Project description:Detecting safety signals in clinical trial safety data is known to be challenging due to high dimensionality, rare occurrence, weak signal, and complex dependence. We propose a new hierarchical testing approach for analyzing safety data from a typical randomized clinical trial. This approach accounts for the hierarchical structure of adverse events (AEs), that is, AEs are categorized by system organ class (SOC). Our approach contains two steps: the first step tests, for each SOC, whether any AEs within this SOC are differently distributed between treatment arms; and the second step identifies signal AEs from SOCs passing the first step tests. We show the superiority, in terms of power of detecting safety signals given controlled false discovery rate, of the new approach comparing with currently available approaches through simulation studies. We also demonstrate this approach with two real data examples.
Project description:Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative disorder with complex biology and significant clinical heterogeneity. Many preclinical and early phase ALS clinical trials have yielded promising results that could not be replicated in larger phase 3 confirmatory trials. One reason for the lack of reproducibility may be ALS biological and clinical heterogeneity. Therefore, in this review, we explore sources of ALS heterogeneity that may reduce statistical power to evaluate efficacy in ALS trials. We also review efforts to manage clinical heterogeneity, including use of validated disease outcome measures, predictive biomarkers of disease progression, and individual clinical risk stratification. We propose that personalized prognostic models with use of predictive biomarkers may identify patients with ALS for whom a specific therapeutic strategy may be expected to be more successful. Finally, the rapid application of emerging clinical and biomarker strategies may reduce heterogeneity, increase trial efficiency, and, in turn, accelerate ALS drug development.
Project description:While conducting a set of large-scale multi-site pragmatic clinical trials involving high-impact public health issues such as end-stage renal disease, opioid use, and colorectal cancer, there were substantial changes to both policies and guidelines relevant to the trials. These external changes gave rise to unexpected challenges for the trials, including decisions regarding how to respond to new clinical practice guidelines, increased difficulty in implementing trial interventions, achieving separation between treatment groups, and differential responses across sites. In this article, we describe these challenges and the approaches used to address them. When deliberating appropriate action in the face of external changes during a pragmatic clinical trial, we recommend considering the well-being of the participants, clinical equipoise, and the strength and quality of the evidence associated with the change; involving those charged with data and safety monitoring; and where possible, planning for potential external changes as the trial is being designed. Any solution must balance the primary obligation to protect the well-being of participants with the secondary obligation to protect the integrity of the trial in order to gain meaningful answers to important public health questions.
Project description:BACKGROUND:The importance of randomization in clinical trials has long been acknowledged for avoiding selection bias. Yet, bias concerns re-emerge with selective attrition. This study takes a causal inference perspective in addressing distinct scenarios of missing outcome data (MCAR, MAR and MNAR). METHODS:This study adopts a causal inference perspective in providing an overview of empirical strategies to estimate the average treatment effect, improve precision of the estimator, and to test whether the underlying identifying assumptions hold. We propose to use Random Forest Lee Bounds (RFLB) to address selective attrition and to obtain more precise average treatment effect intervals. RESULTS:When assuming MCAR or MAR, the often untenable identifying assumptions with respect to causal inference can hardly be verified empirically. Instead, missing outcome data in clinical trials should be considered as potentially non-random unobserved events (i.e. MNAR). Using simulated attrition data, we show how average treatment effect intervals can be tightened considerably using RFLB, by exploiting both continuous and discrete attrition predictor variables. CONCLUSIONS:Bounding approaches should be used to acknowledge selective attrition in randomized clinical trials in acknowledging the resulting uncertainty with respect to causal inference. As such, Random Forest Lee Bounds estimates are more informative than point estimates obtained assuming MCAR or MAR.
Project description:Clinical trial oversight is a critical element that ensures the protection of research participants and integrity of the data collected. The trial sponsor, a local Institutional Review Board, and independent monitoring committees all contribute with complementary but overlapping responsibilities. Consistency among these groups is essential for the smooth conduct of a clinical trial but may be challenging in resource-limited settings (RLS). Capacity building and training for RLS may improve clinical trials oversight and ultimately medical management. In this article, we review the components necessary for optimal clinical trial oversight and the issues that arise in the RLS, with some suggested strategies for improvement.
Project description:The purpose of early stage clinical trials is to determine the recommended dose and toxicity profile of an investigational agent or multi-drug combination. Molecularly targeted agents (MTAs) and immunotherapies have distinct toxicities from chemotherapies that are often not dose dependent and can lead to chronic and sometimes unpredictable side effects. Therefore utilizing a dose escalation method that has toxicity based endpoints may not be as appropriate for determination of recommended dose, and alternative parameters such as pharmacokinetic or pharmacodynamic outcomes are potentially appealing options. Approaches to enhance safety and optimize dosing include improved preclinical models and assessment, innovative model based design and dose escalation strategies, patient selection, the use of expansion cohorts and extended toxicity assessments. Tailoring the design of phase I trials by adopting new strategies to address the different properties of MTAs is required to enhance the development of these agents. This review will focus on the limitations to safety and dose determination that have occurred in the development of MTAs and immunotherapies. In addition, strategies are proposed to overcome these challenges to develop phase I trials that can more accurately define the recommended dose and identify adverse events.
