An optimal stratified Simon two-stage design

Cross-validated risk scores adaptive enrichment (CADEN) design

We propose a Cross-validated ADaptive ENrichment design (CADEN) in which a trial population is enriched with a subpopulation of patients who are predicted to benefit from the treatment more than an average patient (the sensitive group). This subpopulation is found using a risk score constructed from the baseline (potentially high-dimensional) information about patients. The design incorporates an early stopping rule for futility. Simulation studies are used to assess the properties of CADEN against the original (non-enrichment) cross-validated risk scores (CVRS) design which constructs a risk score at the end of the trial. We show that when there exists a sensitive group of patients, CADEN achieves a higher power and a reduction in the expected sample size compared to the CVRS design. We illustrate the application of the design in two real clinical trials. We conclude that the new design offers improved statistical efficiency over the existing non-enrichment method, as well as increased benefit to patients. The method has been implemented in an R package caden.

Isotonic design for single-arm biomarker stratified trials

In single-arm trials with a predefined subgroup based on baseline biomarkers, it is often assumed that a biomarker defined subgroup, the biomarker positive subgroup, has the same or higher response to treatment compared to its complement, the biomarker negative subgroup. The goal is to determine if the treatment is effective in each of the subgroups or in the biomarker positive subgroup only or not effective at all. We propose the isotonic stratified design for this problem. The design has a joint set of decision rules for biomarker positive and negative subjects and utilizes joint estimation of response probabilities using assumed monotonicity of response between the biomarker negative and positive subgroups. The new design reduces the sample size requirement when compared to running two Simon's designs in each biomarker positive and negative. For example, the new design requires 23%-35% fewer patients than running two Simon's designs for scenarios we considered. Alternatively, the new design allows evaluating the response probability in both biomarker negative and biomarker positive subgroups using only 40% more patients needed for running Simon's design in the biomarker positive subgroup only.

A Bayesian adaptive biomarker stratified phase II randomized clinical trial design for radiotherapies with competing risk survival outcomes

In recent decades, many phase II clinical trials have used survival outcomes as the primary endpoints. If radiotherapy is involved, the competing risk issue often arises because the time to disease progression can be censored by the time to normal tissue complications, and vice versa. Besides, many existing research has examined that patients receiving the same radiotherapy dose may yield distinct responses due to their heterogeneous radiation susceptibility statuses. Therefore, the "one-size-fits-all" strategy often fails, and it is more relevant to evaluate the subgroup-specific treatment effect with the subgroup defined by the radiation susceptibility status. In this paper, we propose a Bayesian adaptive biomarker stratified phase II trial design evaluating the subgroup-specific treatment effects of radiotherapy. We use the cause-specific hazard approach to model the competing risk survival outcomes. We propose restricting the candidate radiation doses based on each patient's radiation susceptibility status. Only the clinically feasible personalized dose will be considered, which enhances the benefit for the patients in the trial. In addition, we propose a stratified Bayesian adaptive randomization scheme such that more patients will be randomized to the dose reporting more favorable survival outcomes. Numerical studies and an illustrative trial example have shown that the proposed design performed well and outperformed the conventional design ignoring the competing risk issue.

Bayesian order constrained adaptive design for phase II clinical trials evaluating subgroup-specific treatment effect

The "one-size-fits-all'' paradigm is inappropriate for phase II clinical trials evaluating biotherapies, which are often expected to have substantial heterogeneous treatment effects among different subgroups defined by biomarker. For these biotherapies, the objective of phase II clinical trials is often to evaluate subgroup-specific treatment effects. In this article, we propose a simple yet efficient Bayesian adaptive phase II biomarker-guided design, referred to as the Bayesian-order constrained adaptive design, to detect the subgroup-specific treatment effects of biotherapies. The Bayesian order constrained adaptive design combines the features of the enrichment design and sequential design. It starts with a "all-comers" stage, and subsequently switches to an enrichment stage for either the marker-positive subgroup or marker-negative subgroup, depending on the interim analysis results. The go/no go enrichment criteria are determined by two posterior probabilities utilizing the inherent ordering constraint between two subgroups. We also extend the Bayesian-order constrained adaptive design to handle the missing biomarker situation. We conducted comprehensive computer simulation studies to investigate the operating characteristics of the Bayesian order constrained adaptive design, and compared it with other existing and conventional designs. The results shown that the Bayesian order constrained adaptive design yielded the best overall performance in detecting the subgroup-specific treatment effects by jointly considering the efficiency and cost-effectiveness of the trials. The software for simulation and trial implementation are available for free download.

A stratified adaptive two-stage design with co-primary endpoints for phase II clinical oncology trials

Background

Given 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.

Results

Simulation 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.

Conclusions

In 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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01748-w.

