Novel Clinical Trial Designs for Intermediate AMD

We are honored to be guest editors for this special issue of Applied Stochastic Models in Business and Industry, dedicated to Kathryn Chaloner's life and achievements. Contributions from her colleagues and friends in this volume are devoted to Kathryn's early research interest in Bayesian optimal design. She was a pioneer in this field and had a broad impact on the subsequent literature on experimental design. Kathryn was internationally known for her research in Bayesian Statistics, which included design of experiments, outlier detection, prior elicitation, and clinical trials, and for her advancements in the study of HIV/AIDS. She was an accomplished researcher, teacher, and mentor. She actively encouraged women and under-represented minority students to join the field of biostatistics and provided mentorship, support, and encouragement. The special issue contains seven papers. The first three papers present some of the recent work in Bayesian optimal designs for nonlinear models, binary responses, and experiments with adversarial components. This is followed by two articles on Bayesian designs in clinical trials. The last two articles of the special issue involve engineering design problems. The paper by Giovagnoli considers the binary response models of Chaloner and Larntz (1989, Journal of Statistical Planning and Inference, 21:191–208) and develops an adaptive version of A-optimal Bayesian designs that are revised as additional data become available during the experiment. Adaptive Bayesian compound designs is also discussed as another extension. Konstantinou and Dette develop approximate Bayesian D-optimal designs for nonlinear regression models that involve covariates that cannot be observed directly. Applications to specific nonlinear models including the exponential regression model are considered and characterizations of the D-optimal saturated designs are presented. Optimal sample size selection for experiments is considered by De Santis and Gubbiotti in an adversarial setting where the decision makers have different prior opinions while having common utility functions and data. Using a Bayesian decision approach with a quadratic loss function, the authors present results for the one-parameter exponential family and investigate effect of the priors on the optimal sample size. Müller, Xu, and Thall discuss advantages and limitations of Bayesian decision theoretic approach in design of clinical trials. In doing so, the authors present a specific case study and point out the limitations such as the computational difficulties involved in solving sequential problems as well as selection of utility functions capturing different stakeholder interests. The article by Ventz, Parmigiani, and Trippa also consider limitations of the Bayesian framework in design of clinical trials and propose a strategy to alleviate these. The proposed strategy involves combining Bayesian designs with frequentist metrics such as error rates, confidence intervals, etc. that are commonly used by medical community and regulatory agencies. The authors show that this can be achieved by an inclusion of frequentist constraints into the Bayesian formulation. The constrained decision theoretic framework is illustrated via several applications and computational algorithms are presented for implementing the proposed approach. Nakamura, Seepaul, Kadane, and Reeja-Jayan discuss design of experiments in materials science. The authors consider use of a batch-sequential experiment and a regression model to determine optimal levels of input variables for synthesis of ceramic materials. It is illustrated that the optimal settings produce more reliable results than other experiments. The final article by Polson and Soyer considers Bayesian design of accelerated life tests for reliability assessment. An augmented probability simulation (APS) approach using Lindley's (1976, Annals of Statistics, 4:1–10) conjugate utility functions is proposed to compute optimal designs in an efficient manner. The proposed approach is illustrated using single and multiple point designs. Use of particle based methods for APS is also discussed as an alternative to Markov chain Monte Carlo methods.

Linked article: Petrof et al. Br J Dermatol 2022; 186:843–848.

