We propose a rule-based statistical design for combination dose-finding trials with two agents. The Ci3+3 design is an extension of the i3+3 design with simple decision rules comparing the observed toxicity rates and equivalence intervals that define the maximum tolerated dose combination. Ci3+3 consists of two stages to allow fast and efficient exploration of the dose-combination space. Statistical inference is restricted to a beta-binomial model for dose evaluation, and the entire design is built upon a set of fixed rules. We show via simulation studies that the Ci3+3 design exhibits similar and comparable operating characteristics to more complex designs utilizing model-based inferences. We believe that the Ci3+3 design may provide an alternative choice to help simplify the design and conduct of combination dose-finding trials in practice.
In parametric Bayesian designs of early phase cancer clinical trials with drug combinations exploring a discrete set of partially ordered doses, several authors claimed that there is no added value in including an interaction term to model synergism between the two drugs. In this paper, we investigate these claims in the setting of continuous dose levels of the two agents. Parametric models will be used to describe the relationship between the doses of the two agents and the probability of dose limiting toxicity and efficacy. Trial design proceeds by treating cohorts of two patients simultaneo…
Phase I dose-escalation trials constitute the first step in investigating the safety of potentially promising drugs in humans. Conventional methods for phase I dose-escalation trials are based on a single treatment schedule only. More recently, however, multiple schedules are more frequently investigated in the same trial. Here, we consider sequential phase I trials, where the trial proceeds with a new schedule (e.g. daily or weekly dosing) once the dose escalation with another schedule has been completed. The aim is to utilize the information from both the completed and the ongoing dose-escal…
Purpose: The early identification of maximum tolerated dose (MTD) in phase I trial leads to faster progression to a phase II trial or an expansion cohort to confirm efficacy. Methods: We propose a novel adaptive design for identifying MTD early to accelerate dose-finding trials. The early identification of MTD is determined adaptively by dose-retainment probability using a trial data via Bayesian analysis. We applied the early identification design to an actual trial. A simulation study evaluates the performance of the early identification design. Results: In the actual study, we confirmed the…
Motivated by a clinical trial conducted by Janssen Pharmaceuticals in which a flexible dosing regimen is compared to placebo, we evaluate how switchers in the treatment arm (i.e., patients who were switched to the higher dose) would have fared had they been kept on the low dose. This in order to understand whether flexible dosing is potentially beneficial for them. Simply comparing these patients' responses with those of patients who stayed on the low dose is unsatisfactory because the latter patients are usually in a better health condition. Because the available information in the considered…
The commonplace description of phase 1 clinical trials in oncology as "primarily concerned with safety" is belied by their near universal adoption of dose-escalation practices which are inherently unsafe. In contrast with dose titration, cohort-wise dose escalation regards patients as exchangeable, an indefensible assumption in the face of widely appreciated inter-individual heterogeneity in pharmacokinetics and pharmacodynamics (PKPD). I have previously advanced this argument in terms of a precautionary coherence principle that brings the well-known coherence notion of Cheung (2005) into cont…
Broadening eligibility criteria in cancer trials has been advocated to represent the true patient population more accurately. While the advantages are clear in terms of generalizability and recruitment, novel dose-finding designs are needed to ensure patient safety. These designs should be able to recommend precise doses for subpopulations if such subpopulations with different toxicity profiles exist. While dose-finding designs accounting for patient heterogeneity have been proposed, all existing methods assume the source of heterogeneity is known and thus pre-specify the subpopulations or onl…
Recently, the strategy for dose optimization in oncology has shifted to conduct Phase 2 randomized controlled trials with multiple doses. Optimal biologic dose selection from Phase 1 trial data to determine candidate doses for Phase 2 trials has been gaining attention. This study proposes a novel adaptive cohort size determination method for optimal biologic dose-finding to accelerate trials. The cohort size expansion is determined adaptively depending on the toxicity and efficacy data of the ongoing trial. In a simulation, the proposed method shortened the trial duration and maintained accura…
We consider a Bayesian framework based on "probability of decision" for dose-finding trial designs. The proposed PoD-BIN design evaluates the posterior predictive probabilities of up-and-down decisions. In PoD-BIN, multiple grades of toxicity, categorized as the mild toxicity (MT) and dose-limiting toxicity (DLT), are modeled simultaneously, and the primary outcome of interests is time-to-toxicity for both MT and DLT. This allows the possibility of enrolling new patients when previously enrolled patients are still being followed for toxicity, thus potentially shortening trial length. The Bayes…
