Adaptive trial designs

A landscape of methodology and implementation of adaptive designs in cancer clinical trials

BACKGROUND

The use of adaptive designs in cancer trials has considerably increased worldwide in recent years, along with the release of various guidelines for their application. This systematic review aims to comprehensively summarize the key methodological and executive features of adaptive designs in cancer clinical trials.

METHODS

A comprehensive search from PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials was conducted to screen eligible clinical trials that employed adaptive designs and were conducted in cancer patients. The methodological and executive characteristics of adaptive designs were the main measurements extracted. Descriptive analyses, primarily consisting of frequency and percentage, were employed to analyzed and reported the data.

RESULTS

A total of 180 cancer clinical trials with adaptive designs were identified. The first three most common type of adaptive design was the group sequential design (n=114, 63.3 %), adaptive dose-finding design (n=22, 12.2 %), and adaptive platform design (n=16, 8.9 %). The results showed that 4.4 % (n=8) of trials conducted post hoc modifications, and around 29.4 % (n=53) did not provide the methods for controlling type I errors. Among phase II or above trials, 79.9 % (112/140) applied the surrogate endpoint as the primary outcome in these trials. Importantly, 27.2 % (49/180) of trials did not report clear information on the independent data monitoring committee (iDMC), and 13.3 % (n=24) without clear information on interim analyses. Interim analyses suggested 34.4 % (62/180) of trials being stopped for futility, 10.6 % (n=19) for efficacy, and 2.2 % (n=4) for safety concerns in the early stage.

CONCLUSIONS

This study emphasizes adaptive designs in cancer trials face significant challenges in their design or strict implementation according to protocol, which might significantly compromise the validity and integrity of trials. It is thus important for researchers, sponsors, and policymakers to actively oversee and guide their application.

Design of the National Adaptive Trial for PTSD-related Insomnia (NAP Study), VA Cooperative Study Program (CSP) #2016

There are currently no validated pharmacotherapies for posttraumatic stress disorder (PTSD)-related insomnia. The purpose of the National Adaptive Trial for PTSD-Related Insomnia (NAP Study) is to efficiently compare to placebo the effects of three insomnia medications with different mechanisms of action that are already prescribed widely to veterans diagnosed with PTSD within U.S. Department of Veterans Affairs (VA) Medical Centers. This study plans to enroll 1224 patients from 34 VA Medical Centers into a 12- week prospective, randomized placebo-controlled clinical trial comparing trazodone, eszopiclone, and gabapentin. The primary outcome measure is insomnia, assessed with the Insomnia Severity Index. A novel aspect of this study is its adaptive design. At the recruitment midpoint, an interim analysis will be conducted to inform a decision to close recruitment to any "futile" arms (i.e. arms where further recruitment is very unlikely to yield a significant result) while maintaining the overall study recruitment target. This step could result in the enrichment of the remaining study arms, enhancing statistical power for the remaining comparisons to placebo. This study will also explore clinical, actigraphic, and biochemical predictors of treatment response that may guide future biomarker development. Lastly, due to the COVID-19 pandemic, this study will allow the consenting process and follow-up visits to be conducted via video or phone contact if in-person meetings are not possible. Overall, this study aims to identify at least one effective pharmacotherapy for PTSD-related insomnia, and, perhaps, to generate definitive negative data to reduce the use of ineffective insomnia medications. NATIONAL CLINICAL TRIAL (NCT) IDENTIFIED NUMBER: NCT03668041.

Sample-Size Planning for More Accurate Statistical Power: A Method Adjusting Sample Effect Sizes for Publication Bias and Uncertainty

The sample size necessary to obtain a desired level of statistical power depends in part on the population value of the effect size, which is, by definition, unknown. A common approach to sample-size planning uses the sample effect size from a prior study as an estimate of the population value of the effect to be detected in the future study. Although this strategy is intuitively appealing, effect-size estimates, taken at face value, are typically not accurate estimates of the population effect size because of publication bias and uncertainty. We show that the use of this approach often results in underpowered studies, sometimes to an alarming degree. We present an alternative approach that adjusts sample effect sizes for bias and uncertainty, and we demonstrate its effectiveness for several experimental designs. Furthermore, we discuss an open-source R package, BUCSS, and user-friendly Web applications that we have made available to researchers so that they can easily implement our suggested methods.

