Decentralized Clinical Trials: Scientific Considerations Through the Lens of the Estimand Framework.

While industry and regulators' interest in decentralized clinical trials (DCTs) is long-standing, the Covid-19 pandemic accelerated and broadened the adoption and experience with these trials. The key idea in decentralization is bringing the clinical trial design, typically on-site, closer to the patient's experience (on-site or off-site). Thus, potential benefits of DCTs include reducing the burden of participation in trials, broadening access to a more diverse population, or using innovative endpoints collected off-site. This paper helps researchers to carefully evaluate the added value and the implications of DCTs beyond the operational aspects of their implementation. The proposed approach is to use the ICH E9(R1) estimand framework to guide the strategic decisions around each decentralization component. Furthermore, the framework can guide the process for clinical trialists to systematically consider the implications of decentralization, in turn, for each attribute of the estimand. We illustrate the use of this approach with a fully DCT case study and show that the proposed systematic process can uncover the scientific opportunities, assumptions, and potential risks associated with a possible use of decentralization components in the design of a trial. This process can also highlight the benefits of specifying estimand attributes in a granular way. Thus, we demonstrate that bringing a decentralization component into the design will not only impact estimators and estimation but can also correspond to addressing more granular questions, thereby uncovering new target estimands.

Methodology for Good Machine Learning with Multi-Omics Data.

In 2020, Novartis Pharmaceuticals Corporation and the U.S. Food and Drug Administration (FDA) started a 4-year scientific collaboration to approach complex new data modalities and advanced analytics. The scientific question was to find novel radio-genomics-based prognostic and predictive factors for HR+/HER- metastatic breast cancer under a Research Collaboration Agreement. This collaboration has been providing valuable insights to help successfully implement future scientific projects, particularly using artificial intelligence and machine learning. This tutorial aims to provide tangible guidelines for a multi-omics project that includes multidisciplinary expert teams, spanning across different institutions. We cover key ideas, such as "maintaining effective communication" and "following good data science practices," followed by the four steps of exploratory projects, namely (1) plan, (2) design, (3) develop, and (4) disseminate. We break each step into smaller concepts with strategies for implementation and provide illustrations from our collaboration to further give the readers actionable guidance.

The Predictive Individual Effect for Survival Data.

BACKGROUND: The call for patient-focused drug development is loud and clear, as expressed in the twenty-first Century Cures Act and in recent guidelines and initiatives of regulatory agencies. Among the factors contributing to modernized drug development and improved health-care activities are easily interpretable measures of clinical benefit. In addition, special care is needed for cancer trials with time-to-event endpoints if the treatment effect is not constant over time. OBJECTIVE: To quantify the potential clinical survival benefit for a new patient, would he/she be treated with the test or control treatment. METHODS: We propose the predictive individual effect which is a patient-centric and tangible measure of clinical benefit under a wide variety of scenarios. It can be obtained by standard predictive calculations under a rank preservation assumption that has been used previously in trials with treatment switching. RESULTS: We discuss four recent Oncology trials that cover situations with proportional as well as non-proportional hazards (delayed treatment effect or crossing of survival curves). It is shown that the predictive individual effect offers valuable insights beyond p-values, estimates of hazard ratios or differences in median survival. CONCLUSION: Compared to standard statistical measures, the predictive individual effect is a direct, easily interpretable measure of clinical benefit. It facilitates communication among clinicians, patients, and other parties and should therefore be considered in addition to standard statistical results.

Challenges in Assessing the Impact of the COVID-19 Pandemic on the Integrity and Interpretability of Clinical Trials.

Abstract-The COVID-19 pandemic has a global impact on the conduct of clinical trials of medical products. This article discusses implications of the COVID-19 pandemic on clinical research methodology aspects and provides points to consider to assess and mitigate the risk of seriously compromising the integrity and interpretability of clinical trials. The information in this article will support discussions that need to occur cross-functionally on an ongoing basis to "integrate all available knowledge from the ethical, the medical, and the methodological perspective into decision making." This article aims at facilitating: (i) risk assessments of the impact of the pandemic on trial integrity and interpretability; (ii) identification of the relevant data and information related to the impact of the pandemic on the trial that needs to be collected; (iii) short-term decision making impacting ongoing trial operations; (iv) ongoing monitoring of the trial conduct until completion, including the possible involvement of data monitoring committees, and adequately documenting all measures taken to secure trial integrity throughout and after the pandemic, and (v) proper analysis and interpretation of the eventual interim or final trial data.

Estimands and the Patient Journey: Addressing the Right Question in Oncology Clinical Trials.

