Dose escalation with overdose control using a quasi-continuous toxicity score in cancer Phase I clinical trials

PKBOIN-12: A Bayesian Optimal Interval Phase I/II Design Incorporating Pharmacokinetics Outcomes to Find the Optimal Biological Dose

Immunotherapies and targeted therapies have gained popularity due to their promising therapeutic effects across multiple treatment areas. The focus of early phase dose-finding clinical trials has shifted from finding the maximum tolerated dose (MTD) to identifying the optimal biological dose (OBD), which aims to balance the toxicity and efficacy outcomes, thus optimizing the risk-benefit trade-off. These trials often collect multiple pharmacokinetics (PK) outcomes to assess drug exposure, which has shown correlations with toxicity and efficacy outcomes but has not been utilized in the current dose-finding designs for OBD selection. Moreover, PK outcomes are usually available within days after initial treatment, much faster than toxicity and efficacy outcomes. To bridge this gap, we introduce the innovative model-assisted PKBOIN-12 design, which enhances BOIN12 by integrating PK information into both the dose-finding algorithm and the final OBD determination process. We further extend PKBOIN-12 to TITE-PKBOIN-12 to address the challenges of late-onset toxicity and efficacy outcomes. Simulation results demonstrate that PKBOIN-12 more effectively identifies the OBD and allocates a greater number of patients to it than BOIN12. Additionally, PKBOIN-12 decreases the probability of selecting inefficacious doses as the OBD by excluding those with low drug exposure. Comprehensive simulation studies and sensitivity analysis confirm the robustness of both PKBOIN-12 and TITE-PKBOIN-12 in various scenarios.

Dose Finding in Oncology Trials Guided by Ordinal Toxicity Grades Using Continuous Dose Levels

We present a Bayesian adaptive design for dose finding in oncology trials with application to a first-in-human trial. The design is based on the escalation with overdose control principle and uses an intermediate grade 2 toxicity in addition to the traditional binary indicator of dose-limiting toxicity (DLT) to guide the dose escalation and de-escalation. We model the dose-toxicity relationship using the proportional odds model. This assumption satisfies an important ethical concern when a potentially toxic drug is first introduced in the clinic; if a patient experiences grade 2 toxicity at the most, then the amount of dose escalation is lower relative to that wherein if this patient experienced a maximum of grade 1 toxicity. This results in a more careful dose escalation. The performance of the design was assessed by deriving the operating characteristics under several scenarios for the true MTD and expected proportions of grade 2 toxicities. In general, the trial design is safe and achieves acceptable efficiency of the estimated MTD for a planned sample size of twenty patients. At the time of writing this manuscript, twelve patients have been enrolled to the trial.

A Bayesian quasi-likelihood design for identifying the minimum effective dose and maximum utility dose in dose-ranging studies

Most existing dose-ranging study designs focus on assessing the dose-efficacy relationship and identifying the minimum effective dose. There is an increasing interest in optimizing the dose based on the benefit-risk tradeoff. We propose a Bayesian quasi-likelihood dose-ranging design that jointly considers safety and efficacy to simultaneously identify the minimum effective dose and the maximum utility dose to optimize the benefit-risk tradeoff. The binary toxicity endpoint is modeled using a beta-binomial model. The efficacy endpoint is modeled using the quasi-likelihood approach to accommodate various types of data (e.g. binary, ordinal or continuous) without imposing any parametric assumptions on the dose-response curve. Our design utilizes a utility function as a measure of benefit-risk tradeoff and adaptively assign patients to doses based on the doses' likelihood of being the minimum effective dose and maximum utility dose. The design takes a group-sequential approach. At each interim, the doses that are deemed overly toxic or futile are dropped. At the end of the trial, we use posterior probability criteria to assess the strength of the dose-response relationship for establishing the proof-of-concept. If the proof-of-concept is established, we identify the minimum effective dose and maximum utility dose. Our simulation study shows that compared with some existing designs, the Bayesian quasi-likelihood dose-ranging design is robust and yields competitive performance in establishing proof-of-concept and selecting the minimum effective dose. Moreover, it includes an additional feature for further maximum utility dose selection.

Bayesian dose escalation with overdose and underdose control utilizing all toxicities in Phase I/II clinical trials

Escalation with overdose control (EWOC) is a commonly used Bayesian adaptive design, which controls overdosing risk while estimating maximum tolerated dose (MTD) in cancer Phase I clinical trials. In 2010, Chen and his colleagues proposed a novel toxicity scoring system to fully utilize patients' toxicity information by using a normalized equivalent toxicity score (NETS) in the range 0 to 1 instead of a binary indicator of dose limiting toxicity (DLT). Later in 2015, by adding underdosing control into EWOC, escalation with overdose and underdose control (EWOUC) design was proposed to guarantee patients the minimum therapeutic effect of drug in Phase I/II clinical trials. In this paper, the EWOUC-NETS design is developed by integrating the advantages of EWOUC and NETS in a Bayesian context. Moreover, both toxicity response and efficacy are treated as continuous variables to maximize trial efficiency. The dose escalation decision is based on the posterior distribution of both toxicity and efficacy outcomes, which are recursively updated with accumulated data. We compare the operation characteristics of EWOUC-NETS and existing methods through simulation studies under five scenarios. The study results show that EWOUC-NETS design treating toxicity and efficacy outcomes as continuous variables can increase accuracy in identifying the optimized utility dose (OUD) and provide better therapeutic effects.

A flexible dose-response modeling framework based on continuous toxicity outcomes in phase I cancer clinical trials

BACKGROUND

The past few decades have seen remarkable developments in dose-finding designs for phase I cancer clinical trials. While many of these designs rely on a binary toxicity response, there is an increasing focus on leveraging continuous toxicity responses. A continuous toxicity response pertains to a quantitative measure represented by real numbers. A higher value corresponds not only to an elevated likelihood of side effects for patients but also to an increased probability of treatment efficacy. This relationship between toxicity and dose is often nonlinear, necessitating flexibility in the quest to find an optimal dose.

METHODS

A flexible, fully Bayesian dose-finding design is proposed to capitalize on continuous toxicity information, operating under the assumption that the true shape of the dose-toxicity curve is nonlinear.

RESULTS

We conduct simulations of clinical trials across varying scenarios of non-linearity to evaluate the operational characteristics of the proposed design. Additionally, we apply the proposed design to a real-world problem to determine an optimal dose for a molecularly targeted agent.

CONCLUSIONS

Phase I cancer clinical trials, designed within a fully Bayesian framework with the utilization of continuous toxicity outcomes, offer an alternative approach to finding an optimal dose, providing unique benefits compared to trials designed based on binary toxicity outcomes.

qTPI: A quasi-toxicity probability interval design for phase I trials with multiple-grade toxicities

The common terminology criteria for adverse events by the National Cancer Institute has greatly facilitated the revolution of drug development and an increasing number of Phase I trials have started to collect multiple-grade toxicity endpoints. Appropriate and yet transparent Phase I statistical designs for multiple-grade toxicities are therefore in great needs. In this article, we propose a quasi-toxicity probability interval (qTPI) design that incorporates a quasi-continuous measure of the toxicity probability (q T P ) into the Bayesian theoretic framework of the interval based designs. Multiple-grade toxicity outcomes of each patient are mapped to q T P according to a severity weight matrix. Dose-toxicity curve underlying the dosing decisions in the qTPI design is continuously updated using accumulating trial data. Numerical simulations investigating the operating characteristics of qTPI show that qTPI achieved better safety, accuracy and reliability compared to designs that rely on binary toxicity data. Furthermore, parameter elicitation in qTPI is simple and does not involve multiple hypothetical cohorts specification. Finally, a hypothetical soft tissue sarcoma trial with six toxicity types and grade 0 to grade 4 severity grades is illustrated with patient-by-patient dose allocation under the qTPI design.

Modeling synergism in early phase cancer trials with drug combination with continuous dose levels: is there an added value?

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 simultaneously receiving different dose combinations and response adaptive randomization. We compare trial safety and efficiency of the estimated maximum tolerated dose (MTD) curve between models that include an interaction term with models without the synergism parameter with extensive simulations. Under a selected class of dose-toxicity models and dose escalation algorithm, we found that not including an interaction term in the model can compromise the safety of the trial and reduce the pointwise reliability of the estimated MTD curve.