Project description:Family caregivers are an increasingly diverse group of individuals who provide significant amounts of direct and indirect care for loved ones with long-term chronic illnesses. Caregiver needs are vast, particularly as these relate to the caregiver's quality of life. However, caregivers are often unlikely to address their personal and health-related concerns. Unmet needs combined with the caregiving role often lead to high levels of caregiver anxiety. Unaddressed, this anxiety is likely to result in poor health and low quality of life. Nurses, along with the health care team, are well positioned to assess, monitor, intervene, and reassess anxiety levels in caregivers using standardized screening tools across care settings. This article focuses on the family caregiver anxiety symptom in community-based settings, where health care providers have unique opportunities to detect this symptom in a familiar environment and begin immediate intervention leading to promotion of quality of life for the caregiver and subsequently the care recipient. Additional research efforts should be focused on health care provider goals of care, dyadic assessments, and monitoring of caregiver needs while caring for their loved ones aging in place.
Project description:Clinicians working on first-in-human clinical studies need to be able to judge whether safety signals observed on an investigational drug were more likely to have occurred by chance or to have been caused by the drug. We retrospectively reviewed 84 Novartis studies including 1,234 healthy volunteers receiving placebo to determine the expected incidence of changes in commonly measured laboratory parameters and vital signs, in the absence of any active agent. We calculated the frequency of random incidence of safety signals, focusing on the liver, cardiovascular system, kidney, and pancreas. Using the liver enzyme alanine aminotransferase (ALT) as an example, we illustrate how a predictive model can be used to determine the probability of a given subject to experience an elevation of ALT above the upper limit of the normal range under placebo, conditional on the characteristics of this subject and the study.
Project description:Among the key challenges in Alzheimer's disease drug development is the timely completion of clinical trials. Unfortunately, clinical trials often suffer from slow or insufficient enrollment. Successful clinical trial recruitment describes a balance between expeditiously achieving full enrollment and ensuring an appropriate study sample. Investigators face a number of challenges to the successful negotiation of this balance. The failure to address these challenges means that drug development may take more time and money and that trial results may not adequately represent drug efficacy or may not be applicable beyond the study. We review the challenges to recruitment and retention in Alzheimer's disease clinical trials and present a framework to address them.
Project description:BackgroundIreland's Model of Care for the Management of Overweight and Obesity outlines a plan for treating adolescent and child obesity (CO). However, engagement with key stakeholders is required to support its implementation and improve health services.AimThis study aims to map the perceived barriers and facilitators related to CO management across healthcare settings, professional disciplines, and regions in the Republic of Ireland (ROI).Materials and methodsAn online cross-sectional survey of registered healthcare professionals (HPs), designed to adhere to the Consolidated Framework for Implementation Research (CFIR), was co-developed by a project team consisting of researchers, healthcare professionals, and patient advocates. The survey was pilot tested with project stakeholders and distributed online to professional groups and via a social media campaign, between September 2021 and May 2022, using "SurveyMonkey." Data were summarised using descriptive statistics and thematic analyses. Themes were mapped to the CFIR framework to identify the type of implementation gaps that exist for treating obesity within the current health and social care system.ResultsA total of 184 HPs completed the survey including nurses (18%), physicians (14%), health and social care professionals (60%), and other HPs (8%). The majority were female (91%), among which 54% reported conducting growth monitoring with a third (32.6%) giving a diagnosis of paediatric/adolescent obesity as part of their clinical practice. Nearly half (49%) of the HPs reported having the resources needed for clinical assessment. However, 31.5% of the HPs reported having enough "time," and almost 10% of the HPs reported having no/limited access to suitable anthropometric measurement tools. Most HPs did not conduct obesity-related clinical assessments beyond growth assessment, and 61% reported having no paediatric obesity training. CFIR mapping identified several facilitators and barriers including time for clinical encounters, suitable materials and equipment, adequate training, perceived professional competency and self-efficacy, human equality and child-centredness, relative priorities, local attitudes, referral protocols, and long waiting times.ConclusionsThe findings provide actionable information to guide the implementation of the Model of Care for the Management of Overweight and Obesity in Ireland. Survey findings will now inform a qualitative study to explore implementation barriers and facilitators and prioritise actions to improve child and adolescent obesity management.