Mark Chang

Systematic comparison of the statistical operating characteristics of various Phase I oncology designs

Dose finding Phase I oncology designs can be broadly categorized as rule based, such as the 3 + 3 and the accelerated titration designs, or model based, such as the CRM and Eff-Tox designs. This paper systematically reviews and compares through simulations several statistical operating characteristics, including the accuracy of maximum tolerated dose (MTD) selection, the percentage of patients assigned to the MTD, over-dosing, under-dosing, and the trial dose-limiting toxicity (DLT) rate, of eleven rule-based and model-based Phase I oncology designs that target or pre-specify a DLT rate of ∼0.2, for three sets of true DLT probabilities. These DLT probabilities are generated at common dosages from specific linear, logistic, and log-logistic dose-toxicity curves. We find that all the designs examined select the MTD much more accurately when there is a clear separation between the true DLT rate at the MTD and the rates at the dose level immediately above and below it, such as for the DLT rates generated using the chosen logistic dose-toxicity curve; the separations in these true DLT rates depend, in turn, not only on the functional form of the dose-toxicity curve but also on the investigated dose levels and the parameter set-up. The model based mTPI, TEQR, BOIN, CRM and EWOC designs perform well and assign the greatest percentages of patients to the MTD, and also have a reasonably high probability of picking the true MTD across the three dose-toxicity curves examined. Among the rule-based designs studied, the 5 + 5 a design picks the MTD as accurately as the model based designs for the true DLT rates generated using the chosen log-logistic and linear dose-toxicity curves, but requires enrolling a higher number of patients than the other designs. We also find that it is critical to pick a design that is aligned with the true DLT rate of interest. Further, we note that Phase I trials are very small in general and hence may not provide accurate estimates of the MTD. Thus our work provides a map for planning Phase I oncology trials or developing new ones.

The Add-Arm Design for Unimodal Response Curve with Unknown Mode.

In a classical drop-loser (or drop-arm) design, patients are randomized into all arms (doses) and at the interim analysis, inferior arms are dropped. Therefore, compared to the traditional dose-finding design, this adaptive design can reduce the sample size by not carrying over all doses to the end of the trial or dropping the losers earlier. However, all the doses have to be explored. For unimodal (including linear or umbrella) response curves, we proposed an effective dose-finding design that allows adding arms at the interim analysis. The trial design starts with two arms, depending on the response of the two arms and the unimodality assumption; we can decide which new arms to be added. This design does not require exploring all arms (doses) to find the best responsive dose; therefore, it can further reduce the sample size from the drop-loser design by as much as 10–20%.

Unified additional requirement in consideration of regional approval for multiregional clinical trials

ABSTRACT To speed up the process of bringing a new drug to the market, more and more clinical trials are being conducted simultaneously in multiple regions. After demonstrating the overall drug’s efficacy across regions, the regulatory and drug sponsor may also want to assess the drug’s effect in specific region(s). Most of the recent approaches imposed a uniform criterion to assess the consistency of treatment effects between the interested region(s) and the entire study population regardless of the number of regions in multiregional clinical trials (MRCT). As a result, the needed sample size to achieve the desired probability of satisfying the regional requirement could be huge and implausible for the trial sponsors to implement. In this paper, we propose a unified additional requirement for regional approval by differing the parameters in the additional requirement depending on the number of planned regions. In particular, the values of the parameters are determined by a reasonable sample size increase with the desired probability satisfying the additional requirement. Considering the practicality of the global trial or sample size increase, we recommend specific values of the parameters for a different number of planned regions. We also introduce the assurance probability curve to evaluate the performance of different regional requirements.

Example-based illustrations of design, conduct, analysis and result interpretation of multi-regional clinical trials

Extensive research has been conducted in the Multi-Regional Clinical Trial (MRCT) area. To effectively apply an appropriate approach to a MRCT, we need to synthesize and understand the features of different approaches. In this paper, examples are used to illustrate considerations regarding design, conduct, analysis and interpretation of result of MRCTs. We start with a brief discussion of region definitions and the scenarios where different regions have differing requirements for a MRCT. We then compare different designs and models as well as the corresponding interpretation of the results. We highlight the importance of paying special attention to trial monitoring and conduct to prevent potential issues associated with the final trial results. Besides evaluating the overall treatment effect for the entire MRCT, we also consider other key analyses including quantification of regional treatment effects within a MRCT, and assessment of consistency of these regional treatment effects.