Optimization of dose selection using multiple surrogates of toxicity as a continuous variable in phase I cancer trial

In phase I trials, it is the top priority of clinicians to effectively treat patients and minimize the chance of exposing them to subtherapeutic and overly toxic doses, while exploiting patient information. Motived by this practical consideration, we revive the one parameter linear dose-finder developed in 1970s to accommodate a continuous toxicity response in the phase I cancer clinical trials, which is called the two parameters linear dose-finder (2PLD). The 2PLD is a fully Bayesian model that assumes a linear relationship between toxicity response and dose. We suggest a dose search algorithm based on the 2PLD to exploit the grades of toxicities from multiple adverse events to align with Common Toxicity Criteria for Adverse Events provided by the National Cancer Institute. The proposed search procedure suggests an optimal dose to each patient by using accrued patients' information while controlling the posterior probability of overdose. The heterogeneity of patients in dose reaction is addressed by making a fully Bayesian inference about the standard deviation of toxicity responses. The 2PLD can be an attractive tool for clinical scientists due to its parsimonious description of a toxicity-dose curve and medical interpretation as well as an automatic posterior computation. We illustrate the performance of this design using simulation data to identify the maximum tolerated dose.

Adaptive Bayesian phase I clinical trial designs for estimating the maximum tolerated doses for two drugs while fully utilizing all toxicity information

The combined treatments with multiple drugs are very common in the contemporary medicine, especially for medical oncology. Therefore, we developed a Bayesian adaptive Phase I clinical trial design entitled escalation with overdoing control using normalized equivalent toxicity score for estimating maximum tolerated dose (MTD) contour of two drug combination (EWOC-NETS-COM) used for oncology trials. The normalized equivalent toxicity score (NETS) as the primary endpoint of clinical trial is assumed to follow quasi-Bernoulli distribution and treated as quasi-continuous random variable in the logistic linear regression model which is used to describe the relationship between the doses of the two agents and the toxicity response. Four parameters in the dose-toxicity model were re-parameterized to parameters with explicit clinical meanings to describe the association between NETS and doses of two agents. Noninformative priors were used and Markov chain Monte Carlo was employed to update the posteriors of the four parameters in dose-toxicity model. Extensive simulations were conducted to evaluate the safety, trial efficiency, and MTD estimation accuracy of EWOC-NETS-COM under different scenarios, using the EWOC as reference. The results demonstrated that EWOC-NETS-COM not only efficiently estimates MTD contour of multiple drugs but also provides better trial efficiency by fully utilizing all toxicity information.

On the properties of the toxicity index and its statistical efficiency

Cancer clinical trials typically generate detailed patient toxicity data. The most common measure used to summarize patient toxicity is the maximum grade among all toxicities and it does not fully represent the toxicity burden experienced by patients. In this article, we study the mathematical and statistical properties of the toxicity index (TI), in an effort to address this deficiency. We introduce a total ordering, (T-rank), that allows us to fully rank the patients according to how frequently they exhibit toxicities, and show that TI is the only measure that preserves the T-rank among its competitors. Moreover, we propose a Poisson-Limit model for sparse toxicity data. Under this model, we develop a general two-sample test, which can be applied to any summary measure for detecting differences among two population of toxicity data. We derive the asymptotic power function of this class as well as the asymptotic relative efficiency (ARE) of the members of the class. We evaluate the ARE formula empirically and show that if the data are drawn from a random Poisson-Limit model, the TI is more efficient, with high probability, than the maximum and the average summary measures. Finally, we evaluate our method on clinical trial toxicity data and show that TI has a higher power in detecting the differences in toxicity profile among treatments. The results of this article can be applied beyond toxicity modeling, to any problem where one observes a sparse array of scores on subjects and a ranking based on extreme scores is desirable.

A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations with Ordinal Toxicity Grades

We propose a Bayesian adaptive design for early phase drug combination cancer trials incorporating ordinal grade of toxicities. Parametric models are used to describe the relationship between the dose combinations and the probabilities of the ordinal toxicities under the proportional odds assumption. Trial design proceeds by treating cohorts of two patients simultaneously receiving different dose combinations. Specifically, at each stage of the trial, we seek the dose of one agent by minimizing the Bayes risk with respect to a loss function given the current dose of the other agent. We consider two types of loss functions corresponding to the Continual Reassessment Method (CRM) and Escalation with Overdose Control (EWOC). At the end of the trial, we estimate the MTD curve as a function of Bayes estimates of the model parameters. We evaluate design operating characteristics in terms of safety of the trial and percent of dose recommendation at dose combination neighborhoods around the true MTD by comparing this design to the one that uses a binary indicator of DLT. The methodology is further adapted to the case of a pre-specified discrete set of dose combinations.

