Family members and friends play an important supportive role in the management of chronic illnesses like diabetes, which often require substantial lifestyle changes. Some studies suggest that there ...may be racial differences in the kinds of support people receive, though little research has examined this idea within a chronic illness context. The current research takes a qualitative approach to examining similarities and differences between Black and White individuals with type 2 diabetes in the dimensions of support received from their family members, with a particular focus on better understanding more intrusive forms of support, such as unsolicited and overprotective support. Semi-structured interviews were conducted (N = 32) to characterize differences in support received by Black and White individuals with type 2 diabetes. The results of the thematic analysis suggested that unsolicited and overprotective support were not universally perceived to be negative, as previous work on White populations seemed to suggest. Rather, if the support provided was perceived as inhibiting autonomy, it was generally undesired by participants from both racial groups-however, for Black participants, knowing that the support was provided out of love could make it more acceptable. The analysis also revealed several underexplored dimensions of received support, including the directiveness of support and the tone used to deliver support. The current study provides an initial step towards grounding social support theory in the experiences of marginalized populations and will inform further development of a culturally sensitive measure of social support for individuals with chronic illness.
Effective and tolerable treatments are needed for older patients with classical Hodgkin lymphoma. We report results for older patients with classical Hodgkin lymphoma treated in the large phase III ...ECHELON-1 study of frontline brentuximab vedotin plus doxorubicin, vinblastine, and dacarbazine (A+AVD) versus doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD). Modified progression-free survival per independent review facility for older versus younger patients (aged ≥60 vs. <60 years) was a pre-specified subgroup analysis; as the ECHELON- 1 study was not powered for these analyses, reported P-values are descriptive. Of 1,334 enrolled patients, 186 (14%) were aged ≥60 years (A+AVD: n=84, ABVD: n=102); results below refer to this age group. Modified progression-free survival per independent review facility was similar in the two arms at 24 months (A+AVD: 70.3% 95% confidence interval (CI): 58.4-79.4, ABVD: 71.4% 95% CI: 60.5-79.8, hazard ratio (HR)=1.00 95% CI: 0.58-1.72, P=0.993). After a median follow-up of 60.9 months, 5-year progression-free survival per investigator was 67.1% with A+AVD versus 61.6% with ABVD (HR=0.820 95% CI: 0.494-1.362, P=0.443). Comparing A+AVD versus ABVD, grade 3/4 peripheral neuropathy occurred in 18% versus 3%; any-grade febrile neutropenia in 37% versus 17%; and any-grade pulmonary toxicity in 2% versus 13%, respectively, with three (3%) pulmonary toxicity-related deaths in patients receiving ABVD (none in those receiving A+AVD). Altogether, A+AVD showed overall similar efficacy to ABVD with survival rates in both arms comparing favorably to those of prior series in older patients with advanced-stage classical Hodgkin lymphoma. Compared to ABVD, A+AVD was associated with higher rates of neuropathy and neutropenia, but lower rates of pulmonary-related toxicity. Trials registered at ClinicalTrials.gov identifiers: NCT01712490; EudraCT number: 2011-005450-60.
Over the past decade, artificial intelligence (AI) and machine learning (ML) have become the breakthrough technology most anticipated to have a transformative effect on pharmaceutical research and ...development (R&D). This is partially driven by revolutionary advances in computational technology and the parallel dissipation of previous constraints to the collection/processing of large volumes of data. Meanwhile, the cost of bringing new drugs to market and to patients has become prohibitively expensive. Recognizing these headwinds, AI/ML techniques are appealing to the pharmaceutical industry due to their automated nature, predictive capabilities, and the consequent expected increase in efficiency. ML approaches have been used in drug discovery over the past 15–20 years with increasing sophistication. The most recent aspect of drug development where positive disruption from AI/ML is starting to occur, is in clinical trial design, conduct, and analysis. The COVID-19 pandemic may further accelerate utilization of AI/ML in clinical trials due to an increased reliance on digital technology in clinical trial conduct. As we move towards a world where there is a growing integration of AI/ML into R&D, it is critical to get past the related buzz-words and noise. It is equally important to recognize that the scientific method is not obsolete when making inferences about data. Doing so will help in separating hope from hype and lead to informed decision-making on the optimal use of AI/ML in drug development. This manuscript aims to demystify key concepts, present use-cases and finally offer insights and a balanced view on the optimal use of AI/ML methods in R&D.
Graphical abstract
As the roles of historical trials and real‐world evidence in drug development have substantially increased, several approaches have been proposed to leverage external data and improve the design of ...clinical trials. While most of these approaches focus on methodology development for borrowing information during the analysis stage, there is a risk of inadequate or absent enrollment of concurrent control due to misspecification of heterogeneity from external data, which can result in unreliable estimates of treatment effect. In this study, we introduce a Bayesian hybrid design with flexible sample size adaptation (BEATS) that allows for adaptive borrowing of external data based on the level of heterogeneity to augment the control arm during both the design and interim analysis stages. Moreover, BEATS extends the Bayesian semiparametric meta‐analytic predictive prior (BaSe‐MAP) to incorporate time‐to‐event endpoints, enabling optimal borrowing performance. Initially, BEATS calibrates the expected sample size and initial randomization ratio based on heterogeneity among the external data. During the interim analysis, flexible sample size adaptation is performed to address conflicts between the concurrent and historical control, while also conducting futility analysis. At the final analysis, estimation is provided by incorporating the calibrated amount of external data. Therefore, our proposed design allows for an approximation of an ideal randomized controlled trial with an equal randomization ratio while controlling the size of the concurrent control to benefit patients and accelerate drug development. BEATS also offers optimal power and robust estimation through flexible sample size adaptation when conflicts arise between the concurrent control and external data.
In modern oncology drug development, adaptive designs have been proposed to identify the recommended phase 2 dose. The conventional dose finding designs focus on the identification of maximum ...tolerated dose (MTD). However, designs ignoring efficacy could put patients under risk by pushing to the MTD. Especially in immuno‐oncology and cell therapy, the complex dose‐toxicity and dose‐efficacy relationships make such MTD driven designs more questionable. Additionally, it is not uncommon to have data available from other studies that target on similar mechanism of action and patient population. Due to the high variability from phase I trial, it is beneficial to borrow historical study information into the design when available. This will help to increase the model efficiency and accuracy and provide dose specific recommendation rules to avoid toxic dose level and increase the chance of patient allocation at potential efficacious dose levels. In this paper, we propose iBOIN‐ET design that uses prior distribution extracted from historical studies to minimize the probability of decision error. The proposed design utilizes the concept of skeleton from both toxicity and efficacy data, coupled with prior effective sample size to control the amount of historical information to be incorporated. Extensive simulation studies across a variety of realistic settings are reported including a comparison of iBOIN‐ET design to other model based and assisted approaches. The proposed novel design demonstrates the superior performances in percentage of selecting the correct optimal dose (OD), average number of patients allocated to the correct OD, and overdosing control during dose escalation process.
Immuno-oncology (IO) and cell therapy, the frontier of cancer treatment, is a rapidly developing area that brings new opportunities to patients. In IO and cell therapy clinical trial development, it ...is critical to identify the right dose level in early phase of trials thus improving the probability of success in confirmatory trials to test the superiority over other therapies. Given the complex mechanism interacting with immune system for IO drugs especially cell therapy, the traditional oncology dose finding trial designs may not serve the purpose. Specifically, it is questionable to believe the monotone relationship between dose level and safety/efficacy, which will likely result in inappropriate dose selection using designs with monotone assumption. Additionally, considering the immune system pathway, designs ignoring the heterogeneity of the patient populations may provide misleading dose decisions, which could be either unsafe or lead to selection mistakes for targeted population. Therefore, in our paper, we review and present the limitations of the traditional dose finding designs. Then we discuss improved dose finding designs that consider both safety and efficacy outcomes simultaneously. Furthermore, we propose novel dose finding designs for multiple populations: BNP-mTPI and fBNP-mTPI, which extend the modified toxicity probability interval designs by utilizing Bayesian non-parametric priors. The proposed designs consider patient population differences meanwhile flexibly borrowing information across populations with similar profiles to improve the efficiency of dose search and accuracy of estimation of optimal dose level. Simulations are provided to demonstrate the model performance. Finally, we conclude the recommendations for IO and cell therapy dose finding designs in the discussion and offer insights for future research direction.
Combination therapy, a treatment modality that involves multiple treatment agents, has become imperative for improving treatment effectiveness and addressing resistance in the field of oncology. ...However, determining the most effective dose for these combinations, particularly when dealing with intricate drug interactions and diverse toxicity patterns, presents a substantial challenge. This paper introduces a novel Bayesian
se-finding
esign for
inati
n therapies with information borrowing, named the DOD-Combo design. Leveraging historical single-agent trials and the meta-analytic-predictive (MAP) power prior, our approach utilizes a copula-type model to connect individual drug priors with joint toxicity probabilities in combination treatments. The MAP power prior allows the integration of information from multiple historical trials, constructing informative priors for each agent. Extensive simulations confirm our method's superior performance compared to combination designs with no information borrowing. By adaptively incorporating historical data, our approach reduces sample sizes and enhances efficiency in selecting the maximum tolerated dose (MTD), effectively addressing the intricate challenges presented by combination trials.
Enrolling patients to the standard of care (SOC) arm in randomized clinical trials, especially for rare diseases, can be very challenging due to the lack of resources, restricted patient population ...availability, and ethical considerations. As the therapeutic effect for the SOC is often well documented in historical trials, we propose a Bayesian platform trial design with hybrid control based on the multisource exchangeability modelling (MEM) framework to harness historical control data. The MEM approach provides a computationally efficient method to formally evaluate the exchangeability of study outcomes between different data sources and allows us to make better informed data borrowing decisions based on the exchangeability between historical and concurrent data. We conduct extensive simulation studies to evaluate the proposed hybrid design. We demonstrate the proposed design leads to significant sample size reduction for the internal control arm and borrows more information compared to competing Bayesian approaches when historical and internal data are compatible.
The 2-in-1 design is becoming popular in oncology drug development, with the flexibility in using different endpoints at different decision time. Based on the observed interim data, sponsors can ...choose to seamlessly advance a small phase 2 trial to a full-scale confirmatory phase 3 trial with a pre-determined maximum sample size or remain in a phase 2 trial. While this approach may increase efficiency in drug development, it is rigid and requires a pre-specified fixed sample size. In this paper, we propose a flexible 2-in-1 design with sample size adaptation, while retaining the advantage of allowing an intermediate endpoint for interim decision-making. The proposed design reflects the needs of the recent FDA's Project FrontRunner initiative, which encourages the use of an earlier surrogate endpoint to potentially support accelerated approval with conversion to standard approval with long-term endpoints from the same randomized study. Additionally, we identify the interim decision cut-off to allow a conventional test procedure at the final analysis. Extensive simulation studies showed that the proposed design requires much a smaller sample size and shorter timeline than the simple 2-in-1 design, while achieving similar power. We present a case study in multiple myeloma to demonstrate the benefits of the proposed design.
PDF
