Subgroup-specific dose finding in phase I clinical trials based on time to toxicity allowing adaptive subgroup combination

A Personalized Dose-Finding Algorithm Based on Adaptive Gaussian Process Regression

Dose-finding studies play a crucial role in drug development by identifying the optimal dose(s) for later studies while considering tolerability. This not only saves time and effort in proceeding with Phase III trials but also improves efficacy. In an era of precision medicine, it is not ideal to assume patient homogeneity in dose-finding studies as patients may respond differently to the drug. To address this, we propose a personalized dose-finding algorithm that assigns patients to individualized optimal biological doses. Our design follows a two-stage approach. Initially, patients are enrolled under broad eligibility criteria. Based on the Stage 1 data, we fit a regression model of toxicity and efficacy outcomes on dose and biomarkers to characterize treatment-sensitive patients. In the second stage, we restrict the trial population to sensitive patients, apply a personalized dose allocation algorithm, and choose the recommended dose at the end of the trial. Simulation study shows that the proposed design reliably enriches the trial population, minimizes the number of failures, and yields superior operating characteristics compared to several existing dose-finding designs in terms of both the percentage of correct selection and the number of patients treated at target dose(s).

Adaptive phase I-II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies

Targeted agents and immunotherapies have revolutionized cancer treatment, offering promising options for various cancer types. Unlike traditional therapies the principle of "more is better" is not always applicable to these new therapies due to their unique biomedical mechanisms. As a result, various phase I-II clinical trial designs have been proposed to identify the optimal biological dose that maximizes the therapeutic effect of targeted therapies and immunotherapies by jointly monitoring both efficacy and toxicity outcomes. This review article examines several innovative phase I-II clinical trial designs that utilize accumulated efficacy and toxicity outcomes to adaptively determine doses for subsequent patients and identify the optimal biological dose, maximizing the overall therapeutic effect. Specifically, we highlight three categories of phase I-II designs: efficacy-driven, utility-based, and designs incorporating multiple efficacy endpoints. For each design, we review the dose-outcome model, the definition of the optimal biological dose, the dose-finding algorithm, and the software for trial implementation. To illustrate the concepts, we also present two real phase I-II trial examples utilizing the EffTox and ISO designs. Finally, we provide a classification tree to summarize the designs discussed in this article.

Adaptive Bayesian information borrowing methods for finding and optimizing subgroup-specific doses

In precision oncology, integrating multiple cancer patient subgroups into a single master protocol allows for the simultaneous assessment of treatment effects in these subgroups and promotes the sharing of information between them, ultimately reducing sample sizes and costs and enhancing scientific validity. However, the safety and efficacy of these therapies may vary across different subgroups, resulting in heterogeneous outcomes. Therefore, identifying subgroup-specific optimal doses in early-phase clinical trials is crucial for the development of future trials. In this article, we review various innovative Bayesian information-borrowing strategies that aim to determine and optimize subgroup-specific doses. Specifically, we discuss Bayesian hierarchical modeling, Bayesian clustering, Bayesian model averaging or selection, pairwise borrowing, and other relevant approaches. By employing these Bayesian information-borrowing methods, investigators can gain a better understanding of the intricate relationships between dose, toxicity, and efficacy in each subgroup. This increased understanding significantly improves the chances of identifying an optimal dose tailored to each specific subgroup. Furthermore, we present several practical recommendations to guide the design of future early-phase oncology trials involving multiple subgroups when using the Bayesian information-borrowing methods.

Time-to-event calibration-free odds design: A robust efficient design for phase I trials with late-onset outcomes

Compared with most of the existing phase I designs, the recently proposed calibration-free odds (CFO) design has been demonstrated to be robust, model-free, and easy to use in practice. However, the original CFO design cannot handle late-onset toxicities, which have been commonly encountered in phase I oncology dose-finding trials with targeted agents or immunotherapies. To account for late-onset outcomes, we extend the CFO design to its time-to-event (TITE) version, which inherits the calibration-free and model-free properties. One salient feature of CFO-type designs is to adopt game theory by competing three doses at a time, including the current dose and the two neighboring doses, while interval-based designs only use the data at the current dose and is thus less efficient. We conduct comprehensive numerical studies for the TITE-CFO design under both fixed and randomly generated scenarios. TITE-CFO shows robust and efficient performances compared with interval-based and model-based counterparts. As a conclusion, the TITE-CFO design provides robust, efficient, and easy-to-use alternatives for phase I trials when the toxicity outcome is late-onset.