Phase I dose-escalation trials must be guided by a safety model in order to avoid exposing patients to unacceptably high risk of toxicities. Traditionally, these trials are based on one type of schedule. In more recent practice, however, there is often a need to consider more than one schedule, which means that in addition to the dose itself, the schedule needs to be varied in the trial. Hence, the aim is finding an acceptable dose-schedule combination. However, most established methods for dose-escalation trials are designed to escalate the dose only and ad-hoc choices must be made to adapt t…
University of Virginia（弗吉尼亚大学）；University of Pennsylvania（宾夕法尼亚大学）；University of Cambridge（剑桥大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex. Despite this, most commonly used methods in practice focus on identifying a Maximum Tolerated Dose (MTD) by learning only from toxicity events. We present a novel adaptive clinical trial methodology, called Safe Efficacy Exploration Dose Allocation (SEEDA), that aims at maximizing the cumulative efficacies while satisfying the toxicity safety constraint with high probability. We evaluate performance objectives that have opera…
Cohort-based enrollment can slow down dose-finding trials since the outcomes of the previous cohort must be fully evaluated before the next cohort can be enrolled. This results in frequent suspension of patient enrollment. The issue is exacerbated in recent immune-oncology trials where toxicity outcomes can take a long time to observe. We propose a novel phase I design, the probability-of-decision toxicity probability interval (PoD-TPI) design, to accelerate phase I trials. PoD-TPI enables dose assignment in real-time in the presence of pending toxicity outcomes. With uncertain outcomes, the d…
Dose-finding clinical trials in oncology aim to determine the maximum tolerated dose (MTD) of a new drug, generally defined by the proportion of patients with short-term dose-limiting toxicities (DLTs). Model-based approaches for such phase I oncology trials have been widely designed and are mostly restricted to the DLTs occurring during the first cycle of treatment, although patients continue to receive treatment for multiple cycles. We aim to estimate the probability of DLTs over sequences of treatment cycles via a Bayesian cumulative modeling approach, where the probability of DLT is modele…
In traditional dose-finding studies, dose-limiting toxicity (DLT) is determined within a fixed time observation window where DLT is often defined as a binary outcome. In the setting of oncology dose-finding trials, often patients in advanced stage of diseases are enrolled. Therefore, disease progression may occur within the DLT observation window leading to treatment discontinuation and rendering the patient unevaluable for DLT assessment. As a result, additional patients have to be enrolled, increasing the sample size. We propose and compare several practical methods for handling disease prog…
An objective of phase I dose-finding trials is to find the maximum tolerated dose; the dose with a particular risk of toxicity. Frequently, this risk is assessed across the first cycle of therapy. However, in oncology, a course of treatment frequently consists of multiple cycles of therapy. In many cases, the overall risk of toxicity for a given treatment is not fully encapsulated by observations from the first cycle, and hence it is advantageous to include toxicity outcomes from later cycles in phase I trials. Extending the follow up period in a trial naturally extends the total length of the…
In learning-phase clinical trials in drug development, adaptive designs can be efficient and highly informative when used appropriately. In this article, we extend the multiple comparison procedures with modeling techniques (MCP-Mod) procedure with generalized multiple contrast tests (GMCTs) to two-stage adaptive designs for establishing proof-of-concept. The results of an interim analysis of first-stage data are used to adapt the candidate dose-response models and the dosages studied in the second stage. GMCTs are used in both stages to obtain stage-wise p-values, which are then combined to d…
Bayesian adaptive designs have gained popularity in all phases of clinical trials with numerous new developments in the past few decades. During the COVID-19 pandemic, the need to establish evidence for the effectiveness of vaccines, therapeutic treatments and policies that could resolve or control the crisis emphasized the advantages offered by efficient and flexible clinical trial designs. In many COVID-19 clinical trials, due to the high level of uncertainty, Bayesian adaptive designs were considered advantageous. Designing Bayesian adaptive trials, however, requires extensive simulation st…
Phase I-II cancer clinical trial designs are intended to accelerate drug development. In cases where efficacy cannot be ascertained in a short period of time, it is common to divide the study in two stages: i) a first stage in which dose is escalated based only on toxicity data and we look for the maximum tolerated dose (MTD) set and ii) a second stage in which we search for the most efficacious dose within the MTD set. Current available approaches in the area of continuous dose levels involve fixing the MTD after stage I and discarding all collected stage I efficacy data. However, this method…