Design of clinical trials to evaluate drugs has been moving to increasing levels of complexity. Multicenter, multinational trials have been required to determine the clinical indications for a new drug and to receive licensure across the global market. Problems in trial design, discovered after a large, expensive trial has been completed, can delay drug development and waste the limited resources of both the investigators and the industry. Compromises in trial design or size, made to expedite the development process, produce information that is of limited value and easily misinterpreted, to the detriment of patient care. Complex studies provide more information but require more clinician expertise to understand. Investigators conducting clinical trials have the obligation not only to improve the design of clinical trials but also to publish results in a way that makes both the findings and the limitations of the study apparent to the clinician. Clinical investigators, the pharmaceutical industry, and regulatory agencies have a shared responsibility for improving the process by which drugs are evaluated and explained to physicians involved in patient care. Discussions at the Forum largely centered on design of drug trials but also included trials of a diagnostic test. The group focused on the analysis of certain recent pivotal trials with the intent to discern what these trials have to teach us about design of future trials. A session chairperson introduced each topic. A nominated speaker made a presentation, and further analysis was contributed by one or more discussants and by general discussion. The Forum proceedings are presented in three parts. The first part concerns issues in clinical trials of empirical antifungal therapy to treat febrile neutropenic patients. The second part deals with issues in design of drug trials for invasive aspergillosis. The third part has three components: issues in the evaluation of diagnostic tests, use of historical controls, and merits of the current multicenter collaborative trial groups.

Results from randomized control trials (RCTs) may not be representative when individuals refuse to be randomized or are excluded for having a preference for which treatment they receive. If trial designs do not allow for participant treatment preferences, trials can suffer in accrual, adherence, retention, and external validity of results. Thus, there is interest surrounding clinical trial designs that incorporate participant treatment preferences. We propose a Partially Randomized, Patient Preference, Sequential, Multiple Assignment, Randomized Trial (PRPP-SMART) which combines a Partially Randomized, Patient Preference (PRPP) design with a Sequential, Multiple Assignment, Randomized Trial (SMART) design. This novel PRPP-SMART design is a multi-stage clinical trial design where, at each stage, participants either receive their preferred treatment, or if they do not have a preferred treatment, they are randomized. This paper focuses on the clinical trial design for PRPP-SMARTs and the development of Bayesian and frequentist weighted and replicated regression models (WRRMs) to analyze data from such trials. We propose a two-stage PRPP-SMART with binary end of stage outcomes and estimate the embedded dynamic treatment regimes (DTRs). Our WRRMs use data from both randomized and non-randomized participants for efficient estimation of the DTR effects. We compare our method to a more traditional PRPP analysis which only considers participants randomized to treatment. Our Bayesian and frequentist methods produce more efficient DTR estimates with negligible bias despite the inclusion of non-randomized participants in the analysis. The proposed PRPP-SMART design and analytic method is a promising approach to incorporate participant treatment preferences into clinical trial design.

Scientific innovation has promoted the rapid discovery and development of cancer-related biomarkers. This has fostered improved mechanistic understandings of disease biology, facilitated the development of novel targeted therapies and offers the potential to better define the natural history of a disease and to predict response to specific treatment. Clinical trials are evolving, from the use of exploratory 'correlative studies' that assist in the understanding of disease mechanisms, to the use of validated biomarkers that are integral to the design of definitive prospective studies. A rigorous process for biomarker development and validation, followed by evaluation in well-designed, prospective clinical trials that demonstrate improved patient outcomes is required for new biomarkers to change clinical practice. To date, the number of biomarkers considered clinically useful is disappointingly small because of conflicting conclusions generated from trials with practical and methodological limitations. This review will highlight several important aspects related to the design and analysis of clinical trials that incorporate a biomarker as a central component. First, we review biomarker development, how biomarkers may be used as targets and how biomarkers can influence methodology to optimize efficiency of drug development through the use of surrogate outcomes. Second, we focus on issues related to trials evaluating potential prognostic biomarkers and how the predictive properties of biomarkers may be used to determine which patients might optimally benefit from a specific intervention.

Mild cognitive impairment (MCI) is an operational definition for a cognitive decline in individuals with a greater risk of developing dementia. The amnestic subtype of MCI is of particular interest...

Existing phase II clinical trial designs focus on a single scalar endpoint, such as a binary, continuous, or survival endpoint. In some clinical trials, such as pain management studies, the efficacy endpoint of interest is measured longitudinally. We propose a Bayesian phase II design for such clinical trials. We model the longitudinal measurement process using Bayesian hierarchical model, where subject-specific trajectory shrinks toward the population trajectory to borrow information across subjects. The Bayesian penalized spline is used to model subject-specific and population trajectories without making strong parametric assumption on their shapes. We use the area under the curve of the trajectory as the summary of the treatment effect over time. The design takes a group sequential approach and takes into account both statistical significance and clinical relevance. Bayesian criteria is proposed to make interim and final decisions based on the evidence of statistical significance and clinical relevance. The proposed design is highly flexible and can accommodate trials with one or multiple longitudinal endpoints, as well as a longitudinal primary endpoint with a secondary endpoint. Simulation study shows that the proposed design is robust with desirable operating characteristics.

