Designing phase II trials in cancer: a systematic review and guidance

Unveiling the Promise: A Comprehensive Review of Salpingectomy as a Vanguard for Ovarian Cancer Prevention

This comprehensive review explores the potential of salpingectomy as a groundbreaking strategy for the prevention of ovarian cancer. The discussion encompasses the biological rationale behind salpingectomy, emphasizing its foundation in the tubal hypothesis, which posits the fallopian tubes as a possible origin site for certain ovarian cancers. Ongoing clinical trials and observational studies provide evolving evidence supporting the safety and efficacy of salpingectomy, particularly in high-risk populations. The procedure's ethical considerations, including its impact on fertility and equitable access, are thoroughly examined. Implications for clinical practice underscore the importance of informed decision-making, risk-benefit assessments, and the integration of emerging evidence into reproductive health discussions. Looking ahead, the future landscape of ovarian cancer prevention involves continued research, technological innovations, and collaborative efforts to ensure a holistic and evidence-based approach. The goal is to forge a future where ovarian cancer is not only treatable but also preventable, with salpingectomy potentially playing a pivotal role in this transformative journey.

An Overview of Phase 2 Clinical Trial Designs

Clinical trials are studies to test new treatments in humans. Typically, these treatments are evaluated over several phases to assess their safety and efficacy. Phase 1 trials are designed to evaluate the safety and tolerability of a new treatment, typically with a small number of patients (eg, 20-80), generally spread across several dose levels. Phase 2 trials are designed to determine whether the new treatment has sufficiently promising efficacy to warrant further investigation in a large-scale randomized phase 3 trial, as well as to further assess safety. These studies usually involve a few hundred patients. This article provides an overview of some of the most commonly used phase 2 designs for clinical trials and emphasizes their critical elements and considerations. Key references to some of the most commonly used phase 2 designs are given to allow the reader to explore in more detail the critical aspects when planning a phase 2 trial. A comparison of 3 potential designs in the context of the NRG-HN002 trial is presented to complement the discussion about phase 2 trials.

Of some innovations in clinical trial design in hematology and oncology

The design of clinical trials, formalized in the immediate post-war period, has undergone major changes due to therapeutic innovations, particularly the arrival of targeted therapies in onco-hematology. The traditional phase I-II-III regimen is regularly questioned and multiple adaptations are proposed. This article proposes to expose some of these modifications and the issues they lead to.

Optimal, minimax and admissible two-stage design for phase II oncology clinical trials

Background

The article aims to compare the efficiency of minimax, optimal and admissible criteria in Simon’s and Fleming’s two-stage design.

Methods

Three parameter settings (p₁-p₀ = 0.25–0.05, 0.30–0.10, 0.50–0.30) are designed to compare the maximum sample size, the critical values and the expected sample size for minimax, optimal and admissible designs. Type I & II error constraints (α, β) vary across (0.10, 0.10), (0.05, 0.20) and (0.05, 0.10), respectively.

Results

In both Simon’s and Fleming’s two-stage designs, the maximum sample size of admissible design is smaller than optimal design but larger than minimax design. Meanwhile, the expected samples size of admissible design is smaller than minimax design but larger than optimal design. Mostly, the maximum sample size and expected sample size in Fleming’s designs are considerably smaller than that of Simon’s designs.

Conclusions

Whenever (p₀, p₁) is pre-specified, it is better to explore in the range of probability q, based on relative importance between maximum sample size and expected sample size, and determine which design to choose. When q is unknown, optimal design may be more favorable for drugs with limited efficacy. Contrarily, minimax design is recommended if treatment demonstrates impressive efficacy.