The diversity of patient journeys can raise fundamental questions regarding the evaluation of drug effects in clinical trials to inform clinical practice. When defining the treatment effect of interest in a trial, the researcher needs to account for events occurring after treatment initiation, such as the start of a new therapy, before observing the end point. We review the newly introduced estimand framework to structure discussions on the relationship between patient journeys and the treatment effect of interest in oncology trials. In 2017, the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use released a draft addendum to its E9 guideline. The addendum introduces the concept of an estimand to precisely describe the treatment effect of interest. This estimand framework provides a structured approach to discuss how to account for intercurrent events that occur after random assignment and may affect the assessment or interpretation of the treatment effect. The framework is expected to improve coherence between trial objectives, design, analysis, and interpretation, as illustrated by examples in oncology disease settings. The estimand framework was applied to design a trial for a chimeric antigen receptor T-cell therapy. The treatment effect of interest was carefully defined considering the range of patient journeys expected for this particular indication and treatment. The trial design was developed accordingly to assess that treatment effect. All parties involved in the design of clinical trials need to consider possible patient journeys to define appropriate treatment effects and corresponding trial designs and analysis strategies. The estimand framework provides a common language to address the complexity introduced by varied patient journeys.

A proof of concept phase II non-inferiority criterion.

Traditional phase III non-inferiority trials require compelling evidence that the treatment vs control effect bfθ is better than a pre-specified non-inferiority margin θ(NI) . The standard approach compares this margin to the 95 per cent confidence interval of the effect parameter. In the phase II setting, in order to declare Proof of Concept (PoC) for non-inferiority and proceed in the development of the drug, different criteria that are specifically tailored toward company internal decision making may be more appropriate. For example, less evidence may be needed as long as the effect estimate is reasonably convincing. We propose a non-inferiority design that addresses the specifics of the phase II setting. The requirements are that (1) the effect estimate be better than a critical threshold θ(C), and (2) the type I error with regard to θ(NI) is controlled at a pre-specified level. This design is compared with the traditional design from a frequentist as well as a Bayesian perspective, where the latter relies on the Level of Proof (LoP) metric, i.e. the probability that the true effect is better than effect values of interest. Clinical input is required to establish the value θ(C), which makes the design transparent and improves interactions within clinical teams. The proposed design is illustrated for a non-inferiority trial for a time-to-event endpoint in oncology.

Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology.

The ability to select a sensitive patient population may be crucial for the development of a targeted therapy. Identifying such a population with an acceptable level of confidence may lead to an inflation in development time and cost. We present an approach that allows to decrease these costs and to increase the reliability of the population selection. It is based on an actual adaptive phase II/III design and uses Bayesian decision tools to select the population of interest at an interim analysis. The primary endpoint is assumed to be the time to some event like e.g. progression. It is shown that the use of appropriately stratified logrank tests in the adaptive test procedure guarantees overall type I error control also when using information on patients that are censored at the adaptive interim analysis. The use of Bayesian decision tools for the population selection decision making is discussed. Simulations are presented to illustrate the operating characteristics of the study design relative to a more traditional development approach. Estimation of treatment effects is considered as well.

Docetaxel 75 mg/m(2) is active and well tolerated in patients with metastatic or recurrent gastric cancer: a phase II trial.

OBJECTIVE: The aim of the present study was to confirm the efficacy and tolerability of docetaxel 75 mg/m(2) in a population of Korean patients with advanced gastric cancer. METHODS: Patients with metastatic or locally recurrent gastric cancer received docetaxel 75 mg/m(2) by intravenous infusion every 3 weeks. Objective response rate was the primary endpoint. RESULTS: Forty-five patients were enrolled. Most showed adenocarcinomas of the gastric antrum and/or body of the stomach. All showed metastases and two-thirds retained the primary tumour. Forty-four patients received at least one docetaxel infusion ('treated' population), with 40 patients evaluable for response. A total of 159 cycles (median three cycles) were administered, with mean duration of treatment 10.9 weeks. The objective response rate in the treated population was 15.9% (17.5% in the per protocol population), with stable disease in 25.0% of patients and progressive disease in 50.0%. Grade 3-4 neutropenia occurred in 36 (81.8%) patients and 36.1% of cycles. However, febrile neutropenia occurred in only two (4.5%) patients and 1.3% of cycles. Grade 3 anorexia, experienced by two patients (4.5%) and during 1.9% of cycles, was the most frequent non-haematological adverse event possibly or probably related to docetaxel. No grade 4 non-haematological events occurred. CONCLUSION: This study suggests that docetaxel 75 mg/m(2) is active in metastatic or locally recurrent adenocarcinoma with a low incidence of grade 3-4 adverse events. Docetaxel warrants further study in combination regimens for advanced gastric cancer.