TBCRC 048: Phase II Study of Olaparib for Metastatic Breast Cancer and Mutations in Homologous Recombination-Related Genes

PURPOSE

Olaparib, a poly (ADP-ribose) polymerase (PARP) inhibitor (PARPi), is approved for the treatment of human epidermal growth factor receptor 2 (HER2)-negative metastatic breast cancer (MBC) in germline (g)BRCA1/2 mutation carriers. Olaparib Expanded, an investigator-initiated, phase II study, assessed olaparib response in patients with MBC with somatic (s)BRCA1/2 mutations or g/s mutations in homologous recombination (HR)-related genes other than BRCA1/2.

METHODS

Eligible patients had MBC with measurable disease and germline mutations in non-BRCA1/2 HR-related genes (cohort 1) or somatic mutations in these genes or BRCA1/2 (cohort 2). Prior PARPi, platinum-refractory disease, or progression on more than two chemotherapy regimens (metastatic setting) was not allowed. Patients received olaparib 300 mg orally twice a day until progression. A single-arm, two-stage design was used. The primary endpoint was objective response rate (ORR); the null hypothesis (≤ 5% ORR) would be rejected within each cohort if there were four or more responses in 27 patients. Secondary endpoints included clinical benefit rate and progression-free survival (PFS).

RESULTS

Fifty-four patients enrolled. Seventy-six percent had estrogen receptor-positive HER2-negative disease. Eighty-seven percent had mutations in PALB2, sBRCA1/2, ATM, or CHEK2. In cohort 1, ORR was 33% (90% CI, 19% to 51%) and in cohort 2, 31% (90% CI, 15% to 49%). Confirmed responses were seen only with gPALB2 (ORR, 82%) and sBRCA1/2 (ORR, 50%) mutations. Median PFS was 13.3 months (90% CI, 12 months to not available/computable [NA]) for gPALB2 and 6.3 months (90% CI, 4.4 months to NA) for sBRCA1/2 mutation carriers. No responses were observed with ATM or CHEK2 mutations alone.

CONCLUSION

PARP inhibition is an effective treatment for patients with MBC and gPALB2 or sBRCA1/2 mutations, significantly expanding the population of patients with breast cancer likely to benefit from PARPi beyond gBRCA1/2 mutation carriers. These results emphasize the value of molecular characterization for treatment decisions in MBC.

Auxiliary variable-enriched biomarker-stratified design

Clinical trials in the era of precision medicine require assessment of biomarkers to identify appropriate subgroups of patients for targeted therapy. In a biomarker-stratified design (BSD), biomarkers are measured on all patients and used as stratification variables. However, such a trial can be both inefficient and costly, especially when the prevalence of the subgroup of primary interest is low and the cost of assessing the biomarkers is high. Efficiency can be improved and costs reduced by using enriched biomarker-stratified designs, in which patients of primary interest, typically the biomarker-positive patients, are oversampled. We consider a special type of enrichment design, an auxiliary variable-enriched design (AEBSD), in which enrichment is based on some inexpensive auxiliary variable that is positively correlated with the true biomarker. The proposed AEBSD reduces the total cost of the trial compared with a standard BSD when the prevalence rate of true biomarker positivity is small and the positive predictive value (PPV) of the auxiliary biomarker is larger than the prevalence rate. In addition, for an AEBSD, we can immediately randomize the patients selected in the screening process without waiting for the result of the true biomarker test, reducing the treatment waiting time. We propose an adaptive Bayesian method to adjust the assumed PPV while the trial is ongoing. Numerical studies and an example illustrate the approach. An R package is available.

Addressing heterogeneity in the design of phase II clinical trials in geriatric oncology

INTRODUCTION

Cancer in the elderly is a major public issue. However, older patients have long been debarred from clinical trials. There is a high unmet medical need for specific trials addressing oncology strategies adapted to older patients' conditions. While randomised phase III trials remain the gold standard, they usually require large numbers of patients. In this perspective, late single-arm phase II trials assessing treatment feasibility might prove a good alternative. However, it is essential to take into account the heterogeneity in an ageing population characterised by frailty. Standard parallel phase II studies in defined frail and non-frail populations also require a high number of patients. Used in molecular subtyping and treatment effect heterogeneity, stratified adaptive designs can improve statistical performance, but they have never been used in geriatric oncology. This report describes their potential benefits and useful applications as compared with standard designs.

METHODS

In a heterogeneous population, stratified adaptive designs allowed us to select subgroups of interest in two stages. Operational characteristics were evaluated through simulations of clinical trials under different scenarios.

RESULTS

Simulations showed that the use of stratified adaptive designs can efficiently minimise both the number of patients to be included and accrual duration with competitive statistical power and high heterogeneity detection rate at interim analysis.

CONCLUSION

Compared with classical phase II designs, stratified adaptive phase II trial methodology offers a promising approach to improve clinical research in geriatric oncology. These designs may also be efficient in other populations such as children or adolescents and young adults.

Beyond p-values: A phase II dual-criterion design with statistical significance and clinical relevance

Background

Well-designed phase II trials must have acceptable error rates relative to a pre-specified success criterion, usually a statistically significant p-value. Such standard designs may not always suffice from a clinical perspective because clinical relevance may call for more. For example, proof-of-concept in phase II often requires not only statistical significance but also a sufficiently large effect estimate.