Biomarker informed add-arm design for unimodal response

ABSTRACT In this article, we propose a biomarker informed add-arm design for unimodal response. The new design contributes to optimizing the procedure of dose-finding when a biomarker of the study primary endpoint exists and prior evidence indicates a unimodal dose–response relationship. Designs with up to seven active treatment arms were considered. We propose the statistical approach for the Type I error control and carry out extensive simulation studies for the power performance of the design. The proposed design is shown to outperform the corresponding biomarker informed two-stage winner design in power on an average.

Interpreting the Regulatory Perspective on Adaptive Designs

ABSTRACT The Food and Drug Administration (FDA) Center for Drug Evaluation and Research (CDER) and Center for Biologics Evaluation and Research (CBER) released a draft guidance (DG) on adaptive clinical trials (ACT) for drugs and biologics in February, 2010. In May, 2016, FDA Center for Devices and Radiological Heath (CDRH) and CBER issued the final guidance (FG) on adaptive medical device trials. The purpose of the FG is to provide clarity on how to plan and implement adaptive designs (AD) for clinical studies used in medical device development and to further encourage companies to use AD. While both the device FG and drug and biologics DG provided positive review of ACT, the FG position was stronger, stating that the FDA centers “further encourage companies to consider the use of AD in their clinical trials.” Both guidances emphasize the importance of preplanning to avoid Type I error inflation, strict following of the plan to minimize operational bias, and frequent and early interactions with the FDA to ensure the success of the planned ACT. Both guidances emphasize the utilities of clinical trial simulations in design of ACT and in analysis of adaptive trial data. In this article, we present our understanding the guidances.

What Constitutes Science and Scientific Evidence: Roles of Null Hypothesis Testing.

We briefly discuss the philosophical basis of science, causality, and scientific evidence, by introducing the hidden but most fundamental principle of science: the similarity principle. The principle’s use in scientific discovery is illustrated with Simpson’s paradox and other examples. In discussing the value of null hypothesis statistical testing, the controversies in multiple regression, and multiplicity issues in statistics, we describe how these difficult issues should be handled based on our interpretation of the similarity principle.

iWordNet: A New Approach to Cognitive Science and Artificial Intelligence

One of the main challenges in artificial intelligence or computational linguistics is understanding the meaning of a word or concept. We argue that the connotation of the term “understanding,” or the meaning of the word “meaning,” is merely a word mapping game due to unavoidable circular definitions. These circular definitions arise when an individual defines a concept, the concepts in its definition, and so on, eventually forming a personalized network of concepts, which we call an iWordNet. Such an iWordNet serves as an external representation of an individual’s knowledge and state of mind at the time of the network construction. As a result, “understanding” and knowledge can be regarded as a calculable statistical property of iWordNet topology. We will discuss the construction and analysis of the iWordNet, as well as the proposed “Path of Understanding” in an iWordNet that characterizes an individual’s understanding of a complex concept such as a written passage. In our pilot study of 20 subjects we used a regression model to demonstrate that the topological properties of an individual’s iWordNet are related to his IQ score, a relationship that suggests iWordNets as a potential new methodology to studying cognitive science and artificial intelligence.

An Adaptive Staggered Dose Design for a Normal Endpoint

In a clinical trial where several doses are compared to a control, a multi-stage design that combines both the selection of the best dose and the confirmation of this selected dose is desirable. An example is the two-stage drop-the-losers or pick-the-winner design, where inferior doses are dropped after interim analysis. Selection of target dose(s) can be based on ranking of observed effects, hypothesis testing with adjustment for multiplicity, or other criteria at interim stages. A number of methods have been proposed and have made significant gains in trial efficiency. However, many of these designs started off with all doses with equal allocation and did not consider prioritizing the doses using existing dose-response information. We propose an adaptive staggered dose procedure that allows explicit prioritization of doses and applies error spending scheme that favors doses with assumed better responses. This design starts off with only a subset of the doses and adaptively adds new doses depending on interim results. Using simulation, we have shown that this design performs better in terms of increased statistical power than the drop-the-losers design given strong prior information of dose response.