BackgroundMost statistical design and analysis methods for clinical trials have been developed and evaluated where at least several hundreds of patients could be recruited. These methods may not be suitable to evaluate therapies if the sample size is unavoidably small, which is usually termed by small populations. The specific sample size cut off, where the standard methods fail, needs to be investigated. In this paper, the authors present their view on new developments for design and analysis of clinical trials in small population groups, where conventional statistical methods may be inappropriate, e.g., because of lack of power or poor adherence to asymptotic approximations due to sample size restrictions.MethodFollowing the EMA/CHMP guideline on clinical trials in small populations, we consider directions for new developments in the area of statistical methodology for design and analysis of small population clinical trials. We relate the findings to the research activities of three projects, Asterix, IDeAl, and InSPiRe, which have received funding since 2013 within the FP7-HEALTH-2013-INNOVATION-1 framework of the EU. As not all aspects of the wide research area of small population clinical trials can be addressed, we focus on areas where we feel advances are needed and feasible.ResultsThe general framework of the EMA/CHMP guideline on small population clinical trials stimulates a number of research areas. These serve as the basis for the three projects, Asterix, IDeAl, and InSPiRe, which use various approaches to develop new statistical methodology for design and analysis of small population clinical trials. Small population clinical trials refer to trials with a limited number of patients. Small populations may result form rare diseases or specific subtypes of more common diseases. New statistical methodology needs to be tailored to these specific situations.ConclusionThe main results from the three projects will constitute a useful toolbox for improved design and analysis of small population clinical trials. They address various challenges presented by the EMA/CHMP guideline as well as recent discussions about extrapolation. There is a need for involvement of the patients’ perspective in the planning and conduct of small population clinical trials for a successful therapy evaluation.

OBJECTIVES/GOALS: Since 2017, we have used the Design Lab methodology to support investigators taking innovative approaches to clinical effectiveness trial design. To date we have held 12 Design Labs and this year we are creating a handbook that will support dissemination of this approach across the Clinical and Translational Science Award consortium. METHODS/STUDY POPULATION: The Clinical Trial Design Lab brings together a multi-stakeholder group to consider innovative and impactful clinical trial designs. An investigative team is selected from a competitive pool of applicants, after which expert-led consultations support the investigator team to think about evidence generation in the context of the full treatment development pathway. Teams map the stakeholders at each step of this pathway (e.g. clinicians, patients, researchers, funders, industry experts, policy experts, regulatory experts, payers) and consider innovative design solutions. These consultations prepare investigators for an event that involves all stakeholders in a structured and facilitated discussion about trial designs that generate the best evidence and increase potential for health impact. RESULTS/ANTICIPATED RESULTS: The result of our work will be a set of Design Lab principles, a handbook with templates that support stakeholder mapping and structured discussions, and educational resources to accompany the handbook. The work is supported by a literature review that characterizes the multi-component processes included in the Design Lab, situates them within the larger context of team science interventions, and lays groundwork for the development of process metrics and impact evaluation criteria to assess the Design Lab method. In this poster presentation, we will share our multi-component broadly engaged team science approach, provide a brief outline of the principles and educational resources, and include an early version of the evaluation criteria. DISCUSSION/SIGNIFICANCE: Broadly engaged team science supports innovative thinking about study design and is especially important in the development of clinical trials. We have grown the Design Lab program of work over the past seven years and are now able to characterize our team science methodology and support others to use this approach to innovate for health impact.

Chapter 11 - Clinical Trial Design in Immuno-Oncology

Better design leads to better results - Importance of virological outcome design in clinical trials for antiviral treatment of coronavirus disease 2019.