The COVID-19 pandemic continues to affect the conduct of clinical trials globally. Complications may arise from pandemic-related operational challenges such as site closures, travel limitations and interruptions to the supply chain for the investigational product, or from health-related challenges such as COVID-19 infections. Some of these complications lead to unforeseen intercurrent events in the sense that they affect either the interpretation or the existence of the measurements associated with the clinical question of interest. In this article, we demonstrate how the ICH E9(R1) Addendum o…
Incorporating historical data or real-world evidence has a great potential to improve the efficiency of phase I clinical trials and to accelerate drug development. For model-based designs, such as the continuous reassessment method (CRM), this can be conveniently carried out by specifying a "skeleton," i.e., the prior estimate of dose limiting toxicity (DLT) probability at each dose. In contrast, little work has been done to incorporate historical data or real-world evidence into model-assisted designs, such as the Bayesian optimal interval (BOIN), keyboard, and modified toxicity probability i…
The analysis and planning methods for competing risks model have been described in the literatures in recent decades, and non-inferiority clinical trials are helpful in current pharmaceutical practice. Analytical methods for non-inferiority clinical trials in the presence of competing risks were investigated by Parpia et al., who indicated that the proportional sub-distribution hazard model is appropriate in the context of biological studies. However, the analytical methods of competing risks model differ from those appropriate for analyzing non-inferiority clinical trials with a single outcom…
Background -- In phase I clinical trials, historical data may be available through multi-regional programs, reformulation of the same drug, or previous trials for a drug under the same class. Statistical designs that borrow information from historical data can reduce cost, speed up drug development, and maintain safety. Purpose -- Based on a hybrid design that partly uses probability models and partly uses algorithmic rules for decision making, we aim to improve the efficiency of the dose-finding trials in the presence of historical data, maintain safety for patients, and achieve a level of si…
Many phase II clinical trials have used survival outcomes as the primary endpoints in recent decades. Suppose the radiotherapy is evaluated in a phase II trial using survival outcomes. In that case, 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, much literature has examined that patients receiving the same radiotherapy dose may yield distinct responses due to their heterogeneous radiation susceptibility statuses. Therefore, the "one-dose-fit-all" strategy often fails, and it is m…
Sejong University（世宗大学）；University of Virginia（弗吉尼亚大学）；UCLA（加州大学洛杉矶分校）；Yonsei University（延世大学）；University of Cambridge（剑桥大学）；The Alan Turing Institute（艾伦·图灵研究所）
Phase I clinical trials are designed to test the safety (non-toxicity) of drugs and find the maximum tolerated dose (MTD). This task becomes significantly more challenging when multiple-drug dose-combinations (DC) are involved, due to the inherent conflict between the exponentially increasing DC candidates and the limited patient budget. This paper proposes a novel Bayesian design, SDF-Bayes, for finding the MTD for drug combinations in the presence of safety constraints. Rather than the conventional principle of escalating or de-escalating the current dose of one drug (perhaps alternating bet…
The landscape of dose-finding designs for phase I clinical trials is rapidly shifting in the recent years, noticeably marked by the emergence of interval-based designs. We categorize them as the iDesigns and the IB-Designs. The iDesigns are originated by the toxicity probability inter- val (TPI) designs and its two modifications, the mTPI and mTPI-2 designs. The IB-Designs started as the cumulative cohort design (CCD) and is recently extended by the BOIN design. We discuss the differences and similarities between these two classes of interval-based designs, and compare their simulation perform…
Dose-finding design based on level set estimation in phase I cancer clinical trials
Nagoya University（名古屋大学）；Kyoto University（京都大学）；MI-6 Ltd.（MI-6有限公司）；Nagoya Institute of Technology（名古屋工业大学）；The Institute of Statistical Mathematics（统计数学研究所）
In two harmonized efficacy studies to prevent HIV infection through multiple infusions of the monoclonal antibody VRC01, a key objective is to evaluate whether the serum concentration of VRC01, which changes cyclically over time along with the infusion schedule, is associated with the rate of HIV infection. Simulation studies are needed in the development of such survival models. In this paper, we consider simulating event time data with a continuous time-varying covariate whose values vary with time through multiple drug administration cycles, and whose effect on survival changes differently …
This paper proposes a new method for analysis of adverse event data in clinical trials. The method is illustrated by application to data on 4 phase III clinical trials, two on breast cancer and two on diabetes mellitus.