Perspective: Randomized Controlled Trials Are Not a Panacea for Diet-Related Research

Research into the role of diet in health faces a number of methodologic challenges in the choice of study design, measurement methods, and analytic options. Heavier reliance on randomized controlled trial (RCT) designs is suggested as a way to solve these challenges. We present and discuss 7 inherent and practical considerations with special relevance to RCTs designed to study diet: 1) the need for narrow focus; 2) the choice of subjects and exposures; 3) blinding of the intervention; 4) perceived asymmetry of treatment in relation to need; 5) temporal relations between dietary exposures and putative outcomes; 6) strict adherence to the intervention protocol, despite potential clinical counter-indications; and 7) the need to maintain methodologic rigor, including measuring diet carefully and frequently. Alternatives, including observational studies and adaptive intervention designs, are presented and discussed. Given high noise-to-signal ratios interjected by using inaccurate assessment methods in studies with weak or inappropriate study designs (including RCTs), it is conceivable and indeed likely that effects of diet are underestimated. No matter which designs are used, studies will require continued improvement in the assessment of dietary intake. As technology continues to improve, there is potential for enhanced accuracy and reduced user burden of dietary assessments that are applicable to a wide variety of study designs, including RCTs.

Adding a treatment arm to an ongoing clinical trial: a review of methodology and practice

Incorporating an emerging therapy as a new randomisation arm in a clinical trial that is open to recruitment would be desirable to researchers, regulators and patients to ensure that the trial remains current, new treatments are evaluated as quickly as possible, and the time and cost for determining optimal therapies is minimised. It may take many years to run a clinical trial from concept to reporting within a rapidly changing drug development environment; hence, in order for trials to be most useful to inform policy and practice, it is advantageous for them to be able to adapt to emerging therapeutic developments. This paper reports a comprehensive literature review on methodologies for, and practical examples of, amending an ongoing clinical trial by adding a new treatment arm. Relevant methodological literature describing statistical considerations required when making this specific type of amendment is identified, and the key statistical concepts when planning the addition of a new treatment arm are extracted, assessed and summarised. For completeness, this includes an assessment of statistical recommendations within general adaptive design guidance documents. Examples of confirmatory ongoing trials designed within the frequentist framework that have added an arm in practice are reported; and the details of the amendment are reviewed. An assessment is made as to how well the relevant statistical considerations were addressed in practice, and the related implications. The literature review confirmed that there is currently no clear methodological guidance on this topic, but that guidance would be advantageous to help this efficient design amendment to be used more frequently and appropriately in practice. Eight confirmatory trials were identified to have added a treatment arm, suggesting that trials can benefit from this amendment and that it can be practically feasible; however, the trials were not always able to address the key statistical considerations, often leading to uninterpretable or invalid outcomes. If the statistical concepts identified within this review are considered and addressed during the design of a trial amendment, it is possible to effectively assess a new treatment arm within an ongoing trial without compromising the original trial outcomes.

Adaptive designs for comparative effectiveness research trials

CONTEXT

Medical and health policy decision makers require improved design and analysis methods for comparative effectiveness research (CER) trials. In CER trials, there may be limited information to guide initial design choices. In general settings, adaptive designs (ADs) have effectively overcome limits on initial information. However, CER trials have fundamental differences from standard clinical trials including population heterogeneity and a vaguer concept of a "minimum clinically meaningful difference".

OBJECTIVE

To explore the use of a particular form of ADs for comparing treatments within the CER trial context.

METHODS

We review the current state of clinical CER, identify areas of CER as particularly strong candidates for application of novel ADs, and illustrate potential usefulness of the designs and methods for two group comparisons.

