virus; Lower Respiratory Infection; Hospitalization; a Quasiexperimental Study Design; Passive Immunizatiomentioning

confidence: 99%

Universal palivizumab prophylaxis for children with Down syndrome in Japan: analysis with interrupted time-series

Takeuchi

Kawakami

2020

Human Vaccines & Immunotherapeutics

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Down syndrome (DS) is an independent risk factor for severe respiratory syncytial virus (RSV) infection. Palivizumab -passive immunization for RSV -is the only pharmacological measure for preventing severe disease. In most countries, palivizumab is indicated in young children with congenital heart disease, premature birth, and chronic lung disease. In Japan, since 2013, children with DS, but without such "standard" risk factors, have been able to receive insurance-covered palivizumab prophylaxis, but its effectiveness of policy is unknown. From a nationwide database, we extracted data of children with DS who hospitalized for RSV-related lower respiratory infections (LRTIs), from April 2010 to January 2019. Using an interrupted time-series design, we analyzed data from before and after the introduction of the universal palivizumab prophylaxis program for DS children in Japan. As a result, we identified a total of 152 RSV-related LRTIs in 147 children hospitalized with DS. With time-series analysis, we did not observe a significant change in both level (−1.07, P = .11) and slope (0.26 per 12 months, P = .30), before and after 2013. In summary, the expansion of the palivizumab prophylaxis program to all children with DS in Japan was not associated with a reduction in RSV-related hospitalization in these children.

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“…5,6 With arbitrarily chosen timing and policy for sample size determination, the method provides great flexibility in sizing a clinical trial. In their subsequent works, Cui et al further developed methods for performance evaluation and comparison of different SSA designs, [10][11][12][13][14] which were critical for understanding and implementing the designs, and directly related to their solutions to the problem of design optimization. Other relevant works to mention include the methods proposed by Lehmacher and Wassmer 7 and by Shen and Fisher.…”

Section: Introductionmentioning

confidence: 99%

An automation-based adaptive seamless design for dose selection and confirmation with improved power and efficiency

Zhan

Zhang

Geng

et al. 2021

Stat Methods Med Res

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In a drug development program, the efficacy and safety of multiple doses can be evaluated in patients through a phase 2b dose ranging study. With a demonstrated dose response in the trial, promising doses are identified. Their effectiveness then is further investigated and confirmed in phase 3 studies. Although this two-step approach serves the purpose of the program, in general, it is inefficient because of its prolonged development duration and the exclusion of the phase 2b data in the final efficacy evaluation and confirmation which are only based on phase 3 data. To address the issue, we propose a new adaptive design, which seamlessly integrates the dose finding and confirmation steps under one pivotal study. Unlike existing adaptive seamless phase 2b/3 designs, the proposed design combines the response adaptive randomization, sample size modification, and multiple testing techniques to achieve better efficiency. The design can be easily implemented through an automated randomization process. At the end, a number of targeted doses are selected and their effectiveness is confirmed with guaranteed control of family-wise error rate.

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On the efficiency of adaptive sample size design

Cited by 7 publications

References 28 publications

Sample size adaptation designs and efficiency comparison with group sequential designs

Sample size adaptation designs and efficiency comparison with group sequential designs

Universal palivizumab prophylaxis for children with Down syndrome in Japan: analysis with interrupted time-series

An automation-based adaptive seamless design for dose selection and confirmation with improved power and efficiency

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