اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدBayesian Decision-optimal Interval Designs for Phase I Clinical Trials
200
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Interval designs are a class of phase I trial designs for which the decision of dose assignment is determined by comparing the observed toxicity rate at the current dose with a prespecified (toxicity tolerance) interval. If the observed toxicity rate is located within the interval, we retain the current dose; if the observed toxicity rate is greater than the upper boundary of the interval, we deescalate the dose; and if the observed toxicity rate is smaller than the lower boundary of the interval, we escalate the dose. The most critical issue for the interval design is choosing an appropriate interval so that the design has good operating characteristics. By casting dose finding as a Bayesian decision-making problem, we propose new flexible methods to select the interval boundaries so as to minimize the probability of inappropriate dose assignment for patients. We show, both theoretically and numerically, that the resulting optimal interval designs not only have desirable finite- and large-sample properties, but also are particularly easy to implement in practice. Compared to existing designs, the proposed (local) optimal design has comparable average performance, but a lower risk of yielding a poorly performing clinical trial.
قيم البحث
اقرأ أيضاً
Incorporating preclinical animal data, which can be regarded as a special kind of historical data, into phase I clinical trials can improve decision making when very little about human toxicity is known. In this paper, we develop a robust hierarchica
l modelling approach to leverage animal data into new phase I clinical trials, where we bridge across non-overlapping, potentially heterogeneous patient subgroups. Translation parameters are used to bring both historical and contemporary data onto a common dosing scale. This leads to feasible exchangeability assumptions that the parameter vectors, which underpin the dose-toxicity relationship per study, are assumed to be drawn from a common distribution. Moreover, human dose-toxicity parameter vectors are assumed to be exchangeable either with the standardised, animal study-specific parameter vectors, or between themselves. Possibility of non-exchangeability for each parameter vector is considered to avoid inferences for extreme subgroups being overly influenced by the other. We illustrate the proposed approach with several trial data examples, and evaluate the operating characteristics of our model compared with several alternatives in a simulation study. Numerical results show that our approach yields robust inferences in circumstances, where data from multiple sources are inconsistent and/or the bridging assumptions are incorrect.
Integrated phase I-II clinical trial designs are efficient approaches to accelerate drug development. In cases where efficacy cannot be ascertained in a short period of time, two-stage approaches are usually employed. When different patient populatio
ns are involved across stages, it is worth of discussion about the use of efficacy data collected from both stages. In this paper, we focus on a two-stage design that aims to estimate safe dose combinations with a certain level of efficacy. In stage I, conditional escalation with overdose control (EWOC) is used to allocate successive cohorts of patients. The maximum tolerated dose (MTD) curve is estimated based on a Bayesian dose-toxicity model. In stage II, we consider an adaptive allocation of patients to drug combinations that have a high probability of being efficacious along the obtained MTD curve. A robust Bayesian hierarchical model is proposed to allow sharing of information on the efficacy parameters across stages assuming the related parameters are either exchangeable or nonexchangeable. Under the assumption of exchangeability, a random-effects distribution is specified for the main effects parameters to capture uncertainty about the between-stage differences. The proposed methodology is assessed with extensive simulations motivated by a real phase I-II drug combination trial using continuous doses.
We propose BaySize, a sample size calculator for phase I clinical trials using Bayesian models. BaySize applies the concept of effect size in dose finding, assuming the MTD is defined based on an equivalence interval. Leveraging a decision framework
that involves composite hypotheses, BaySize utilizes two prior distributions, the fitting prior (for model fitting) and sampling prior (for data generation), to conduct sample size calculation under desirable statistical power. Look-up tables are generated to facilitate practical applications. To our knowledge, BaySize is the first sample size tool that can be applied to a broad range of phase I trial designs.
A central goal in designing clinical trials is to find the test that maximizes power (or equivalently minimizes required sample size) for finding a true research hypothesis subject to the constraint of type I error. When there is more than one test,
such as in clinical trials with multiple endpoints, the issues of optimal design and optimal policies become more complex. In this paper we address the question of how such optimal tests should be defined and how they can be found. We review different notions of power and how they relate to study goals, and also consider the requirements of type I error control and the nature of the policies. This leads us to formulate the optimal policy problem as an explicit optimization problem with objective and constraints which describe its specific desiderata. We describe a complete solution for deriving optimal policies for two hypotheses, which have desired monotonicity properties, and are computationally simple. For some of the optimization formulations this yields optimal policies that are identical to existing policies, such as Hommels procedure or the procedure of Bittman et al. (2009), while for others it yields completely novel and more powerful policies than existing ones. We demonstrate the nature of our novel policies and their improved power extensively in simulation and on the APEX study (Cohen et al., 2016).
We propose an information borrowing strategy for the design and monitoring of phase II basket trials based on the local multisource exchangeability assumption between baskets (disease types). We construct a flexible statistical design using the propo
sed strategy. Our approach partitions potentially heterogeneous baskets into non-exchangeable blocks. Information borrowing is only allowed to occur locally, i.e., among similar baskets within the same block. The amount of borrowing is determined by between-basket similarities. The number of blocks and block memberships are inferred from data based on the posterior probability of each partition. The proposed method is compared to the multisource exchangeability model and Simons two-stage design, respectively. In a variety of simulation scenarios, we demonstrate the proposed method is able to maintain the type I error rate and have desirable basket-wise power. In addition, our method is computationally efficient compared to existing Bayesian methods in that the posterior profiles of interest can be derived explicitly without the need for sampling algorithms.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