RESULTS

ADs can stabilize power. The designs ensure adequate power for true effects are at least at clinically significant preplanned effect size, or when variability is larger than expected. The designs allow for sample size savings when the true effect is larger or when variability is smaller than planned.

CONCLUSION

ADs in CER have great potential to allow trials to successfully and efficiently make important comparisons.

Cardiovascular drug discovery: a perspective from a research-based pharmaceutical company

The theme of this review is to summarize the evolving processes in cardiovascular drug discovery and development within a large pharmaceutical company. Emphasis is placed on the contrast between the academic and industrial research operating environments, which can influence the effectiveness of research collaboration between the two constituencies, but which plays such an important role in drug innovation. The strategic challenges that research directors face are also emphasized. The need for improved therapy in many cardiovascular indications remains high, but the feasibility in making progress, despite the advances in molecular biology and genomics, is also assessed.

Quantitative Extrapolation: An Approach to Validation of Adult Drug Efficacy in Pediatric Subjects

Confirmation of efficacy in pediatric drug development has traditionally required large, fully powered efficacy studies that have proven to have major feasibility and ethical challenges. Extrapolation of efficacy in the framework provided by the US Food and Drug Administration and European Medicines Agency is an appropriate solution when there is similarity of disease. When there is uncertainty regarding the degree of disease similarity, partial extrapolation may be utilized. The authors propose a more quantitative approach to partial extrapolation (ie, quantitative extrapolation), involving (1) integration of adult pharmacokinetic (PK), pharmacodynamic (PD), and clinical outcome data using pharmacometric models, (2) extrapolation using the adult pharmacometric model to predict PD and efficacy outcomes in pediatric subjects, and (3) validation of pediatric predictions with a streamlined plan of pediatric trials (ie, a quantitative extrapolation plan). A case study is presented for quantitative extrapolation using dipeptidyl peptidase 4 (DPP-4) inhibitors. In this example, the authors demonstrate how adult PK, PD, and HbA1c data can be integrated using a pharmacometric model for DPP-4 inhibitors with pediatric dose selection and efficacy validated with relatively few pediatric subjects.

Research practices that can prevent an inflation of false-positive rates

Recent studies have indicated that research practices in psychology may be susceptible to factors that increase false-positive rates, raising concerns about the possible prevalence of false-positive findings. The present article discusses several practices that may run counter to the inflation of false-positive rates. Taking these practices into account would lead to a more balanced view on the false-positive issue. Specifically, we argue that an inflation of false-positive rates would diminish, sometimes to a substantial degree, when researchers (a) have explicit a priori theoretical hypotheses, (b) include multiple replication studies in a single paper, and (c) collect additional data based on observed results. We report findings from simulation studies and statistical evidence that support these arguments. Being aware of these preventive factors allows researchers not to overestimate the pervasiveness of false-positives in psychology and to gauge the susceptibility of a paper to possible false-positives in practical and fair ways.

Exploiting the cancer genome: strategies for the discovery and clinical development of targeted molecular therapeutics

Our biological understanding of the molecular basis of cancer has benefited from advances in basic research, accelerated recently by cancer genome sequencing and other high-throughput, genome-wide profiling technologies. Given the diverse heterogeneity among tumors, the traditional cytotoxic chemotherapy and one-size-fits-all approaches to cancer discovery and development are not appropriate for molecularly targeted agents. Selection of new drug targets is based on achieving cancer selectivity through exploiting specific dependencies and vulnerabilities predicted from tumor genetics. Discovery of highly target-selective agents is enhanced by integrating multiple modern technologies, particularly structure-based design. Efficient clinical evaluation requires smart, hypothesis-testing studies using validated pharmacodynamic and predictive biomarkers. We discuss and exemplify biomarker-driven clinical development and the concept of the Pharmacologic Audit Trail. We detail the exciting approaches offered by drugging the cancer genome, focusing on blocking oncogene addiction, drugging the oncogenic lipid kinome, addressing nononcogene addiction, exploiting synthetic lethality, and overcoming apoptotic resistance, leading to personalized molecular medicine.