140 Background: Online cancer patient and caregiver support groups have burgeoned in the recent years; many provide critical emotional support and experiential wisdom to newly diagnosed patients. We outline below our experience engaging patients in clinical trial design working with COLONTOWN, a 5000+ member, online colorectal cancer (CRC) patient and caregiver support and educational community. Effective trial design is a complicated and lengthy process that requires input from multiple sources. Complex trial design can be particularly challenging, where opinions are mixed and a clear path forward may not be visible. Informed patient perspectives from support groups can be invaluable in such situations. Methods: Principal Investigators (PIs) for the oligometastatic CRC ERASure trial and the Janus rectal cancer trial worked with the lead author, a patient who is the Scientific Director of COLONTOWN. An interactive survey strategy was adopted and followed to solicit group feedback. COLONTOWN members (n = 70-120) were enthusiastic to participate and share the logic behind poll choices, and their personal experiences. Poll participation led to additional scientific discussion and improved community engagement around the survey questions. Poll data and highlights of the discussion were collected and passed on to study PIs in a de-identified manner. Results: Feedback from COLONTOWN helped PIs gauge patient enthusiasm for trial concepts and provide patient perspective and clarity to controversial questions in study design. This was particularly useful where data to support more than one option was available and the guidance from the NCI Task Force or cooperative group was split. The perspective from COLONTOWN was invaluable and used alongside NCI Task Force and cooperative group feedback to make decisions to move the trial concept forward. Conclusions: Support groups such as COLONTOWN are a versatile, valuable, underutilized and available resource that can be used to tap into patient perspectives during any clinical trial design process. Ideally, when utilized early, structured patient input can provide unique insights to PIs developing clinical trial concepts that could eliminate barriers and shorten the inception part of trial design. Increased awareness and knowledge of trial design support provided by such cancer communities could facilitate the development of patient-friendly trials to promote better accrual.

Patient-derived xenograft (PDX) is a well-accepted experimental cancer model mimicking original patients in histo- & molecular pathology, as well as drug response. Mouse clinical trials (MCT) using PDX becomes increasingly prevalent in oncology research, yet the theory and methodology for trial design and analysis is underdeveloped. By analyzing tumor growths of 34081 PDX mice, we showed that majority of them can be satisfactorily modeled by exponential growth kinetics, based on which we derived a new drug response readout called the AUC ratio that is superior to the tumor growth inhibition (TGI) and its variants. We outlined a bootstrap algorithm to calculate its confidence interval, and showed that a 4:4 (4 mice in both treatment groups) or up design sharply increases measurement accuracy over the lesser design. Next, we introduce three approaches for drug evaluation and biomarker discovery in MCTs. In the end-point based analysis, we used a cohort of gastric PDXs to show a positive correlation between EGFR mRNA expression and cetuximab efficacy, in agreement with clinical trial results. We then used linear mixed models (LMMs) to describe MCTs as clustered longitudinal studies that capture the growth and drug response heterogeneities across PDXs and among mice within a PDX. Further, LMMs can separate prognostic and predictive biomarker effects, quantify response difference to a drug for multiple cancers, or efficacy difference of multiple drugs to a cohort of PDXs. Thirdly, we used additive frailty models to perform survival analysis on MCTs. We defined survival endpoints of PFS and OS in PDXs, and showed that hazard ratios can be more accurately estimated. We revealed an inherent connection between frailty and growth rate for PDXs. We performed computational simulations for the last two methods to show that statistical power improves tremendously with a 3:3 or up design. This work lays the foundation for rational design and analysis of PDX mouse clinical trials. Citation Format: Sheng Guo, Binchen Mao, Henry Q. Li. Theory and methodology for the design and analysis of PDX mouse clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4534. doi:10.1158/1538-7445.AM2017-4534