An overview of precision oncology basket and umbrella trials for clinicians

With advancements in biomarkers and momentum in precision medicine, biomarker-guided trials such as basket trials and umbrella trials have been developed under the master protocol framework. A master protocol refers to a single, overarching design developed to evaluate multiple hypotheses with the general goal of improving the efficiency of trial evaluation. One type of master protocol is the basket trial, in which a targeted therapy is evaluated for multiple diseases that share common molecular alterations or risk factors that may help predict whether the patients will respond to the given therapy. Another variant of a master protocol is the umbrella trial, in which multiple targeted therapies are evaluated for a single disease that is stratified into multiple subgroups based on different molecular or other predictive risk factors. Both designs follow the core principle of precision medicine-to tailor intervention strategies based on the patient's risk factor(s) that can help predict whether they will respond to a specific treatment. There have been increasing numbers of basket and umbrella trials, but they are still poorly understood. This article reviews common characteristics of basket and umbrella trials, key trials and recent US Food and Drug Administration approvals for precision oncology, and important considerations for clinical readers when critically evaluating future publications on basket trials and umbrella trials and for researchers when designing these clinical trials.

Leveraging historical data into oncology development programs: Two case studies of phase 2 Bayesian augmented control trial designs

Leveraging historical data into the design and analysis of phase 2 randomized controlled trials can improve efficiency of drug development programs. Such approaches can reduce sample size without loss of power. Potential issues arise when the current control arm is inconsistent with historical data, which may lead to biased estimates of treatment efficacy, loss of power, or inflated type 1 error. Consideration as to how to borrow historical information is important, and in particular, adjustment for prognostic factors should be considered. This paper will illustrate two motivating case studies of oncology Bayesian augmented control (BAC) trials. In the first example, a glioblastoma study, an informative prior was used for the control arm hazard rate. Sample size savings were 15% to 20% by using a BAC design. In the second example, a pancreatic cancer study, a hierarchical model borrowing method was used, which enabled the extent of borrowing to be determined by consistency of observed study data with historical studies. Supporting Bayesian analyses also adjusted for prognostic factors. Incorporating historical data via Bayesian trial design can provide sample size savings, reduce study duration, and enable a more scientific approach to development of novel therapies by avoiding excess recruitment to a control arm. Various sensitivity analyses are necessary to interpret results. Current industry efforts for data transparency have meaningful implications for access to patient-level historical data, which, while not critical, is helpful to adjust for potential imbalances in prognostic factors.

A Review of Perspectives on the Use of Randomization in Phase II Oncology Trials

Historically, phase II oncology trials assessed a treatment's efficacy by examining its tumor response rate in a single-arm trial. Then, approximately 25 years ago, certain statistical and pharmacological considerations ignited a debate around whether randomized designs should be used instead. Here, based on an extensive literature review, we review the arguments on either side of this debate. In particular, we describe the numerous factors that relate to the reliance of single-arm trials on historical control data and detail the trial scenarios in which there was general agreement on preferential utilization of single-arm or randomized design frameworks, such as the use of single-arm designs when investigating treatments for rare cancers. We then summarize the latest figures on phase II oncology trial design, contrasting current design choices against historical recommendations on best practice. Ultimately, we find several ways in which the design of recently completed phase II trials does not appear to align with said recommendations. For example, despite advice to the contrary, only 66.2% of the assessed trials that employed progression-free survival as a primary or coprimary outcome used a randomized comparative design. In addition, we identify that just 28.2% of the considered randomized comparative trials came to a positive conclusion as opposed to 72.7% of the single-arm trials. We conclude by describing a selection of important issues influencing contemporary design, framing this discourse in light of current trends in phase II, such as the increased use of biomarkers and recent interest in novel adaptive designs.

Effects of biomarker diagnostic accuracy on biomarker-guided phase 2 trials

Recent advancements in genomics have attracted attention towards biomarker-guided trials. These trials aim to identify therapies that target diseases based on their genetic profile, and are especially common in cancer research. Careful incorporation of biomarkers in phase II studies is critical to the selection of candidates for further phase III investigation. This short communication focuses on problems of biomarker test accuracy in biomarker-guided trials. We assessed how diagnostic accuracy of biomarker tests affects type I error rate, statistical power, and sample size requirements of single-arm biomarker-guided trials. In particular, we report how false positive rates (FPRs) of biomarker tests reduce statistical power and type I error for Simon's two-stage design, and the degree of sample size correction required to achieve pre-specified power and type I error with varying FPRs. This was done using a case study based on a previous biomarker-guided single-arm trial that was designed with an assumed tumor response rate of 10% under the null hypothesis and 40% for the alternative hypothesis for the mutant group for 5% type I error and 90% power. With varying FPRs of biomarker tests, we considered two scenarios in which the response rate for the wild-type group was assumed to be lower than the response rate for the mutant group at 5% and 10%. We also developed a simple open-source online trial planner for future investigators to use for their biomarker-guided phase II trials (https://mtek.shinyapps.io/Biomarker_Trial_Planner/).

