Sample size calculations for a split‐cluster, beta‐binomial design in the assessment of toxicity

Hendriks, Jan C.M.; Teerenstra, Steven; Zalm, J. P. E. Punt-Van der; Wetzels, A.M.M.; Westphal, J. R.; Borm, George F.

doi:10.1002/sim.2412

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…If ρ = 0, then the variance of R 1 degenerates into the binomial variance. After estimation of the parameters (a,b), the sample size and test statistics can be calculated based on the type of difference to be detected (Hendriks et al, 2005). It should be noted that the estimation of the parameters does not require subgroup source knowledge, prognostic or predictive, about the heterogeneity; only the estimated amount of variation.…”

Section: Methodsmentioning

confidence: 99%

Modeling Heterogeneity in Phase II Clinical Trials

Barnes¹

2010

American Journal of Biostatistics

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Problem statement: The common assumption in non-randomized Phase II clinical trials is a homogeneous population with homogeneous response. This assumption is at odds with many trials today; a heterogeneous response due to the existence of subgroups. Approach: In order to examine the effects of heterogeneity on the trial outcome, a systematic platform is developed to quantify the range and classes of possible response heterogeneity using a mixed model approach. Five recent methods developed to handle heterogeneity, stratified analysis, beta-binomial models, Bayesian hierarchical models and regression models are compared and contrasted using a set of performance criteria to provide clinicians with scenarios where each method is applicable. Results: All methods require a priori information on the subgroup composition, a limiting factor in most trial conduct. The Bayesian methods require the least amount of assumptions, provide a methodology to share information across subgroups and allow partial subgroup outcomes, but require substantial computational resources and time. The stratified methods provide a simple improvement over the standard phase II Simon design, but lack the methodology to allow for partial subgroup stopping. Conclusion: The heterogeneity model provides a useful tool to model data under a heterogeneity assumption. The proper handling of heterogeneous populations under a Phase II design is a contentious debate; ignoring this fundamental assumption may lead to incorrect trial outcomes. New methods need to be developed which can include the heterogeneity structure in the trial design and allow for partial hypothesis testing.

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Section: Methodsmentioning

confidence: 99%

Modeling Heterogeneity in Phase II Clinical Trials

Barnes¹

2010

American Journal of Biostatistics

View full text Add to dashboard Cite

show abstract

“…The third method, the beta-binomial distribution has been previously proposed as a model that can account for heterogeneity in binary outcome models (Makuch, Stephens et al 1989;Yamamoto and Yanagimoto 1994;Hendriks, Teerenstra et al 2005 The mean and variance are (24) a where TC = 0…”

Section: Beta-binomial Methodsmentioning

confidence: 99%

“…After estimation of the parameters (a o ' b o ) , the sample size and test statistics can be calculated based on the type of difference to be detected (Hendriks, Teerenstra et al 2005;Chow, Shao et al 2007). It should be noted that the estimation of the parameters does not require subgroup source knowledge, prognostic or predictive, about the heterogeneity; only the estimated amount of variation.…”

Section: Beta-binomial Methodsmentioning

confidence: 99%

A phase II two stage clinical trial design to handle latent heterogeneity for a binary response.

Barnes¹

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Phase II clinical trial are generally single arm trial where a homogeneity assumption is placed on the response. In practice, this assumption may be violated resulting in a heterogeneous response. This heterogeneous or overdispersed response can be decomposed into distinct subgroups based on the etiology of the heterogeneity. A general classification model is developed to quantify the heterogeneity. The most common Phase II trial design used in practice is the Simon 2-stage design which relies on the assumption of response homogeneity. This design is shown to be flawed under the assumption of heterogeneity with errors exceeding the target trial errors. To correct for the error inflation, a modification is made to the Simon design if heterogeneity is detected after the first stage trial conduct. The trial sample size is increased using an empirical estimate for the variance inflation factor and the trial is then completed with design parameters constructed through the posterior predictive Beta-binomial distribution given the first stage results. The new design, denoted the 2-stage Heterogeneity Adaptive (2HA) design, is applied to a two subgroup problem under latent heterogeneity. Latent heterogeneity represents the most general form of heterogeneity, no information is known prior to trial conduct. The results, through simulation, show that the target errors can be v maintained with this modification to the Simon design under a wide range of heterogeneity.VI

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Toxicity testing of human assisted reproduction devices using the mouse embryo assay

Zalm

Hendriks

Westphal

et al. 2009

Reproductive BioMedicine Online

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Systems to assess the toxicity of materials used in human assisted reproduction currently lack efficiency and/or sufficient discriminatory power. The development of 1-cell CBA/B6 F1 hybrid mouse embryos to blastocysts, expressed as blastocyst rate (BR), is used to measure toxicity. The embryos were divided into control and test groups, and were exposed to either control medium or to a potentially toxic test medium. Inferences on toxicity were based on differences in BR between the two groups. The mouse embryo assay followed a stratified (mouse), randomized (embryo), and balanced (equal number of embryos per group and per mouse) design. The number of embryos needed was calculated using power analysis. The basal BR of the hybrid strain was determined in a historical population. Sixty-nine mouse embryos per group were required to detect toxic materials with sufficient sensitivity and to account for the considerable inter-mouse variation in blastocyst development. Fifty-two samples, divided over batches of seven different products were tested before use in the study IVF centre and five of these were found to be toxic. This test system, presented as the Nijmegen mouse embryo assay (NMEA), can be used to detect embryo-toxic materials in daily IVF practice, and this report may provide a starting point for standardization.

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Sample size calculations for a split‐cluster, beta‐binomial design in the assessment of toxicity

Cited by 5 publications

References 19 publications

Modeling Heterogeneity in Phase II Clinical Trials

Modeling Heterogeneity in Phase II Clinical Trials

A phase II two stage clinical trial design to handle latent heterogeneity for a binary response.

Toxicity testing of human assisted reproduction devices using the mouse embryo assay

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