Embracing model-based designs for dose-finding trials

Background: Dose-finding trials are essential to drug development as they establish recommended doses for later-phase testing. We aim to motivate wider use of model-based designs for dose finding, such as the continual reassessment method (CRM). Methods: We carried out a literature review of dose-finding designs and conducted a survey to identify perceived barriers to their implementation. Results: We describe the benefits of model-based designs (flexibility, superior operating characteristics, extended scope), their current uptake, and existing resources. The most prominent barriers to implementation of a model-based design were lack of suitable training, chief investigators’ preference for algorithm-based designs (e.g., 3 þ 3), and limited resources for study design before funding. We use a real-world example to illustrate how these barriers can be overcome. Conclusions: There is overwhelming evidence for the benefits of CRM. Many leading pharmaceutical companies routinely implement model-based designs. Our analysis identified barriers for academic statisticians and clinical academics in mirroring the progress industry has made in trial design. Unified support from funders, regulators, and journal editors could result in more accurate doses for later-phase testing, and increase the efficiency and success of clinical drug development. We give recommendations for increasing the uptake of model-based designs for dose-finding trials in academia

Bivariate Generalization of the Time-to-Event Conditional Reassessment Method with a Novel Adaptive Randomization Method

Phase I clinical trials in oncology aim to evaluate the toxicity risk of new therapies and identify a safe but also effective dose for future studies. Traditional Phase I trials of chemotherapies focus on estimating the maximum tolerated dose (MTD). The rationale for finding the MTD is that better therapeutic effects are expected at higher dose levels as long as the risk of severe toxicity is acceptable. With the advent of a new generation of cancer treatments such as the molecularly targeted agents (MTAs) and immunotherapies, higher dose levels no longer guarantee increased therapeutic effects, and the focus has shifted to estimating the optimal biological dose (OBD). The OBD is a dose level with the highest biologic activity with acceptable toxicity. The search for OBD requires joint evaluation of toxicity and efficacy. Although several seamleass phase I/II designs have been published in recent years, there is not a consensus regarding an optimal design and further improvement is needed for some designs to be widely used in practice. In this dissertation, we propose a modification to an existing seamless phase I/II design by Wages and Tait (2015) for locating the OBD based on binary outcomes, and extend it to time to event (TITE) endpoints. While the original design showed promising results, we hypothesized that performance could be improved by replacing the original adaptive randomization stage with a different randomization strategy. We proposed to calculate dose assigning probabilities by averaging all candidate models that fit the observed data reasonably well, as opposed to the original design that based all calculations on one best-fit model. We proposed three different strategies to select and average among candidate models, and simulations are used to compare the proposed strategies to the original design. Under most scenarios, one of the proposed strategies allocates more patients to the optimal dose while improving accuracy in selecting the final optimal dose without increasing the overall risk of toxicity. We further extend this design to TITE endpoints to address a potential issue of delayed outcomes. The original design is most appropriate when both toxicity and efficacy outcomes can be observed shortly after the treatment, but delayed outcomes are common, especially for efficacy endpoints. The motivating example for this TITE extension is a Phase I/II study evaluating optimal dosing of all-trans retinoic acid (ATRA) in combination with a fixed dose of daratumumab in the treatment of relapsed or refractory multiple myeloma. The toxicity endpoint is observed in one cycle of therapy (i.e., 4 weeks) while the efficacy endpoint is assessed after 8 weeks of treatment. The difference in endpoint observation windows causes logistical challenges in conducting the trial, since it is not acceptable in practice to wait until both outcomes for each participant have been observed before sequentially assigning the dose of a newly eligible participant. The result would be a delay in treatment for patients and undesirably long trial duration. To address this issue, we generalize the time-to-event continual reassessment method (TITE-CRM) to bivariate outcomes with potentially non-monotonic dose-efficacy relationship. Simulation studies show that the proposed TITE design maintains similar probability in selecting the correct OBD comparing to the binary original design, but the number of patients treated at the OBD decreases as the rate of enrollment increases. We also develop an R package for the proposed methods and document the R functions used in this research. The functions in this R package assist implementation of the proposed randomization strategy and design. The input and output format of these functions follow similar formatting of existing R packages such as dfcrm or pocrm to allow direct comparison of results. Input parameters include efficacy skeletons, prior distribution of any model parameters, escalation restrictions, design method, and observed data. Output includes recommended dose level for the next patient, MTD, estimated model parameters, and estimated probabilities of each set of skeletons. Simulation functions are included in this R package so that the proposed methods can be used to design a trial based on certain parameters and assess performance. Parameters of these scenarios include total sample size, true dose-toxicity relationship, true dose-efficacy relationship, patient recruit rate, delay in toxicity and efficacy responses