Evaluation of the quality of the reporting of phase II clinical trials in oncology: A systematic review

OBJECTIVE

To describe the current state of knowledge concerning the quality of reporting in phase II clinical trials in oncology and to describe the various methods published allowing this quality evaluation.

METHODS

databases including MEDLINE and COCHRANE were searched. Reviews and meta-analyses analyzing the quality of the reporting of phase II trials in oncology were included. Descriptive analysis of the results was performed.

RESULTS

Thirteen publications were retained. Only 2 publications adopted a systematic approach of evaluation of the quality of reporting by overall scores. The Key Methodological Score (KMS), proposed by Grellety et al., gathering 3 items, seemed adapted for such an evaluation. A score of 3/3 was found in 16.1% of the 156 phase II trials analysed by this score. The other reviews used a qualitative analysis to evaluate the reporting, via an analysis of a single criterion, generally the statistical plan of the study. This item was considered as having been correctly reported in less than 50% of the analysed articles.

CONCLUSION

The quality of reporting in phase II trials in oncology is a field that has been investigated very little (13 publications). When it is studied, the estimated level of quality is not satisfactory, whatever the method employed. The use of an overall score of evaluation is a path which should be pursued, in order to get reliable results. It also seems necessary to propose strong recommendations, which would create a consensus for the methodology and the reporting of these studies.

Quality of reporting in oncology phase II trials: A 5-year assessment through systematic review

BACKGROUND

Phase II clinical trials are a cornerstone of the development in experimental treatments They work as a "filter" for phase III trials confirmation. Surprisingly the attrition ratio in Phase III trials in oncology is significantly higher than in any other medical specialty. This suggests phase II trials in oncology fail to achieve their goal. Objective The present study aims at estimating the quality of reporting in published oncology phase II clinical trials.

DATA SOURCES

A literature review was conducted among all phase II and phase II/III clinical trials published during a 5-year period (2010-2015).

STUDY ELIGIBILITY CRITERIA

All articles electronically published by three randomly-selected oncology journals with Impact-Factors>4 were included: Journal of Clinical Oncology, Annals of Oncology and British Journal of Cancer.

INTERVENTION

Quality of reporting was assessed using the Key Methodological Score.

RESULTS

557 articles were included. 315 trials were single-arm studies (56.6%), 193 (34.6%) were randomized and 49 (8.8%) were non-randomized multiple-arm studies. The Methodological Score was equal to 0 (lowest level), 1, 2, 3 (highest level) respectively for 22 (3.9%), 119 (21.4%), 270 (48.5%) and 146 (26.2%) articles. The primary end point is almost systematically reported (90.5%), while sample size calculation is missing in 66% of the articles. 3 variables were independently associated with reporting of a high standard: presence of statistical design (p-value <0.001), multicenter trial (p-value = 0.012), per-protocol analysis (p-value <0.001).

LIMITATIONS

Screening was mainly performed by a sole author. The Key Methodological Score was based on only 3 items, making grey zones difficult to translate.

CONCLUSIONS & IMPLICATIONS OF KEY FINDINGS

This literature review highlights the existence of gaps concerning the quality of reporting. It therefore raised the question of the suitability of the methodology as well as the quality of these trials, reporting being incomplete in the corresponding articles.

Phase 2 Neoadjuvant Treatment Intensification Trials in Rectal Cancer: A Systematic Review

PURPOSE

Multiple phase 2 trials of neoadjuvant treatment intensification in locally advanced rectal cancer have reported promising efficacy signals, but these have not translated into improved cancer outcomes in phase 3 trials. Improvements in phase 2 trial design are needed to reduce these false-positive signals. This systematic review evaluated the design of phase 2 trials of neoadjuvant long-course radiation or chemoradiation therapy treatment intensification in locally advanced rectal cancer.

