Dataset Information

Spatial Two-stage Designs for Phase II Clinical Trials.

ABSTRACT: A common endpoint in a single-arm phase II study is tumor response as a binary variable. Two widely used designs for such a study are Simon's two-stage minimax and optimal designs. The minimax design minimizes the maximal sample size and the optimal design minimizes the expected sample size under the null hypothesis. The optimal design generally has the larger total sample size than the minimax design, but its first stage's sample size is smaller than that of the minimax design. The difference in the total sample size between two types of designs can be large and so both designs can be unappealing to investigators. We develop novel designs that compromise on the two optimality criteria and avoid such occurrences using the spatial information on the first stage's required sample size and the total required sample size. We study properties of these spatial designs and show our proposed designs have advantages over Simon's designs and one of its extensions by Lin and Shih. As applications, we construct spatial designs for real-life studies on patients with Hodgkin disease and another study on effect of head and neck cancer on apnea.

SUBMITTER: Kim S

PROVIDER: S-EPMC8765730 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Spatial Two-stage Designs for Phase II Clinical Trials.

Kim Seongho S Wong Weng Kee WK

Computational statistics & data analysis 20220106

A common endpoint in a single-arm phase II study is tumor response as a binary variable. Two widely used designs for such a study are Simon's two-stage minimax and optimal designs. The minimax design minimizes the maximal sample size and the optimal design minimizes the expected sample size under the null hypothesis. The optimal design generally has the larger total sample size than the minimax design, but its first stage's sample size is smaller than that of the minimax design. The difference i ...[more]

PMID: 35058669

Similar Datasets

Project description:BackgroundGiven the inherent challenges of conducting randomized phase III trials in older cancer patients, single-arm phase II trials which assess the feasibility of a treatment that has already been shown to be effective in a younger population may provide a compelling alternative. Such an approach would need to evaluate treatment feasibility based on a composite endpoint that combines multiple clinical dimensions and to stratify older patients as fit or frail to account for the heterogeneity of the study population to recommend an appropriate treatment approach. In this context, stratified adaptive two-stage designs for binary or composite endpoints, initially developed for biomarker studies, allow to include two subgroups whilst maintaining competitive statistical performances. In practice, heterogeneity may indeed affect more than one dimension and incorporating co-primary endpoints, which independently assess each individual clinical dimension, would therefore appear quite pertinent. The current paper presents a novel phase II design for co-primary endpoints which takes into account the heterogeneity of a population. METHODS: We developed a stratified adaptive Bryant & Day design based on the Jones et al. and Parashar et al. algorithm. This two-stage design allows to jointly assess two dimensions (e.g. activity and toxicity) in two different subgroups. The operating characteristics of this new design were evaluated using examples and simulation comparisons with the Bryant & Day design in the context where the study population is stratified according to a pre-defined criterion.ResultsSimulation results demonstrated that the new design minimized the expected and maximum sample sizes as compared to parallel Bryant & Day designs (one in each subgroup), whilst controlling type I error rates and maintaining a competitive statistical power as well as a high probability of detecting heterogeneity.ConclusionsIn a heterogeneous population, this two-stage stratified adaptive phase II design provides a useful alternative to classical one and allows to identify a subgroup of interest without dramatically increasing sample size. As heterogeneity is not limited to older populations, this new design may also be relevant to other study populations such as children or adolescents and young adults or the development of targeted therapies based on a biomarker.

Dataset Information

Spatial Two-stage Designs for Phase II Clinical Trials.

Publications

Spatial Two-stage Designs for Phase II Clinical Trials.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets