Bayesian Models and Decision Algorithms for Complex Early Phase Clinical Trials

An early phase clinical trial is the first step in evaluating the effects in humans of a potential new anti-disease agent or combination of agents. Usually called "phase I" or "phase I/II" trials, these experiments typically have the nominal scientific goal of determining an acceptable dose, most often based on adverse event probabilities. This arose from a tradition of phase I trials to evaluate cytotoxic agents for treating cancer, although some methods may be applied in other medical settings, such as treatment of stroke or immunological diseases. Most modern statistical designs for early phase trials include model-based, outcome-adaptive decision rules that choose doses for successive patient cohorts based on data from previous patients in the trial. Such designs have seen limited use in clinical practice, however, due to their complexity, the requirement of intensive, computer-based data monitoring, and the medical community's resistance to change. Still, many actual applications of model-based outcome-adaptive designs have been remarkably successful in terms of both patient benefit and scientific outcome. In this paper I will review several Bayesian early phase trial designs that were tailored to accommodate specific complexities of the treatment regime and patient outcomes in particular clinical settings.Comment: Published in at http://dx.doi.org/10.1214/09-STS315 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design

Abstract: Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised. This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process. The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text. The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits. In order to encourage its wide dissemination this article is freely accessible on the BMJ and Trials journal websites.“To maximise the benefit to society, you need to not just do research but do it well” Douglas G Altma

Bayesian Design in Clinical Trials

In the last decade, the number of clinical trials using Bayesian methods has grown dramatically. Nowadays, regulatory authorities appear to be more receptive to Bayesian methods than ever. The Bayesian methodology is well suited to address the issues arising in the planning, analysis, and conduct of clinical trials. Due to their flexibility, Bayesian design methods based on the accrued data of ongoing trials have been recommended by both the US Food and Drug Administration and the European Medicines Agency for dose-response trials in early clinical development. A distinctive feature of the Bayesian approach is its ability to deal with external information, such as historical data, findings from previous studies and expert opinions, through prior elicitation. In fact, it provides a framework for embedding and handling the variability of auxiliary information within the planning and analysis of the study. A growing body of literature examines the use of historical data to augment newly collected data, especially in clinical trials where patients are difficult to recruit, which is the case for rare diseases, for example. Many works explore how this can be done properly, since using historical data has been recognized as less controversial than eliciting prior information from experts’ opinions. In this book, applications of Bayesian design in the planning and analysis of clinical trials are introduced, along with methodological contributions to specific topics of Bayesian statistics. Finally, two reviews regarding the state-of-the-art of the Bayesian approach in clinical field trials are presented

A phase I dose-finding and pharmacokinetic study of subcutaneous semisynthetic homoharringtonine (ssHHT) in patients with advanced acute myeloid leukaemia

To determine the maximum-tolerated dose (MTD), dose-limiting toxicities and pharmacokinetic of semisynthetic homoharringtonine (ssHHT), given as a twice daily subcutaneous (s.c.) injections for 9 days, in patients with advanced acute leukaemia, 18 patients with advanced acute myeloid leukaemia were included in this sequential Bayesian phase I dose-finding trial. A starting dose of 0.5 mg m−2 day−1 was explored with subsequent dose escalations of 1, 3, 5 and 6 mg m−2 day−1. Myelosuppression was constant. The MTD was estimated as the dose level of 5 mg m−2 day−1 for 9 consecutive days by s.c. route. Dose-limiting toxicities were hyperglycaemia with hyperosmolar coma at 3 mg m−2, and (i) one anasarque and haematemesis, (ii) one life-threatening pulmonary aspergillosis, (iii) one skin rash and (iv) one scalp pain at dose level of 5 mg m−2 day−1. The mean half-life of ssHHT was 11.01±3.4 h, the volume of distribution at steady state was 2±1.4 l kg−1 and the plasma clearance was 11.6±10.4 l h−1. Eleven of the 12 patients with circulating leukaemic cells had blood blast clearance, two achieved complete remission and one with blast crisis of CMML returned in chronic phase. The recommended daily dose of ssHHT on the 9-day schedule is 5 mg m−2 day−1

Enrollment and Stopping Rules for Managing Toxicity in Phase II Oncology Trials with Delayed Outcome