In oncology dose-finding trials, due to staggered enrollment, it might be desirable to make dose-assignment decisions in real-time in the presence of pending toxicity outcomes, for example, when the dose-limiting toxicity is late-onset. Patients' time-to-event information may be utilized to facilitate such decisions. We review statistical frameworks for time-to-event modeling in dose-finding trials and summarize existing designs into two classes: TITE designs and POD designs. TITE designs are based on inference on toxicity probabilities, while POD designs are based on inference on dose-finding…
Nowadays, more and more clinical trials choose combinational agents as the intervention to achieve better therapeutic responses. However, dose-finding for combinational agents is much more complicated than single agent as the full order of combination dose toxicity is unknown. Therefore, regular phase I designs are not able to identify the maximum tolerated dose (MTD) of combinational agents. Motivated by such needs, plenty of novel phase I clinical trial designs for combinational agents were proposed. With so many available designs, research that compare their performances, explore parameters…
Clinical trials are considered as the golden standard for medical device validation. However, many sacrifices have to be made during the design and conduction of the trials due to cost considerations and partial information, which may compromise the significance of the trial results. In this paper, we proposed a model-based pre-clinical trial framework using statistical model checking. Physiological models represent disease mechanism, which enable automated adjudication of simulation results. Sampling of the patient parameters and hypothesis testing are performed by statistical model checker. …
Existing trials had not taken enough consideration of their population representativeness, which can lower the effectiveness when the treatment is applied in real-world clinical practice. We analyzed the eligibility criteria of Bevacizumab colorectal cancer treatment trials, assessed their a priori generalizability, and examined how it affects patient outcomes when applied in real-world clinical settings. To do so, we extracted patient-level data from a large collection of electronic health records (EHRs) from the OneFlorida consortium. We built a zero-inflated negative binomial model using a …
University of Cambridge（剑桥大学）；Yonsei University（延世大学）；University of Virginia（弗吉尼亚大学）；University of Oxford（牛津大学）；UCLA（加利福尼亚大学洛杉矶分校）
Clinical trials in the medical domain are constrained by budgets. The number of patients that can be recruited is therefore limited. When a patient population is heterogeneous, this creates difficulties in learning subgroup specific responses to a particular drug and especially for a variety of dosages. In addition, patient recruitment can be difficult by the fact that clinical trials do not aim to provide a benefit to any given patient in the trial. In this paper, we propose C3T-Budget, a contextual constrained clinical trial algorithm for dose-finding under both budget and safety constraints…
Effective decision making from randomised controlled clinical trials relies on robust interpretation of the numerical results. However, the language we use to describe clinical trials can cause confusion both in trial design and in comparing results across trials. ACceptability Curve Estimation using Probability Above Threshold (ACCEPT) aids comparison between trials (even where of different designs) by harmonising reporting of results, acknowledging different interpretations of the results may be valid in different situations, and moving the focus from comparison to a pre-specified value to i…
In many clinical trials, individuals in different subgroups have experience differential treatment effects. This leads to individualized differences in treatment benefit. In this article, we introduce the general concept of predictive directions, which are risk scores motivated by potential outcomes considerations. These techniques borrow heavily from sufficient dimension reduction (SDR) and causal inference methodology. Under some conditions, one can use existing methods from the SDR literature to estimate the directions assuming an idealized complete data structure, which subsequently yields…
Modeling dose-response relationships of drugs is essential to understanding their effect on patient outcomes under realistic circumstances. While intention-to-treat analyses of clinical trials provide the effect of assignment to a particular drug and dose, they do not capture observed exposure after factoring in non-adherence and dropout. We develop Bayesian methods to flexibly model dose-response relationships of binary outcomes with continuous treatment, allowing for treatment effect heterogeneity and a non-linear response surface. We use a hierarchical framework for meta-analysis with the e…
Individualized treatment rules (ITRs) tailor treatments according to individual patient characteristics. They can significantly improve patient care and are thus becoming increasingly popular. The data collected during randomized clinical trials are often used to estimate the optimal ITRs. However, these trials are generally expensive to run, and, moreover, they are not designed to efficiently estimate ITRs. In this paper, we propose a cost-effective estimation method from an active learning perspective. In particular, our method recruits only the "most informative" patients (in terms of learn…
Phase III randomized clinical trials play a monumentally critical role in the evaluation of new medical products. Because of the intrinsic nature of uncertainty embedded in our capability in assessing the efficacy of a medical product, interpretation of trial results relies on statistical principles to control the error of false positives below desirable level. The well-established statistical hypothesis testing procedure suffers from two major limitations, namely, the lack of flexibility in the thresholds to claim success and the lack of capability of controlling the total number of false pos…