Bayesian treatment screening and selection using subgroup-specific utilities of response and toxicity

A Bayesian design is proposed for randomized phase II clinical trials that screen multiple experimental treatments compared to an active control based on ordinal categorical toxicity and response. The underlying model and design account for patient heterogeneity characterized by ordered prognostic subgroups. All decision criteria are subgroup specific, including interim rules for dropping unsafe or ineffective treatments, and criteria for selecting optimal treatments at the end of the trial. The design requires an elicited utility function of the two outcomes that varies with the subgroups. Final treatment selections are based on posterior mean utilities. The methodology is illustrated by a trial of targeted agents for metastatic renal cancer, which motivated the design methodology. In the context of this application, the design is evaluated by computer simulation, including comparison to three designs that conduct separate trials within subgroups, or conduct one trial while ignoring subgroups, or base treatment selection on estimated response rates while ignoring toxicity.

A Road Map for Designing Phase I Clinical Trials of Radiotherapy-Novel Agent Combinations

Radiotherapy has proven efficacy in a wide range of cancers. There is growing interest in evaluating radiotherapy-novel agent combinations and a drive to initiate this earlier in the clinical development of the novel agent, where the scientific rationale and preclinical evidence for a radiotherapy combination approach are high. Optimal design, delivery, and interpretation of studies are essential. In particular, the design of phase I studies to determine safety and dosing is critical to an efficient development strategy. There is significant interest in early-phase research among scientific and clinical communities over recent years, at a time when the scrutiny of the trial methodology has significantly increased. To enhance trial design, optimize safety, and promote efficient trial conduct, this position paper reviews the current phase I trial design landscape. Key design characteristics extracted from 37 methodology papers were used to define a road map and a design selection process for phase I radiotherapy-novel agent trials. Design selection is based on single- or dual-therapy dose escalation, dose-limiting toxicity categorization, maximum tolerated dose determination, subgroup evaluation, software availability, and design performance. Fifteen of the 37 designs were identified as being immediately accessible and relevant to radiotherapy-novel agent phase I trials. Applied examples of using the road map are presented. Developing these studies is intensive, highlighting the need for funding and statistical input early in the trial development to ensure appropriate design and implementation from the outset. The application of this road map will improve the design of phase I radiotherapy-novel agent combination trials, enabling a more efficient development pathway.

2 stage subgroup-specific time-to-event (2S-Sub-TITE): An adaptive two-stage time-to-toxicity design for subgroup-specific dose finding in phase I oncology trials

For phase I trials, the subgroup-specific time-to-event (Sub-TITE) design identifies the maximum tolerated dose (MTD) separately in 2+ heterogeneous patient subgroups. Sub-TITE allows borrowing strength and dynamic clustering across subgroups from the trial's start, but delaying the initiation of borrowing and clustering may improve trial accuracy. We propose the 2-stage Sub-TITE (2S-Sub-TITE) design in which the trial starts by estimating separate models per subgroup, and then initiates the Sub-TITE design at some pre-specified point of patient accrual. We evaluate the operating characteristics of the 2S-Sub-TITE design using simulations. Nine configurations of the 2S-Sub-TITE design (varying in timing of initiation of borrowing/clustering and prior probability of subgroup heterogeneity, p_hetero) and three control methods were compared across 1000 randomly-generated true toxicity probability scenarios. Effects of priors, sample size, escalation rules, target toxicity probability, accrual rate, and number of subgroups were evaluated. Metrics included: proportion of correct selection (PCS) of the true MTD, and average number of toxicities incurred. Among the 5 2S-Sub-TITE configurations (out of 9 total) with the highest PCS (45%) when the subgroup heterogeneity assumption is correct (all of which out-perform the control methods by 2%-6%), the configuration which enables borrowing and clustering allowance with p_hetero = 0.7 starting at 75% patient accrual best minimizes toxicities as well as losses in accuracy if the heterogeneity assumption is incorrect. For trials with high confidence in subgroup heterogeneity, the 2S-Sub-TITE configuration enabling borrowing/clustering with p_hetero = 0.7 starting at 75% patient accrual exhibits superior dose-finding accuracy compared to existing methods.