Stopping rules for toxicity are routinely used in phase II oncology trials. If the follow-up for toxicity is long, it is desirable to have a stopping rule that uses all toxicity information available, not only information from patients with full follow-up. Further, to prevent excessive toxicity in such trials, an enrollment rule is needed. The enrollment rule informs an investigator about the maximum number of patients that can be enrolled depending on the current enrollment and all available information about toxicity. We give recommendations on how to construct Bayesian and frequentist.Doctor of Public Healt

Unified Approaches for Frequentist and Bayesian Methods in Two-Sample Clinical Trials with Binary Endpoints

Two opposing paradigms, analyses via frequentist or Bayesian methods, dominate the statistical literature. Most commonly, frequentist approaches have been used to design and analyze clinical trials, though Bayesian techniques are becoming increasingly popular. However, these two paradigms can generate divergent results even in analyses of the same trial data, which may harm the scientific interpretability of the trial. Therefore, it is crucial to harmonize analyses under each approach. In this dissertation, novel unified approaches for one-sided frequentist and Bayesian hypothesis testing problems comparing two proportions in fixed-sample and group-sequential clinical trials are proposed. When a frequentist design with desired type I and II error rates are given, the unification is achieved by deriving specific Bayesian decision thresholds and sample sizes. Similarly, when a Bayesian design is given, the unification is achieved by deriving corresponding frequentist characteristics. In addition, theoretical methods to determine the Bayesian decision threshold, sample size and power are provided. Numerical results show that the unified approach can yield the same type I and II error rates for frequentist and Bayesian hypothesis tests through a numerical study. Further, detailed evaluations suggest that Bayesian priors specifications, allocation ratios, number of analyses can affect the resulting Bayesian sample sizes and decision thresholds. Overall, the unified approach can be adopted into the current clinical trial setting and is helpful to make trial results translatable between frequentist and Bayesian methods

Adaptive methodologies in multi-arm dose response and biosimilarity clinical trials

As most adaptive clinical trial designs are implemented in stages, well-understood methods of sequential trial monitoring are needed. In the frequentist paradigm, examples of sequential monitoring methodologies include the p-value combination tests, conditional error, conditional power, and alpha spending approaches. Within the Bayesian framework, posterior and predictive probabilities are used as monitoring criteria, with the latter being analogous to the conditional power approach. In a placebo or active-contolled dose response clinical trial, we are interested in achieving two objectives: selecting the best therapeutic dose and confirming this selected dose. Traditional approach uses the parallel group design with Dunnett's adjustment. Recently, some two- stage Seamless II/III designs have been proposed. The drop-the-losers design considers selecting the dose with the highest empirical mean after the first stage, while another design assumes a dose-response model to aid dose selection. These designs however do not consider prioritizing the doses and adaptively inserting new doses. We propose an adaptive staggered dose design for a normal endpoint that makes minimal assumption regarding the dose response and sequentially adds doses to the trial. An alpha spending function is applied in a novel way to monitor the doses across the trial. Through numerical and simulation studies, we confirm that optimistic alpha spending coupled with informative dose ordering jointly produce some desirable operating characteristics when compared to drop-the-losers and model-based Seamless designs. In addition, we show how the design parameters can be flexibly varied to further improve its performance and how it can be extended to binary and survival endpoints. In a biosimilarity trial, we are interested in establishing evidence of comparable efficacy between a follow-on biological product and a reference innovator product. So far, no standard method for biosimilarity has been endorsed by regulatory agency. We propose a Bayesian hierarchical bias model and a non-inferiority hypothesis framework to prove biosimilarity. A two-stage adaptive design using predictive probability as early stopping criterion is pro- posed. Through simulation study, the proposed design controls the type I error better than the frequentist approach and Bayesian power is superior when biosimilarity is plausible. Two-stage design further reduces the expected sample size