Interactive calculator for operating characteristics of phase I cancer clinical trials using standard 3+3 designs

Among various Phase I clinical trial designs, rule-based standard 3 + 3 designs are the most widely utilized for their simplicity and robustness. It is necessary to define crucial operating characteristics of a Phase I clinical trial before it starts. Based on the assumed probability of dose limiting toxicity (DLT) at each tested dose level, Lin and Shih elaborated formulas to calculate the five key operating characteristics of Phase I clinical trials using the two subtypes of standard 3 + 3 designs (with vs without dose de-escalation): probability of each dose level being chosen as the maximum tolerated dose (MTD); expected number of patients treated at each dose level; expected number of patients experiencing DLT at each dose level; target toxicity level (TTL) (expected probability of DLT at MTD); expected total number of patients experiencing DLT. Understanding these formulas requires advanced statistical knowledge and the formulas are too complicated to be used directly. To facilitate their application, we have developed stand-alone interactive software for convenient calculation of these key operating characteristics. The calculated results are presented in tables and plots that can be saved and easily edited for further use. Some examples of calculation using the software are presented and discussed.

A Bayesian adaptive design for cancer phase I trials using a flexible range of doses

We present a Bayesian adaptive design for dose finding in cancer phase I clinical trials. The goal is to estimate the maximum tolerated dose (MTD) after possible modification of the dose range during the trial. Parametric models are used to describe the relationship between the dose and the probability of dose-limiting toxicity (DLT). We investigate model reparameterization in terms of the probabilities of DLT at the minimum and maximum available doses at the start of the trial. Trial design proceeds using escalation with overdose control (EWOC), where at each stage of the trial we seek the dose of the agent such that the posterior probability of exceeding the MTD of this agent is bounded by a feasibility bound. At any time during the trial, we test whether the MTD is below or above the minimum and maximum doses, respectively. If during the trial there is evidence that the MTD is outside the range of doses, we extend the range of doses and complete the trial with the planned sample size. At the end of the trial, a Bayes estimate of the MTD is proposed. We evaluate design operating characteristics in terms of safety of the trial design and efficiency of the MTD estimate under various scenarios and model misspecification. The methodology is further compared to the original EWOC design. We showed by comprehensive simulation studies that the proposed method is safe and can estimate the MTD more efficiently than the original EWOC design.

How to keep high-risk studies ethical: classifying candidate solutions

This article lays out a wide spectrum of candidate ethical solutions for the challenge on which this JME symposium focuses: the benefit:risk ratio challenge to some early-phase HIV cure and remission studies. These candidate solutions fall into four categories: ones that seek to reduce risks in early-phase HIV cure and remission studies, ones that enhance the benefits for these studies' participants (or show that those were adequate in the first place), ones that focus on participants' free and informed consent to participate and ones according to whom the large benefits to non-participants can defeat considerations about individual participant net risks. In so doing, this article also structures the rest of the symposium.

Adaptive Estimation of Personalized Maximum Tolerated Dose in Cancer Phase I Clinical Trials Based on All Toxicities and Individual Genomic Profile

BACKGROUND

Many biomarkers have been shown to be associated with the efficacy of cancer therapy. Estimation of personalized maximum tolerated doses (pMTDs) is a critical step toward personalized medicine, which aims to maximize the therapeutic effect of a treatment for individual patients. In this study, we have established a Bayesian adaptive Phase I design which can estimate pMTDs by utilizing patient biomarkers that can predict susceptibility to specific adverse events and response as covariates.

METHODS

Based on a cutting-edge cancer Phase I clinical trial design called escalation with overdose control using normalized equivalent toxicity score (EWOC-NETS), which fully utilizes all toxicities, we propose new models to incorporate patient biomarker information in the estimation of pMTDs for novel cancer therapeutic agents. The methodology is fully elaborated and the design operating characteristics are evaluated with extensive simulations.