Subgroup‐specific dose finding for phase I‐II trials using Bayesian clustering

In most models and algorithms for dose‐finding clinical trials, it is assumed that the trial participants are homogeneous—the optimal dose is the same for all those who qualify for the trial. However, if there are heterogeneous populations who may benefit from the same treatment, it is inefficient to conduct dose‐finding separately for each group, and assuming homogeneity across all subpopulations may lead to identification of the incorrect dose for some (or all) subgroups. To accommodate heterogeneity in dose‐finding trials when both efficacy and toxicity outcomes must be used to identify the optimal dose (as in immunotherapeutic oncology treatments), we utilize an adaptive Bayesian clustering method which borrows strength among similar subgroups and clusters truly homogeneous subgroups. Unlike methodology already described in the literature, our proposed methodology does not require the assumption of exchangeability between subgroups or a priori ordering of subgroups, but does allow for specification of different subgroup‐specific priors if prior information is available. We provide a comparison of operating characteristics between our method and Bayesian hierarchical models for subgroups in a variety of relevant scenarios. After simulation studies with four a priori subgroups, we observed that our method and the hierarchical models both outperform separate subgroup‐specific models when all subgroups have the same dose‐efficacy and dose‐toxicity curves. However, our method outperforms hierarchical models when one subgroup has a different dose‐efficacy or dose‐toxicity curve from the other three subgroups.

BIPSE: A biomarker-based phase I/II design for immunotherapy trials with progression-free survival endpoint

A Bayesian biomarker-based phase I/II design (BIPSE) is presented for immunotherapy trials with a progression-free survival (PFS) endpoint. The objective is to identify the subgroup-specific optimal dose, defined as the dose with the best risk-benefit tradeoff in each biomarker subgroup. We jointly model the immune response, toxicity outcome, and PFS with information borrowing across subgroups. A plateau model is used to describe the marginal distribution of the immune response. Conditional on the immune response, we model toxicity using probit regression and model PFS using the mixture cure rate model. During the trial, based on the accumulating data, we continuously update model estimates and adaptively randomize patients to doses with high desirability within each subgroup. Simulation studies show that the BIPSE design has desirable operating characteristics in selecting the subgroup-specific optimal doses and allocating patients to those optimal doses, and outperforms conventional designs.

A Phase I-II Basket Trial Design to Optimize Dose-Schedule Regimes Based on Delayed Outcomes

This paper proposes a Bayesian adaptive basket trial design to optimize the dose-schedule regimes of an experimental agent within disease subtypes, called "baskets", for phase I-II clinical trials based on late-onset efficacy and toxicity. To characterize the association among the baskets and regimes, a Bayesian hierarchical model is assumed that includes a heterogeneity parameter, adaptively updated during the trial, that quantifies information shared across baskets. To account for late-onset outcomes when doing sequential decision making, unobserved outcomes are treated as missing values and imputed by exploiting early biomarker and low-grade toxicity information. Elicited joint utilities of efficacy and toxicity are used for decision making. Patients are randomized adaptively to regimes while accounting for baskets, with randomization probabilities proportional to the posterior probability of achieving maximum utility. Simulations are presented to assess the design's robustness and ability to identify optimal dose-schedule regimes within disease subtypes, and to compare it to a simplified design that treats the subtypes independently.

Adaptive Enrichment Designs in Clinical Trials

Adaptive enrichment designs for clinical trials may include rules that use interim data to identify treatment-sensitive patient subgroups, select or compare treatments, or change entry criteria. A common setting is a trial to compare a new biologically targeted agent to standard therapy. An enrichment design's structure depends on its goals, how it accounts for patient heterogeneity and treatment effects, and practical constraints. This article first covers basic concepts, including treatment-biomarker interaction, precision medicine, selection bias, and sequentially adaptive decision making, and briefly describes some different types of enrichment. Numerical illustrations are provided for qualitatively different cases involving treatment-biomarker interactions. Reviews are given of adaptive signature designs; a Bayesian design that uses a random partition to identify treatment-sensitive biomarker subgroups and assign treatments; and designs that enrich superior treatment sample sizes overall or within subgroups, make subgroup-specific decisions, or include outcome-adaptive randomization.

Comparison of Phase I-II designs with parametric or semi-parametric models using two different risk-benefit trade-off criteria

A semi-parametric stochastic ordering model (SPSO) is introduced to characterize functional relationships between dose level and the probabilities of binary Efficacy and Toxicity events. This model is used to implement a Bayesian adaptive phase I-II clinical trial using one of two different optimality criteria, either dose desirability defined as a function of the marginal Efficacy and Toxicity probabilities, or mean utility based on numerical scores of the four possible (Efficacy, Toxicity) events. A simulation study is conducted to compare designs using the SPSO model to the parametric EffTox model described in Thall and Cook, with each (model, optimality criterion) combination. Each of these four designs adaptively assigns patient cohorts to estimated optimal dose levels after restricting assignments to dose levels that are acceptably efficacious and safe. The simulation study shows that different design configurations may have superior performance depending on the assumed true dose-outcome scenario.