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An optimal stratified Simon two-stage design.

ABSTRACT: In Phase II oncology trials, therapies are increasingly being evaluated for their effectiveness in specific populations of interest. Such targeted trials require designs that allow for stratification based on the participants' molecular characterisation. A targeted design proposed by Jones and Holmgren (JH) Jones CL, Holmgren E: 'An adaptive Simon two-stage design for phase 2 studies of targeted therapies', Contemporary Clinical Trials 28 (2007) 654-661.determines whether a drug only has activity in a disease sub-population or in the wider disease population. Their adaptive design uses results from a single interim analysis to decide whether to enrich the study population with a subgroup or not; it is based on two parallel Simon two-stage designs. We study the JH design in detail and extend it by providing a few alternative ways to control the familywise error rate, in the weak sense as well as the strong sense. We also introduce a novel optimal design by minimising the expected sample size. Our extended design contributes to the much needed framework for conducting Phase II trials in stratified medicine. © 2016 The Authors Pharmaceutical Statistics Published by John Wiley & Sons Ltd.

SUBMITTER: Parashar D

PROVIDER: S-EPMC5405342 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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An optimal stratified Simon two-stage design.

Parashar Deepak D Bowden Jack J Starr Colin C Wernisch Lorenz L Mander Adrian A

Pharmaceutical statistics 20160302 4

In Phase II oncology trials, therapies are increasingly being evaluated for their effectiveness in specific populations of interest. Such targeted trials require designs that allow for stratification based on the participants' molecular characterisation. A targeted design proposed by Jones and Holmgren (JH) Jones CL, Holmgren E: 'An adaptive Simon two-stage design for phase 2 studies of targeted therapies', Contemporary Clinical Trials 28 (2007) 654-661.determines whether a drug only has activit ...[more]

PMID: 26932771

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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.

Project description:Identification of novel biomarkers for risk prediction is important for disease prevention and optimal treatment selection. However, studies aiming to discover which biomarkers are useful for risk prediction often require the use of stored biological samples from large assembled cohorts, and thus the depletion of a finite and precious resource. To make efficient use of such stored samples, two-phase sampling designs are often adopted as resource-efficient sampling strategies, especially when the outcome of interest is rare. Existing methods for analyzing data from two-phase studies focus primarily on single marker analysis or fitting the Cox regression model to combine information from multiple markers. However, the Cox model may not fit the data well. Under model misspecification, the composite score derived from the Cox model may not perform well in predicting the outcome. Under a general two-phase stratified cohort sampling design, we present a novel approach to combining multiple markers to optimize prediction by fitting a flexible nonparametric transformation model. Using inverse probability weighting to account for the outcome-dependent sampling, we propose to estimate the model parameters by maximizing an objective function which can be interpreted as a weighted C-statistic for survival outcomes. Regardless of model adequacy, the proposed procedure yields a sensible composite risk score for prediction. A major obstacle for making inference under two phase studies is due to the correlation induced by the finite population sampling, which prevents standard inference procedures such as the bootstrap from being used for variance estimation. We propose a resampling procedure to derive valid confidence intervals for the model parameters and the C-statistic accuracy measure. We illustrate the new methods with simulation studies and an analysis of a two-phase study of high-density lipoprotein cholesterol (HDL-C) subtypes for predicting the risk of coronary heart disease.

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An optimal stratified Simon two-stage design.

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An optimal stratified Simon two-stage design.

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