On optimal designs for clinical trials: An updated review

Optimization of clinical trial designs can help investigators achieve higher qualityresults for the given resource constraints. The present paper gives an overviewof optimal designs for various important problems that arise in different stages ofclinical drug development, including phase I dose–toxicity studies; phase I/II studiesthat consider early efficacy and toxicity outcomes simultaneously; phase IIdose–response studies driven by multiple comparisons (MCP), modeling techniques(Mod), or their combination (MCP–Mod); phase III randomized controlled multiarmmulti-objective clinical trials to test difference among several treatment groups;and population pharmacokinetics–pharmacodynamics experiments. We find thatmodern literature is very rich with optimal design methodologies that can be utilizedby clinical researchers to improve efficiency of drug development

Statistical Concepts in Clinical Research

The overall objectives of the reference guide are: To introduce or review concepts to consider when designing a clinical trial To introduce or review the four phases of clinical trials including different types of designs for Phase I and Phase II clinical trials To introduce or review observational studies To introduce or review analysis of categorical, continuous, and time-to event measures as well as Bayesian methodology.https://openworks.mdanderson.org/mozart/1006/thumbnail.jp

Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs

Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical and efficient. These benefits are achieved while preserving the integrity and validity of the trial, through the pre-specification and proper adjustment for the possible alterations during the course of the trial. Despite much research in the statistical literature highlighting the potential advantages of adaptive designs over traditional fixed designs, the uptake of such methods in clinical research has been slow. One major reason for this is that different adaptations to trial designs, as well as their advantages and limitations, remain unfamiliar to large parts of the clinical community. The aim of this paper is to clarify where adaptive designs can be used to address specific questions of scientific interest; we introduce the main features of adaptive designs and commonly used terminology, highlighting their utility and pitfalls, and illustrate their use through case studies of adaptive trials ranging from early-phase dose escalation to confirmatory Phase III studies

Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs.

Funder: Cancer Research UK; doi: http://dx.doi.org/10.13039/501100000289Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical, and efficient. These benefits are achieved while preserving the integrity and validity of the trial, through the pre-specification and proper adjustment for the possible alterations during the course of the trial. Despite much research in the statistical literature highlighting the potential advantages of adaptive designs over traditional fixed designs, the uptake of such methods in clinical research has been slow. One major reason for this is that different adaptations to trial designs, as well as their advantages and limitations, remain unfamiliar to large parts of the clinical community. The aim of this paper is to clarify where adaptive designs can be used to address specific questions of scientific interest; we introduce the main features of adaptive designs and commonly used terminology, highlighting their utility and pitfalls, and illustrate their use through case studies of adaptive trials ranging from early-phase dose escalation to confirmatory phase III studies

Early phase clinical trials extension to the guidelines for the content of statistical analysis plans

This paper reports guidelines for the content of statistical analysis plans for early phase clinical trials, ensuring specification of the minimum reporting analysis requirements, by detailing extensions (11 new items) and modifications (25 items) to existing guidance after a review by various stakeholders

Parametric Models for Optimal Treatment Schedule Finding in Adaptive Early-Phase Clinical Trials.