The Utility of Adaptive Designs in Publicly Funded Confirmatory Trials

Introduction: Adaptive designs (ADs) are underused, particularly in publicly funded confirmatory trials, despite their promising benefits and methodological prominence given in the statistical literature. Research Question: This thesis investigates why ADs are underused in the publicly funded setting, explores facilitators, and proposes recommendations to improve their appropriate use. Methods: Confirmatory ADs are reviewed from a statistical and practical perspective. Cross-disciplinary key stakeholders are then interviewed to explore roadblocks to the use of ADs. Based on the interview findings, follow-up quantitative surveys are undertaken to explore wider perceptions on barriers, concerns, and facilitators aimed to generalise the findings. The surveys targeted CTUs (Clinical Trials Units), private sector organisations, and Public Funders in the UK. In view of some of the findings, case studies of applied confirmatory ADs are reviewed to highlight their scope and characteristic, and to investigate the state of reporting of the most common AD. The design and implementation of selected ADs is demonstrated using retrospective and prospective planned case studies. Lessons learned are highlighted to enhance the design of future trials of similar characteristics. Results: The main barriers to the use of ADs include the lack of funding support accessible to UK CTUs to aid their design; limited practical knowledge; preference for traditional mainstream designs; difficulties in marketing ADs to key stakeholders; limited time to support ADs relative to other competing priorities; lack of applied training; and insufficient access to case studies of undertaken ADs, which would facilitate practical learning and successful implementation. Researchers’ inadequate description of AD-related aspects (such as rationale, scope, and decision-making criteria to guide the planned AD) in grant proposals was viewed among the major obstacles by Public Funders. Suboptimal reporting of the design and conduct of undertaken ADs appears to influence concerns about their robustness in decision-making and credibility to change practice. Conclusions: Most obstacles appear connected to a lack of practical implementation knowledge and applied training, and limited access to adequately reported case studies to facilitate practical learning. Assurance of scientific rigour through transparent adequate reporting is paramount to the credibility of findings from adaptive trials. There is a need for a consensus guidance document on ADs and an AD-tailored CONSORT statement to enhance their reporting and conduct. This thesis provides detailed recommendations to improve the appropriate use of ADs and areas for future related research

Innovative adaptive designs in oncology clinical trials with drug combinations

The use of drug combinations in clinical trials has emerged during the last years as an alternative to single agent trials since a more favorable therapeutic response may be obtained by combining drugs that, for instance, target multiple pathways or inhibit resistance mechanisms. This practice is common in both early phase and late phase clinical trials. However, depending on the phase of the trial, we may find different challenges that will require novel methodology. In early phase, where we model the probability of toxicity and efficacy, the main challenge is to find a suitable multivariate model that works well with a relatively low sample size. In late phase trials, the main challenge is to propose a design that allows to perfectly control the the type-I error and the power while allowing for the trial to stop in case of a lack of efficacy or in case the interim analyses show an efficacy that is big enough so it would be unethical to continue the trial. Other challenges may involve certain characteristics of the drug, such us delayed effects. This issue is quite present in nowadays clinical research because of the use of immuno-therapy against cancer. In early phase trials, we studied the state of the art methodology and we observed that a large number of published methods are not appropriate for drug combination settings since were originally designed for single agents and then adapted to drug combinations. This statement is not based only on performance, because in fact many of these methods perform quite well even though they were not designed to be used in a drug combination setting, but because most of them do not take into account the interaction between drugs. In late phase trials we focused our attention on the design of clinical trials in the presence of delayed effects in a drug combination setting. We performed a state of the art methodology review, and we observed that there is enough published methodology to design efficient confirmatory trials under this conditions. However, we also observed that most of this methodology primarily focuses on power recovery rather than type-I error rate control, which makes it difficult to apply in practice given the nature of confirmatory trials. Our intention during this thesis was not only to develop novel methodology but to do it in areas that could be of interest for clinicians. In this thesis we make three contributions to the field of clinical trials with drug combinations. In early phase trials, we propose a Bayesian adaptive phase I trial design that allows the investigator to attribute a DLT to one or both agents in a unknown fraction of patients, even when the drugs are given concurrently. We also propose a Bayesian adaptive phase I/II design with drug combinations, a binary endpoint in stage 1, and a TTP endpoint in stage 2, where we aim to identify the dose combination region associated with the highest median TTP among doses along the MTD curve. In late phase trials, we did an assessment of the impact of delayed effects in group sequential and adaptive group sequential designs, with an empirical evaluation in terms of power and type-I error rate of the weighted log-rank in a simulated scenario. Our last contribution includes several practical recommendations regarding which methodology should be used in the presence of delayed effects depending on certain characteristics of the trial