METHODS AND MATERIALS

The PubMed, EMBASE, MEDLINE, and Cochrane Library databases were searched for published phase 2 trials of neoadjuvant treatment intensification from 2004 to 2016. Trial clinical design and outcomes were assessed, with statistical design and compliance rated using a previously published system. Multivariable meta-regression analysis of pathologic complete response (pCR) was conducted.

RESULTS

We identified 92 eligible trials. Patients with American Joint Committee on Cancer stage II and III equivalent disease were eligible in 87 trials (94.6%). In 43 trials (46.7%), local staging on magnetic resonance imaging was mandated. Only 12 trials (13.0%) were randomized, with 8 having a standard-treatment control arm. Just 51 trials (55.4%) described their statistical design, with 21 trials (22.8%) failing to report their sample size derivation. Most trials (n=84, 91.3%) defined a primary endpoint, but 15 different primary endpoints were used. All trials reported pCR rates. Only 38 trials (41.3%) adequately reported trial statistical design and compliance. Meta-analysis revealed a pooled pCR rate of 17.5% (95% confidence interval, 15.7%-19.4%) across treatment arms of neoadjuvant long-course radiation or chemoradiation therapy treatment intensification and substantial heterogeneity among the reported effect sizes (I² = 55.3%, P<.001). Multivariable meta-regression analysis suggested increased pCR rates with higher radiation therapy doses (adjusted P=.025).

CONCLUSIONS

Improvement in the design of future phase 2 rectal cancer trials is urgently required. A significant increase in randomized trials is essential to overcome selection bias and determine novel schedules suitable for phase 3 testing. This systematic review provides key recommendations to guide future treatment intensification trial design in rectal cancer.

Extended two-stage adaptive designs with three target responses for phase II clinical trials

We develop a nature-inspired stochastic population-based algorithm and call it discrete particle swarm optimization to find extended two-stage adaptive optimal designs that allow three target response rates for the drug in a phase II trial. Our proposed designs include the celebrated Simon's two-stage design and its extension that allows two target response rates to be specified for the drug. We show that discrete particle swarm optimization not only frequently outperforms greedy algorithms, which are currently used to find such designs when there are only a few parameters; it is also capable of solving design problems posed here with more parameters that greedy algorithms cannot solve. In stage 1 of our proposed designs, futility is quickly assessed and if there are sufficient responders to move to stage 2, one tests one of the three target response rates of the drug, subject to various user-specified testing error rates. Our designs are therefore more flexible and interestingly, do not necessarily require larger expected sample size requirements than two-stage adaptive designs. Using a real adaptive trial for melanoma patients, we show our proposed design requires one half fewer subjects than the implemented design in the study.

Recent developments in clinical trial designs for HIV vaccine research

HIV vaccine strategies are expected to be a crucial component for controlling the HIV epidemic. Despite the large spectrum of potential candidate vaccines for both prophylactic and therapeutic use, the overall development process of an efficacious HIV vaccine strategy is lengthy. The design of clinical trials and the progression of a candidate strategy through the different clinical development stages remain methodologically challenging, mainly due to the lack of validated correlates of protection. In this review, we describe recent advances in clinical trial designs to increase the efficiency of the clinical development of candidate HIV vaccine strategies. The methodological aspects of the designs for early- (phase I and II) and later -stage (phase IIB and III) development are discussed, taking into account the specificities of both prophylactic and therapeutic HIV vaccine development.

Statistical challenges in assessing potential efficacy of complex interventions in pilot or feasibility studies

Early phase trials of complex interventions currently focus on assessing the feasibility of a large randomised control trial and on conducting pilot work. Assessing the efficacy of the proposed intervention is generally discouraged, due to concerns of underpowered hypothesis testing. In contrast, early assessment of efficacy is common for drug therapies, where phase II trials are often used as a screening mechanism to identify promising treatments. In this paper, we outline the challenges encountered in extending ideas developed in the phase II drug trial literature to the complex intervention setting. The prevalence of multiple endpoints and clustering of outcome data are identified as important considerations, having implications for timely and robust determination of optimal trial design parameters. The potential for Bayesian methods to help to identify robust trial designs and optimal decision rules is also explored.

