Adaptive Enrichment Designs in Clinical Trials

Use of Seamless Study Designs in Oncology Clinical Development- A Survey Conducted by IDSWG Oncology Sub-team

Seamless study designs have the potential to accelerate clinical development. The use of innovative seamless designs has been increasing in the oncology area; however, while the concept of seamless designs becomes more popular and accepted, many challenges remain in both the design and conduct of these trials. This may be especially true when seamless designs are used in late phase development supporting regulatory decision-making. The Innovative Design Scientific Working Group (IDSWG) Oncology team conducted a survey to understand the current use of seamless study designs for registration purposes in oncology clinical development. The survey was designed to provide insights into the benefits and to identify the roadblocks. A total of 16 questions were included in the survey that was distributed using the ASA Biopharmaceutical Section and IDSWG email listings from August to September 2022. A total of 51 responses were received, with 39 (76%) respondents indicating that their organizations had seamless oncology studies in planning or implementation for registration purposes. Detailed survey results are presented in the manuscript. Overall, while seamless designs offer advantages in terms of timeline reduction and cost saving, they also present challenges related to additional complexity and the need for efficient surrogate clinical endpoints in oncology drug development.

Bayesian Sequential Design for Identifying and Ranking Effective Patient Subgroups in Precision Medicine in the Case of Counting Outcome Data with Inflated Zeros

Precision medicine is revolutionizing health care, particularly by addressing patient variability due to different biological profiles. As traditional treatments may not always be appropriate for certain patient subsets, the rise of biomarker-stratified clinical trials has driven the need for innovative methods. We introduced a Bayesian sequential scheme to evaluate therapeutic interventions in an intensive care unit setting, focusing on complex endpoints characterized by an excess of zeros and right truncation. By using a zero-inflated truncated Poisson model, we efficiently addressed this data complexity. The posterior distribution of rankings and the surface under the cumulative ranking curve (SUCRA) approach provided a comprehensive ranking of the subgroups studied. Different subsets of subgroups were evaluated depending on the availability of biomarker data. Interim analyses, accounting for early stopping for efficacy, were an integral aspect of our design. The simulation study demonstrated a high proportion of correct identification of the subgroup which is the most predictive of the treatment effect, as well as satisfactory false positive and true positive rates. As the role of personalized medicine grows, especially in the intensive care setting, it is critical to have designs that can manage complicated endpoints and that can control for decision error. Our method seems promising in this challenging context.

Interpreting Randomized Controlled Trials

This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial's sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as executed in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial's convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.

How therapeutic advances have transformed the medical landscape: a primer for clinicians

Novel medicines are entering the market rapidly and are increasingly being used alone or in combination to treat illnesses of every sort. While transforming the lives of many patients, these new therapies have also forced us to reconsider the way we evaluate, use and fund medicines. This article offers a primer to help practitioners understand how the therapeutic landscape is changing and how this might impact the evidence generation, access to interventions, patient experience and quality of care.

Point estimation for adaptive trial designs II: Practical considerations and guidance

In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This article is the second in a two-part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realization they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy prespecified in the statistical analysis plan. When available, unbiased or bias-reduced estimates are to be preferred.

Considerations for identifying the "right" subgroup in adaptive enrichment trials

Adaptive Enrichment Trials aim to make efficient use of data in a pivotal trial of a new targeted therapy to both (a) more precisely identify who benefits from that therapy and (b) improve the likelihood of successfully concluding that the drug is effective, while controlling the probability of false positives. There are a number of frameworks for conducting such a trial and decisions that must be made regarding how to identify that target subgroup. Among those decisions, one must choose how aggressively to restrict enrollment criteria based on the accumulating evidence in the trial. In this article, we empirically evaluate the impact of aggressive versus conservative enrollment restrictions on the power of the trial to detect an effect of treatment. We identify that, in some cases, a more aggressive strategy can substantially improve power. This additionally raises an important question regarding label indication: To what degree do we need a formal test of the hypothesis of no treatment effect in the exact population implied by the label indication? We discuss this question and evaluate how our answer for adaptive enrichment trials may relate to the answer implied by current practice for broad eligibility trials.

