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The majority of companies and agencies believe that the quality of decision making can and should be measured. Moreover, organizations considered the occurrence of biases within their organization as pertinent. Finally, almost all the participants felt that there was room for improvement for their organization’s quality of decision making. CONCLUSION These findings are consistent with a published study on regulatory processes and support the need for more consistent and predictable decision-making processes during the life cycle of medicines. This could be achieved through capacity building, systematically evaluating the quality of decision making, and encouraging utilization of formal decision-making frameworks within companies and agencies.Masking (or blinding) of treatment assignment is routinely implemented in classical randomized clinical trials (RCTs) to isolate the effect of the intervention itself and to minimize the potential for bias that could occur with traditional trials. Such biases could be introduced with the conduct, assessment of endpoints, management of conditions, analysis, and reporting when the treatment assignments are known. However, masking of treatments is not only complex but it hinders how generalizable the findings are to the “real world” clinical setting. Pragmatic RCTs (pRCTs) are intended to evaluate the effects of interventions within routine medical care, and as such, do not typically mask treatment groups; moreover, pRCTs assess comparators that are available in routine medical practice, not masked placebos. Whether pRCTs should be masked if intended for regulatory or other purposes has recently been questioned. The literature on pRCTs, while extensive, does not address how much actual benefit is gained from masking outcomes and how masking may affect the “real world” nature of a study. Here, we propose an approach to evaluate sources of bias, describe stakeholders in the conduct of pRCTs who are most likely affected, and offer a framework for considering how masking may be implemented effectively while maintaining generalizability.Alzheimer’s disease (AD) has increasingly been recognized as a huge unmet medical need. Currently, there is no approved drug to cure, prevent, or even slow down the disease. It is imperative to develop disease-modifying treatments for AD to alter the underlying disease progression. This paper reviews the most up-to-date regulatory guidance on how to demonstrate disease modification and provides an overview of available methodologies and applications to clinical trials. The intent is to assist the field with future clinical trials designed to demonstrate disease-modifying effect in AD. The methodologies may be generalizable to broader neurodegenerative diseases.Notwithstanding successful harmonization efforts, the global regulatory framework governing product safety is complex and continually evolving, as evidenced by additional regional guidance and regulations. Selleckchem Vorinostat In this regulatory review, we provide an overview from both global and regional perspectives. A historical perspective, with a focus on recent developments, enables identification of important long-term trends, such as a shift from single-case medical review of serious adverse events to an interdisciplinary evaluation of aggregate data for the purpose of judging product causality and informing benefit-risk assessments. We will show how these trends lead to opportunities for closer interdisciplinary collaboration, for bridging the gap between preand postmarketing surveillance, and for a more proactive determination of patient populations with a positive benefit-risk profile for product use. We will conclude by pointing to ongoing and future work that seeks to provide specific solutions for ongoing aggregate safety evaluation.BACKGROUND Clinical research awareness, familiarity, referral proclivity, and practice have been assessed to varying degrees among US-based physicians specializing in oncology but very few studies have assessed these attitudes and behaviors among US physicians and nurses outside of oncology. METHODS To address this gap, the Tufts Center for the Study of Drug Development (Tufts CSDD) conducted a study of 589 US-based physicians and 1255 US-based nurses. RESULTS AND CONCLUSIONS US health care providers have very limited exposure to clinical research in medical and nursing school and in professional meetings. Very high percentages of multispecialty nurses and doctors view clinical trials as health care options, are interested in referring their patients into appropriate clinical trials, and are comfortable providing clinical trial information to, and discussing clinical trial opportunities with, their patients. Yet US physicians and nurses refer very small numbers of patients each year largely because of the inability to access clinical trial information, and the lack of sufficient information and time to evaluate and confidently discuss clinical trial options with their patients. Several factors are predictors of referral behavior, including proximity to research activity and past involvement in clinical research as an investigator, study coordinator, or study volunteer. The results of this study offer new insights into addressing low referral rates among US health care providers.The draft ICH E9(R1) addendum stipulates that an estimator should align with its associated estimand and yield an estimate that facilitates reliable interpretations. The addendum further stipulates that assumptions should be justifiable and plausible, and that the extent of assumptions is an important consideration for whether an estimate will be robust because assumptions are often unverifiable. The draft addendum specifies 5 strategies for dealing with intercurrent events. The intent of this paper is to provide conceptual considerations and technical details for various estimators that align with these strategies. We include focus on how the nature and extent of assumptions influences the potential robustness of the various estimators. The content reflects the knowledge, experience, and opinions of the Drug Information Association’s Scientific Working Group on Missing Data. This group includes experienced statisticians from across industry and academia, primarily in the US and European Union.