High-dose intravenous vitamin C combined with cytotoxic chemotherapy in patients with advanced cancer: a phase I-II clinical trial

Background

Biological and some clinical evidence suggest that high-dose intravenous vitamin C (IVC) could increase the effectiveness of cancer chemotherapy. IVC is widely used by integrative and complementary cancer therapists, but rigorous data are lacking as to its safety and which cancers and chemotherapy regimens would be the most promising to investigate in detail.

Methods and Findings

We carried out a phase I-II safety, tolerability, pharmacokinetic and efficacy trial of IVC combined with chemotherapy in patients whose treating oncologist judged that standard-of-care or off-label chemotherapy offered less than a 33% likelihood of a meaningful response. We documented adverse events and toxicity associated with IVC infusions, determined pre- and post-chemotherapy vitamin C and oxalic acid pharmacokinetic profiles, and monitored objective clinical responses, mood and quality of life. Fourteen patients were enrolled. IVC was safe and generally well tolerated, although some patients experienced transient adverse events during or after IVC infusions. The pre- and post-chemotherapy pharmacokinetic profiles suggested that tissue uptake of vitamin C increases after chemotherapy, with no increase in urinary oxalic acid excretion. Three patients with different types of cancer experienced unexpected transient stable disease, increased energy and functional improvement.

Conclusions

Despite IVC’s biological and clinical plausibility, career cancer investigators currently ignore it while integrative cancer therapists use it widely but without reporting the kind of clinical data that is normally gathered in cancer drug development. The present study neither proves nor disproves IVC’s value in cancer therapy, but it provides practical information, and indicates a feasible way to evaluate this plausible but unproven therapy in an academic environment that is currently uninterested in it. If carried out in sufficient numbers, simple studies like this one could identify specific clusters of cancer type, chemotherapy regimen and IVC in which exceptional responses occur frequently enough to justify appropriately focused clinical trials.

Trial Registration

ClinicalTrials.gov NCT01050621

Integrating dynamic mixed-effect modelling and penalized regression to explore genetic association with pharmacokinetics

CONTEXT

In a previous work, we have shown that penalized regression approaches can allow many genetic variants to be incorporated into sophisticated pharmacokinetic (PK) models in a way that is both computationally and statistically efficient. The phenotypes were the individual model parameter estimates, obtained a posteriori of the model fit and known to be sensitive to the study design.

OBJECTIVE

The aim of this study was to propose an integrated approach in which genetic effect sizes are estimated simultaneously with the PK model parameters, which should improve the estimate precision and reduce sensitivity to study design.

METHODS

A total of 200 data sets were simulated under the null and each of the following three alternative scenarios: (i) a phase II study with N=300 participants and n=6 sampling times, wherein six unobserved causal variants affect the drug elimination clearance; (ii) the addition of participants with a residual concentration collected in clinical routine (N=300, n=6 plus N=700, n=1); and (iii) a phase II study (N=300, n=6) in which four unobserved causal variants affect two different model parameters.

RESULTS

In all scenarios the integrated approach detected fewer false positives. In scenario (i), true-positive rates were low and the stepwise procedure outperformed the integrated approach. In scenario (ii), approaches performed similarly and rates were higher. In scenario (iii), the integrated approach outperformed the stepwise procedure.

CONCLUSION

A PK phase II study with N=300 lacks the power to detect genetic effects on PK using genetic arrays. Our approach can simultaneously analyse phase II and clinical routine data and identify when genetic variants affect multiple PK parameters.

Global diffusion tensor imaging derived metrics differentiate glioblastoma multiforme vs. normal brains by using discriminant analysis: introduction of a novel whole-brain approach

Background

Histological behavior of glioblastoma multiforme suggests it would benefit more from a global rather than regional evaluation. A global (whole-brain) calculation of diffusion tensor imaging (DTI) derived tensor metrics offers a valid method to detect the integrity of white matter structures without missing infiltrated brain areas not seen in conventional sequences. In this study we calculated a predictive model of brain infiltration in patients with glioblastoma using global tensor metrics.