Cancer medicine continues to evolve into a more molecularly based discipline with many new treatments being developed aimed at specific cancer-related pathways and genetic alterations. In order to meet the challenges of the evolving science, new clinical trial designs and approaches for treatment trials are needed to evaluate the promise of these potential therapies. The National Cancer Institute (NCI) with the national NCI Clinical Trials Network (NCTN) Groups will launch two clinical research initiatives in lung cancer in 2014 that demonstrate innovative approaches to the design and conduct of clinical trials. The first initiative is the ALCHEMIST trial (Adjuvant Lung Cancer Enrichment Marker Identification and Sequencing Trial) which is aimed at patients with lung adenocarcinoma that has been entirely removed by surgery. In this study, samples of tumor tissue from thousands of patients will be tested for two specific genetic alterations, one in the EGFR gene, and the other in the ALK gene. These alterations are not common; about 10% of the lung cancer patient population in the US will have tumors with alterations in the EGFR gene and 5% will have alterations in the ALK gene. Rather than conduct a single trial for each alteration, the screening component of the ALCHEMIST trial will screen patients for both alterations at the same time. The ALCHEMIST is connected to 2 specific NCI-supported NCTN Group clinical treatment trials that are each testing a targeted agent specific for one of the alterations in conjunction with adjuvant therapy. Patients found to have tumors containing one of the alterations will be offered enrollment onto the appropriate clinical treatment trial. Every patient in the ALCHEMIST trial, including those without an EGFR or ALK alteration, will also be studied for cancer risk characteristics and their tumor tissue will be analyzed in a research genomics initiative conducted by the NCI Center for Cancer Genomics (CCG). This effort will employ next-generation sequencing technology to capitalize on the foundation of the CCG's earlier effort, the Cancer Genome Atlas. The second initiative, the Advanced Squamous Cell Carcinoma Lung Master Protocol, is a public-private effort made possible through collaboration with stakeholders including the NCI, NCTN Groups, FDA, NIH Foundation, the Friends of Cancer patient advocacy group, and the pharmaceutical industry. By pooling resources to screen tumor samples from about a thousand patients per year with advanced squamous cell carcinoma for multiple genetic alterations, this master protocol will enable several, randomized, phase II clinical trials with promising new agents to take place in parallel within a single study management framework. Agents will be evaluated rapidly and either be further evaluated in a phase III clinical trial or replaced by other new agents for phase II evaluation, without having to develop individual trials de novo each time. Compared with the usual process of developing clinical trials, these two new design approaches will hopefully allow more patients to be enrolled in trials with agents targeted for important molecular characteristics specific for their tumors and the trials will yield clinically useful results more rapidly. Citation Format: Margaret M. Mooney, Jack Welch, Jeffrey S. Abrams. Clinical trial design and master protocols in NCI clinical treatment trials. [abstract]. In: Proceedings of the AACR-IASLC Joint Conference on Molecular Origins of Lung Cancer; 2014 Jan 6-9; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2014;20(2Suppl):Abstract nr IA08.

Biological staging of individuals with Alzheimer’s disease (AD) may improve diagnostic and prognostic workup of dementia in clinical practice and the design of clinical trials. In this study, we used the Subtype and Stage Inference (SuStaIn) algorithm to establish a robust biological staging model for AD using cerebrospinal fluid (CSF) biomarkers. Our analysis involved 426 participants from BioFINDER-2 and was validated in 222 participants from the Knight Alzheimer Disease Research Center cohort. SuStaIn identified a singular biomarker sequence and revealed that five CSF biomarkers effectively constituted a reliable staging model (ordered: Aβ42/40, pT217/T217, pT205/T205, MTBR-tau243 and non-phosphorylated mid-region tau). The CSF stages (0–5) demonstrated a correlation with increased abnormalities in other AD-related biomarkers, such as Aβ-PET and tau-PET, and aligned with longitudinal biomarker changes reflective of AD progression. Higher CSF stages at baseline were associated with an elevated hazard ratio of clinical decline. This study highlights a common molecular pathway underlying AD pathophysiology across all patients, suggesting that a single CSF collection can accurately indicate the presence of AD pathologies and characterize the stage of disease progression. The proposed staging model has implications for enhancing diagnostic and prognostic assessments in both clinical practice and the design of clinical trials.