Purpose

We propose dual-criterion designs to complement statistical significance with clinical relevance, discuss their methodology, and illustrate their implementation in phase II.

Methods

Clinical relevance requires the effect estimate to pass a clinically motivated threshold (the decision value (DV)). In contrast to standard designs, the required effect estimate is an explicit design input, whereas study power is implicit. The sample size for a dual-criterion design needs careful considerations of the study’s operating characteristics (type I error, power).

Results

Dual-criterion designs are discussed for a randomized controlled and a single-arm phase II trial, including decision criteria, sample size calculations, decisions under various data scenarios, and operating characteristics. The designs facilitate GO/NO-GO decisions due to their complementary statistical–clinical criterion.

Limitations

While conceptually simple, implementing a dual-criterion design needs care. The clinical DV must be elicited carefully in collaboration with clinicians, and understanding similarities and differences to a standard design is crucial.

Conclusion

To improve evidence-based decision-making, a formal yet transparent quantitative framework is important. Dual-criterion designs offer an appealing statistical–clinical compromise, which may be preferable to standard designs if evidence against the null hypothesis alone does not suffice for an efficacy claim.

Designing phase II clinical trials to target subgroup of interest in a heterogeneous population: A case study using an R package

Phase II trials that evaluate target therapies based on a biomarker must be well designed in order to assess anti-tumor activity as well as clinical utility of the biomarker. Classical phase II designs do not deal with this molecular heterogeneity and can lead to an erroneous conclusion in the whole population, whereas a subgroup of patients may well benefit from the new therapy. Moreover, the target population to be evaluated in a phase III trial may be incorrectly specified. Alternative approaches are proposed in the literature that make it possible to include two subgroups according to biomarker status (negative/positive) in the same study. Jones, Parashar and Tournoux et al. propose different stratified adaptive two-stage designs to identify a subgroup of interest in a heterogeneous population that could possibly benefit from the experimental treatment at the end of the first or second stage. Nevertheless, these designs are rarely used in oncology research. After introducing these stratified adaptive designs, we present an R package (ph2hetero) implementing these methods. A case study is provided to illustrate both the designs and the use of the R package. These stratified adaptive designs provide a useful alternative to classical two-stage designs and may also provide options in contexts other than biomarker studies.

Single arm two-stage studies: Improved designs for molecularly targeted agents

Mechanistic understanding of cancers and their potential interactions with molecularly targeted agents is driving the need for stratified medicine to ensure each participant receives the best possible care. This understanding, backed by scientific research, should be used to guide the design of clinical trials for these agents. The mechanism of action of a molecularly targeted agent often suggests that a biomarker can be used as a predictor of activity of the agent on the targeted disease. A biomarker driven trial is needed to confirm that the molecularly targeted agent stratifies the participant population with disease into high and low responder groups. We assume that the biomarker of interest can be dichotomised and propose a balanced parallel two-stage single-arm phase II trial that builds on existing two-stage single-arm designs. A single-arm trial cannot distinguish between a marker being predictive in the population as a whole and the agent causing an increased response in the marker positive group, but it is a first step. We compare this approach to the existing single-arm approaches, sequential enrichment, tandem two-stage, and parallel two-stage designs, and discuss the advantages and disadvantages of each design. We show that our design compares favourably to existing designs in the Bayesian framework, making a more efficient use of collected data. We recommend using the parallel two-stage balanced or sequential enrichment designs when randomisation is not practical in a phase II trial.

Near-Infrared Imaging with Indocyanine Green for Detection of Endometriosis Lesions (Gre-Endo Trial): A Pilot Study

STUDY OBJECTIVE

To evaluate near-infrared radiation imaging with intravenous indocyanine green (NIR-ICG) during laparoscopic intervention to identify endometriosis lesions.

DESIGN

A single-center, prospective, single-arm pilot study (Canadian Task Force classification II-2).

SETTING

An academic tertiary care and research center.

PATIENTS

Twenty-seven patients with symptomatic endometriosis were enrolled.

INTERVENTIONS

Patients underwent laparoscopic surgery using a laparoscopic system prototype with NIR-ICG.

MEASUREMENTS AND MAIN RESULTS

A total of 116 suspected endometriosis lesions were removed from 27 patients. One hundred lesions had already been visualized in white light imaging by an expert surgeon; the remaining 16 were detected and removed using NIR-ICG. A total of 111 specimens were positive for endometriosis pathology. Positive predictive value of 95% and 97.8% and negative predictive value of 86.2% and 82.3% were found by white light imaging and NIR-ICG, respectively, with sensitivity of 85.6% and 82% and specificity of 95.2% and 97.9%, respectively.

CONCLUSION

NIR-ICG may be a tool for intraoperative diagnosis, confirmation of visible endometriosis lesions, and a marker for identifying occult endometriosis. Further prospective studies with a larger population sample are warranted to validate these encouraging preliminary results.

An optimal stratified Simon two-stage design

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