Methods

Retrospective, case and control study; 11 global DTI-derived tensor metrics were calculated in 27 patients with glioblastoma multiforme and 34 controls: mean diffusivity, fractional anisotropy, pure isotropic diffusion, pure anisotropic diffusion, the total magnitude of the diffusion tensor, linear tensor, planar tensor, spherical tensor, relative anisotropy, axial diffusivity and radial diffusivity. The multivariate discriminant analysis of these variables (including age) with a diagnostic test evaluation was performed.

Results

The simultaneous analysis of 732 measures from 12 continuous variables in 61 subjects revealed one discriminant model that significantly differentiated normal brains and brains with glioblastoma: Wilks’ λ = 0.324, χ² (3) = 38.907, p < .001. The overall predictive accuracy was 92.7%.

Conclusions

We present a phase II study introducing a novel global approach using DTI-derived biomarkers of brain impairment. The final predictive model selected only three metrics: axial diffusivity, spherical tensor and linear tensor. These metrics might be clinically applied for diagnosis, follow-up, and the study of other neurological diseases.

Accelerating clinical development of HIV vaccine strategies: methodological challenges and considerations in constructing an optimised multi-arm phase I/II trial design

Background

Many candidate vaccine strategies against human immunodeficiency virus (HIV) infection are under study, but their clinical development is lengthy and iterative. To accelerate HIV vaccine development optimised trial designs are needed. We propose a randomised multi-arm phase I/II design for early stage development of several vaccine strategies, aiming at rapidly discarding those that are unsafe or non-immunogenic.

Methods

We explored early stage designs to evaluate both the safety and the immunogenicity of four heterologous prime-boost HIV vaccine strategies in parallel. One of the vaccines used as a prime and boost in the different strategies (vaccine 1) has yet to be tested in humans, thus requiring a phase I safety evaluation. However, its toxicity risk is considered minimal based on data from similar vaccines. We newly adapted a randomised phase II trial by integrating an early safety decision rule, emulating that of a phase I study. We evaluated the operating characteristics of the proposed design in simulation studies with either a fixed-sample frequentist or a continuous Bayesian safety decision rule and projected timelines for the trial.

Results

We propose a randomised four-arm phase I/II design with two independent binary endpoints for safety and immunogenicity. Immunogenicity evaluation at trial end is based on a single-stage Fleming design per arm, comparing the observed proportion of responders in an immunogenicity screening assay to an unacceptably low proportion, without direct comparisons between arms. Randomisation limits heterogeneity in volunteer characteristics between arms. To avoid exposure of additional participants to an unsafe vaccine during the vaccine boost phase, an early safety decision rule is imposed on the arm starting with vaccine 1 injections. In simulations of the design with either decision rule, the risks of erroneous conclusions were controlled <15%. Flexibility in trial conduct is greater with the continuous Bayesian rule. A 12-month gain in timelines is expected by this optimised design. Other existing designs such as bivariate or seamless phase I/II designs did not offer a clear-cut alternative.

Conclusions

By combining phase I and phase II evaluations in a multi-arm trial, the proposed optimised design allows for accelerating early stage clinical development of HIV vaccine strategies.

Estimation of renal cell carcinoma treatment effects from disease progression modeling

To improve future drug development efficiency in renal cell carcinoma (RCC), a disease-progression model was developed with longitudinal tumor size data from a phase III trial of sorafenib in RCC. The best-fit model was externally evaluated on 145 placebo-treated patients in a phase III trial of pazopanib; the model incorporated baseline tumor size, a linear disease-progression component, and an exponential drug effect (DE) parameter. With the model-estimated effect of sorafenib on RCC growth, we calculated the power of randomized phase II trials between sorafenib and hypothetical comparators over a range of effects. A hypothetical comparator with 80% greater DE than sorafenib would have 82% power (one-sided α = 0.1) with 50 patients per arm. Model-based quantitation of treatment effect with computed tomography (CT) imaging offers a scaffold on which to develop new, more efficient, phase II trial end points and analytic strategies for RCC.