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Comparison of three sample size calculation methods for non-inferiority vaccine trials with multiple continuous co-primary endpoints.

ABSTRACT: Clinical trials that study immunogenicity of combination vaccines often have less power than desirable. To make up for the reduction in statistical power at the study level, researchers have to increase the study sample size. To study immunogenicity variables, we used the geometric mean concentration of immune response after vaccination as immunologic endpoint and compared 3 sample size calculation methods: the "Inflation factors" method, the "Incrementing" method, and the "Bonferroni correction" method when there are multiple continuous co-primary endpoints. The parameters were set according to the actual situation of the use of combination vaccines and the simulation results were used as reference. The present study demonstrates that all 3 methods are applicable when the effect size of each endpoint is similar and the endpoints are at most weakly correlated, but when there is a true difference in effect sizes among endpoints, the "Incrementing" method has the best performance.

SUBMITTER: Yang J

PROVIDER: S-EPMC6363135 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Comparison of three sample size calculation methods for non-inferiority vaccine trials with multiple continuous co-primary endpoints.

Yang Jiaying J Li Jingxin J Wang Shiyuan S Luo Li L Liu Pei P

Human vaccines & immunotherapeutics 20181004 1

Clinical trials that study immunogenicity of combination vaccines often have less power than desirable. To make up for the reduction in statistical power at the study level, researchers have to increase the study sample size. To study immunogenicity variables, we used the geometric mean concentration of immune response after vaccination as immunologic endpoint and compared 3 sample size calculation methods: the "Inflation factors" method, the "Incrementing" method, and the "Bonferroni correction ...[more]

PMID: 30273507

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Project description:BACKGROUND:Multi-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. METHODS:We 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. RESULTS:A 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. CONCLUSION:Unbalanced 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.

Project description:BackgroundObservational burden of illness studies are used in pharmacoepidemiology to address a variety of objectives, including contextualizing the current treatment setting, identifying important treatment gaps, and providing estimates to parameterize economic models. Methodologies such as retrospective chart review may be utilized in settings for which existing datasets are not available or do not include sufficient clinical detail. While specifying the number of charts to be extracted and/or determining whether the number that can feasibly extracted will be clinically meaningful is an important study design consideration, there is a lack of rigorous methods available for sample size calculation in this setting. The objective of this study was to develop recommended sample size calculations for use in such studies.MethodsCalculations for identifying the optimal feasible sample size calculations were derived, for studies characterizing treatment patterns and medical costs, based on the ability to comprehensively observe treatments and maximize precision of resulting 95% confidence intervals. For cost outcomes, if the standard deviation is not known, the coefficient of variation cv can be used as an alternative. A case study of a chart review of advanced melanoma (MELODY) was used to characterize plausible values for cv in a real-world example.ResultsAcross sample sizes, any treatment given with greater than 1% frequency has a high likelihood of being observed. For a sample of size 200, and a treatment given to 5% of the population, the precision of a 95% confidence interval (CI) is expected to be ±0.03. For cost outcomes, for the median cv value observed in the MELODY study (0.72), a sample size of approximately 200 would be required to generate a 95% CI precise to within ±10% of the mean.ConclusionThis study presents a formal guidance on sample size calculations for retrospective burden of illness studies. The approach presented here is methodologically rigorous and designed for practical application in real-world retrospective chart review studies.

Project description:BackgroundMultiple primary outcomes may be specified in randomised controlled trials (RCTs). When analysing multiple outcomes it's important to control the family wise error rate (FWER). A popular approach to do this is to adjust the p-values corresponding to each statistical test used to investigate the intervention effects by using the Bonferroni correction. It's also important to consider the power of the trial to detect true intervention effects. In the context of multiple outcomes, depending on the clinical objective, the power can be defined as: 'disjunctive power', the probability of detecting at least one true intervention effect across all the outcomes or 'marginal power' the probability of finding a true intervention effect on a nominated outcome. We provide practical recommendations on which method may be used to adjust for multiple comparisons in the sample size calculation and the analysis of RCTs with multiple primary outcomes. We also discuss the implications on the sample size for obtaining 90% disjunctive power and 90% marginal power.MethodsWe use simulation studies to investigate the disjunctive power, marginal power and FWER obtained after applying Bonferroni, Holm, Hochberg, Dubey/Armitage-Parmar and Stepdown-minP adjustment methods. Different simulation scenarios were constructed by varying the number of outcomes, degree of correlation between the outcomes, intervention effect sizes and proportion of missing data.ResultsThe Bonferroni and Holm methods provide the same disjunctive power. The Hochberg and Hommel methods provide power gains for the analysis, albeit small, in comparison to the Bonferroni method. The Stepdown-minP procedure performs well for complete data. However, it removes participants with missing values prior to the analysis resulting in a loss of power when there are missing data. The sample size requirement to achieve the desired disjunctive power may be smaller than that required to achieve the desired marginal power. The choice between whether to specify a disjunctive or marginal power should depend on the clincial objective.

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Comparison of three sample size calculation methods for non-inferiority vaccine trials with multiple continuous co-primary endpoints.

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Comparison of three sample size calculation methods for non-inferiority vaccine trials with multiple continuous co-primary endpoints.

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