RESULTS

Simulation studies demonstrate that the utilization of biomarkers in EWOC-NETS can estimate pMTDs while maintaining the original merits of this Phase I trial design, such as ethical constraint of overdose control and full utilization of all toxicity information, to improve the accuracy and efficiency of the pMTD estimation.

CONCLUSIONS

Our novel cancer Phase I designs with inclusion of covariate(s) in the EWOC-NETS model are useful to estimate a personalized MTD and have substantial potential to improve the therapeutic effect of drug treatment.

Dose Finding for Drug Combination in Early Cancer Phase I Trials using Conditional Continual Reassessment Method

We describe a dose escalation algorithm for drug combinations in cancer phase I clinical trials. Parametric models for describing the association between the doses and the probability of dose limiting toxicity are used assuming univariate monotonicity of the dose-toxicity relationship. Trial design proceeds using the continual reassessment method, where at each stage of the trial, we seek the dose of one agent with estimated probability of toxicity closest to a target probability of toxicity given the current dose of the other agent. A Bayes estimate of the maximum tolerated dose (MTD) curve is proposed at the conclusion of the trial for continuous doses or a set of MTDs is determined in the case of discrete dose levels. We evaluate design operating characteristics in terms of safety of the trial and percent of dose recommendation at dose combination neighborhoods around the true MTD under various model generated scenarios and misspecification. The method is further assessed for varying algorithms enrolling cohorts of two and three patients receiving different doses and compared to previous approaches such as escalation with overdose control and two-dimensional design.

Approaches for informing optimal dose of behavioral interventions

BACKGROUND

There is little guidance about to how select dose parameter values when designing behavioral interventions.

PURPOSE

The purpose of this study is to present approaches to inform intervention duration, frequency, and amount when (1) the investigator has no a priori expectation and is seeking a descriptive approach for identifying and narrowing the universe of dose values or (2) the investigator has an a priori expectation and is seeking validation of this expectation using an inferential approach.

METHODS

Strengths and weaknesses of various approaches are described and illustrated with examples.

RESULTS

Descriptive approaches include retrospective analysis of data from randomized trials, assessment of perceived optimal dose via prospective surveys or interviews of key stakeholders, and assessment of target patient behavior via prospective, longitudinal, observational studies. Inferential approaches include nonrandomized, early-phase trials and randomized designs.

CONCLUSIONS

By utilizing these approaches, researchers may more efficiently apply resources to identify the optimal values of dose parameters for behavioral interventions.

Dose finding with drug combinations in cancer phase I clinical trials using conditional escalation with overdose control

We present a Bayesian adaptive design for dose finding of a combination of two drugs in cancer phase I clinical trials. The goal is to estimate the maximum tolerated dose (MTD) as a curve in the two-dimensional Cartesian plane. We use a logistic model to describe the relationship between the doses of the two agents and the probability of dose limiting toxicity. The model is re-parameterized in terms of parameters clinicians can easily interpret. Trial design proceeds using univariate escalation with overdose control, where at each stage of the trial, we seek a dose of one agent using the current posterior distribution of the MTD of this agent given the current dose of the other agent. At the end of the trial, an estimate of the MTD curve is proposed as a function of Bayes estimates of the model parameters. We evaluate design operating characteristics in terms of safety of the trial design and percent of dose recommendation at dose combination neighborhoods around the true MTD curve. We also examine the performance of the approach under model misspecifications for the true dose-toxicity relationship.

Escalation with overdose control using all toxicities and time to event toxicity data in cancer Phase I clinical trials

The primary purposes of Phase I cancer clinical trials are to determine the maximum tolerated dose (MTD) and the treatment schedule of a new drug. Phase I trials usually involve a small number of patients so that fully utilizing all toxicity information including time to event toxicity data is key to improving the trial efficiency and the accuracy of MTD estimation. Chen et al. proposed a novel normalized equivalent toxicity score (NETS) system to fully utilize multiple toxicities per patient instead of a binary indicator of dose limiting toxicity (DLT). Cheung and Chappell developed the time to toxicity event (TITE) approach to incorporate time to toxicity event data. Escalation with overdose control (EWOC) is an adaptive Bayesian Phase I design which can allow rapid dose escalation while controlling the probability of overdosing patients. In this manuscript, we use EWOC as a framework and integrate it with the NETS system and the TITE approach to develop an advanced Phase I design entitled EWOC-NETS-TITE. We have conducted simulation studies to compare its operating characteristics using selected derived versions of EWOC because EWOC itself has already been extensively compared with common Phase I designs [3]. Simulation results demonstrate that EWOC-NETS-TITE can substantially improve the trial efficiency and accuracy of MTD determination as well as allow patients to be entered in a staggered fashion to significantly shorten trial duration. Moreover, user-friendly software for EWOC-NETS-TITE is under development.