Recently, a Bayesian paradigm was constructed for Phase I trial designs that allows for the evaluation and comparison of several nested treatment schedules, each consisting of a sequence of administration times. In contrast to traditional Phase I trial designs that seek to find a maximum tolerated dose (MTD), the goal of this new design was to determine a maximum tolerated schedule (MTS). Subject accrual, Bayesian estimation procedure and outcome adaptive decision-making are done in a sequential fashion as in classical Phase I trial designs. As competing approaches to the additive triangular hazard model proposed with the Bayesian paradigm, we propose several classes of parametric models for optimal treatment schedule finding by both maximum likelihood and Bayesian approaches. In part I of our research, we propose a mixture cure model to identify the MTS from a fixed number of nested treatment schedules. We model the cure rate with logistic regression and the conditional hazard function for the susceptible patients using a combination of two Weibull distributions to account for the non-monotonic nature of the hazard of toxicity. We use a modified likelihood approach to estimate parameters of interest. In part II of our research, we propose using maximum likelihood to estimate the parameters of the triangular hazard model in a single adminstration setting. We describe how to derive estimators for the change-point and boundary parameters of the triangular hazard model and discuss their large sample properties. In part III of our research, we propose a parametric non-mixture cure model to identify the optimal treatment schedule from a fixed number of nested treatment schedules. With such a model, we generate a continuous non-monotonic hazard function for the time to toxicity of each administration, as well as model the population probability of toxicity to increase with the number of administrations. Via simulation, we compare the performance of our proposed approaches to the existing method in a variety of settings motivated by an actual study in allogeneic bone marrow transplant patients. The parameters of interest are estimated by both maximum likelihood method (EM algorithm) and Bayesian approach (MCMC procedures).Ph.D.BiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57655/2/liuchang_1.pd

Essais cliniques de recherche de dose en oncologie : d'un schéma d'essai permettant l'inclusion continue à l’utilisation des données longitudinales de toxicité

Phase I dose-finding trials aim at identifying the maximum tolerated dose (MTD). The “3+3” design requires an interruption of enrolment while the evaluation of the previous three patients is pending. In pediatric oncology, investigators proposed the Rolling 6 design to allow for a more continuous enrollment. In a simulation study, we showed that an adaptive dose-finding design, with dose allocation guided by a statistical model not only minimizes accrual suspension as with the rolling 6, and but also led to identify more frequently the MTD. However, the performance of these designs in terms of correct identification of the MTD is limited by the binomial variability of the main outcome: the occurrence of dose-limiting toxicity over the first cycle of treatment. We have then proposed a new adaptive design using repeated ordinal data of toxicities experienced during all the cycles of treatment. We aim at identifying the dose associated with a specified tolerable probability of severe toxicity per cycle. The outcome was expressed as the worst toxicity experienced, in three categories (severe / moderate / no toxicity), repeated at each treatment cycle. It was modeled through a proportional odds mixed model. This model enables to seek for cumulated toxicity with time, and to increase the ability to identify the targeted dose, with no increased risk of toxicity, and without delaying study completion. We also compared this ordinal model to a more parsimonious logistic mixed model.Because of their applicability and efficiency, those models for longitudinal data should be more often used in phase I dose-finding trials.L’objectif des essais de phase I en oncologie est d’identifier la dose maximale tolérée (DMT). Le schéma « 3+3 » nécessite d’interrompre les inclusions en attendant l’évaluation d’une cohorte de trois patients pour définir la dose à attribuer aux patients suivants. Les investigateurs d’oncologie pédiatrique ont proposé l’adaptation Rolling 6 pour éviter cette suspension temporaire des inclusions. Dans une étude de simulation, nous avons montré qu’un schéma adaptatif avec attribution des doses basées sur un modèle statistique permettait de pallier ce problème, et identifiait plus fréquemment la DMT. Néanmoins ces trois schémas restent limités pour identifier la DMT, notamment du fait que le critère de jugement est un critère binaire, la survenue de toxicité dose-limitante sur un cycle de traitement. Nous avons proposé un nouveau schéma adaptatif utilisant les données ordinales répétées de toxicité sur l’ensemble des cycles de traitement. La dose à identifier est celle associée au taux de toxicité grave maximal par cycle que l’on juge tolérable. Le grade maximal de toxicité par cycle de traitement, en 3 catégories (grave / modéré / nul), a été modélisé par le modèle mixte à cotes proportionnelles. Le modèle est performant à la fois pour détecter un effet cumulé dans le temps et améliore l’identification de la dose cible, sans risque majoré de toxicité, et sans rallonger la durée des essais. Nous avons aussi étudié l’intérêt de ce modèle ordinal par rapport à un modèle logistique mixte plus parcimonieux. Ces modèles pour données longitudinales devraient être plus souvent utilisés pour l’analyse des essais de phase I étant donné leur pertinence et la faisabilité de leur implémentation