Promising zone two-stage design for a single-arm study with binary outcome

Adaptive designs are increasingly used in clinical trials to assess the effectiveness of new drugs. For a single-arm study with a binary outcome, several adaptive designs were developed by using numerical search algorithms and the conditional power approach. The design based on numerical search algorithms is able to identify the global optimal design, but the computational intensity limits the usage of these designs. The conditional power approach searches for the optimal design without expensive computing time. In addition, promising zone strategy was proposed to move on drug development to the follow-up stages when the interim results are promising. We propose to develop two adaptive designs: One based on the conditional power approach, and the other based on the promising zone strategy. These two designs preserve types I and II error rates. It is preferable to satisfy the monotonic property for adaptive designs: The second stage sample size decreases as the first stage responses go up. We theoretically prove this important property for the two proposed designs. The proposed designs can be easily applied to real trials with limited computing resources.

Recent innovations in adaptive trial designs: A review of design opportunities in translational research

Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of "adaptive" clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.

New insights into adaptive enrichment designs

The transition towards personalized medicine is happening and the new experimental framework is raising several challenges, from a clinical, ethical, logistical, regulatory, and statistical perspective. To face these challenges, innovative study designs with increasing complexity have been proposed. In particular, adaptive enrichment designs are becoming more attractive for their flexibility. However, these procedures rely on an increasing number of parameters that are unknown at the planning stage of the clinical trial, so the study design requires particular care. This review is dedicated to adaptive enrichment studies with a focus on design aspects. While many papers deal with methods for the analysis, the sample size determination and the optimal allocation problem have been overlooked. We discuss the multiple aspects involved in adaptive enrichment designs that contribute to their advantages and disadvantages. The decision-making process of whether or not it is worth enriching should be driven by clinical and ethical considerations as well as scientific and statistical concerns.

Challenges and opportunities in biomarker-driven trials: adaptive randomization

In an era of precision medicine, as advanced technology such as molecular profiling at individual patient level has been developed and become increasingly accessible and affordable, biomarker-driven trials have been received a lot of attention and are expected to receive more attention in order to integrate clinical practice with clinical research. Biomarkers play a critical role to identify patients who are expected to get benefit from a treatment, and it is important to effectively incorporate the biomarkers into clinical trials to understand the biomarker-treatment relationship and increase the efficiency. We investigate incorporating biomarkers in adaptive randomization to identify patients who would respond better to the treatment and optimize the treatment allocation. The covariate-adjusted variants of the existing response-adaptive randomization are used to implement biomarker-driven randomization, and the performance of the biomarker-driven randomization is compared with the existing randomization methods, such as traditional fixed randomization with equal probability and response-adaptive randomization without incorporating biomarkers, under the group sequential design allowing early stopping due to superiority and futility. Various scenarios are taken into account to see the impact of the biomarker-driven randomization in the simulation study. It shows that the overall type I error rate is likely to be inflated by the effect of prognostic biomarkers. Several suggestions and considerations for the challenges are discussed to maintain the type I error rate at the nominal level.

Statistical and Operational Considerations for 2-Stage Adaptive Designs with Simultaneous Evaluation of Overall and Marker-Selected Populations in Oncology Confirmatory Trials

In biomarker enrichment study designs that start with an all-comer population, simultaneous evaluation of the entire and the marker-selected populations can be more desirable than pre-specifying the testing order, when the degree of marker predictiveness is uncertain. While there has been substantial research on this approach, our goal is to provide a complete overview and guidance in all aspects of this approach, including the interim analysis potentially using different endpoints, combination tests with associated multiplicity control, and the final treatment effect estimation. Regulatory/operational aspects and actual cases demonstrating the potential advantage of this approach are also described.