Escalation with overdose control for phase I drug-combination trials

Dose finding for combined drugs has grown rapidly in oncology drug development. The escalation with overdose control (EWOC) method is a popular model-based dose-finding approach to single-agent phase I clinical trials. When two drugs are combined as a treatment, we propose a two-dimensional EWOC design for dose finding on the basis of a four-parameter logistic regression model. During trial conduct, we continuously update the posterior distribution of the maximum tolerated dose (MTD) combination to find the most appropriate dose combination for each cohort of patients. The probability that the next assigned dose combination exceeds the MTD combination can be controlled by a feasibility bound, which is based on a prespecified quantile level of the MTD distribution such as to reduce the possibility of overdosing. We determine dose escalation, de-escalation, or staying at the same doses by searching the MTD combination along the rows and columns in a two-drug combination matrix, respectively. We conduct simulation studies to examine the performance of the two-dimensional EWOC design under various practical scenarios, and illustrate it with a trial example.

Interactive Software "Isotonic Design using Normalized Equivalent Toxicity Score (ID-NETS©TM)" for Cancer Phase I Clinical Trials

Isotonic Design using Normalized Equivalent Toxicity Score (ID-NETS) is a novel Phase I design that integrates the novel toxicity scoring system originally proposed by Chen et al. [1] and the original Isotonic Design proposed by Leung et al. [2]. ID-NETS has substantially improved the accuracy of maximum tolerated dose (MTD) estimation and trial efficiency in the Phase I clinical trial setting by fully utilizing all toxicities experienced by each patient and treating toxicity response as a quasi-continuous variable instead of a binary indicator of dose limiting toxicity (DLT). To facilitate the incorporation of the ID-NETS method into the design and conduct of Phase I clinical trials, we have designed and developed a user-friendly software, ID-NETS(©TM), which has two functions: 1) Calculating the recommended dose for the subsequent patient cohort using available completed data; and 2) Performing simulations to obtain the operating characteristics of a trial designed with ID-NETS. Currently, ID-NETS(©TM)v1.0 is available for free download at http://winshipbbisr.emory.edu/IDNETS.html.

Escalation with Overdose Control Using Ordinal Toxicity Grades for Cancer Phase I Clinical Trials

We extend a Bayesian adaptive phase I clinical trial design known as escalation with overdose control (EWOC) by introducing an intermediate grade 2 toxicity when assessing dose-limiting toxicity (DLT). Under the proportional odds model assumption of dose-toxicity relationship, we prove that in the absence of DLT, the dose allocated to the next patient given that the previously treated patient had a maximum of grade 2 toxicity is lower than the dose given to the next patient had the previously treated patient exhibited a grade 0 or 1 toxicity at the most. Further, we prove that the coherence properties of EWOC are preserved. Simulation results show that the safety of the trial is not compromised and the efficiency of the estimate of the maximum tolerated dose (MTD) is maintained relative to EWOC treating DLT as a binary outcome and that fewer patients are overdosed using this design when the true MTD is close to the minimum dose.

Methodology and Application of Adaptive and Sequential Approaches in Contemporary Clinical Trials

The clinical trial, a prospective study to evaluate the effect of interventions in humans under prespecified conditions, is a standard and integral part of modern medicine. Many adaptive and sequential approaches have been proposed for use in clinical trials to allow adaptations or modifications to aspects of a trial after its initiation without undermining the validity and integrity of the trial. The application of adaptive and sequential methods in clinical trials has significantly improved the flexibility, efficiency, therapeutic effect, and validity of trials. To further advance the performance of clinical trials and convey the progress of research on adaptive and sequential methods in clinical trial design, we review significant research that has explored novel adaptive and sequential approaches and their applications in Phase I, II, and III clinical trials and discuss future directions in this field of research.