Notes

Comprehending the difficulties associated with prescription antibiotic prescribing behavior throughout severe private hospitals: an organized evaluate as well as meta-ethnography.
Several articles showed that statistical efficiency of multi-arm randomized clinical trials (RCTs) is much better than conventional two-arm RCTs. Multi-arm RCTs attract interest mainly when the experimental treatment regimen is not optimized or several pipelines under development exist. Breast cancer is a possible candidate disease. Our aim was to elucidate the current study designs and multiplicity adjustment methods in multi-arm RCTs.

A search of the PubMed database revealed 468 articles on breast cancer RCTs published from 2010 to 2016. Information on study designs and analysis methods was collected from 4 major journals.

A total of 202 RCTs were selected, 48 were multi-arm and 29 were three-arm RCTs. In two of the target journals, multi-arm RCTs have been increasingly reported since 2013. Compared with two-arm RCTs, three-arm RCTs were frequently conducted in neoadjuvant settings (7.7% vs 33.3%). Afatinib The number of trials performed in perioperative settings was 46 in two-arm and 15 in three-arm RCTs. Of these, the proportion of industry-sponsored trials in two-arm and three-arm RCTs was 26.1% and 53.3%, respectively. Shared control designs (SCDs) which randomized to a common control arm and multiple experimental arms comprised 54.2% of 48 multi-arm RCTs. For SCDs, detailed information on multiplicity adjustment methods was seldom reported. The Bonferroni adjustment method together with alpha-spending functions was commonly used.

Breast cancer multi-arm RCTs have been increasingly reported. The majority of multi-arm RCTs are industry-sponsored trials using SCDs in neoadjuvant settings. Detailed description about multiplicity adjustment methods is required for multi-arm RCTs.
Breast cancer multi-arm RCTs have been increasingly reported. The majority of multi-arm RCTs are industry-sponsored trials using SCDs in neoadjuvant settings. Detailed description about multiplicity adjustment methods is required for multi-arm RCTs.
There are limited quantitative approaches for evaluating rare safety outcomes from controlled clinical trials in either a blinded or unblinded setting. This manuscript demonstrates an application of three statistical methods for quantitative safety monitoring that can be implemented during any phase of a clinical trial, including open-label extension studies.

An interactive safety monitoring (iSM) tool was developed using R language in the publicly available R-Shiny app and was implemented for three statistical methods of quantitative safety monitoring. These methods are sequential probability ratio test (SPRT), maximized SPRT (MaxSPRT), and Bayesian posterior probability threshold (BPPT). The iSM tool evaluated specific safety signals that incorporated pre-specified background rates or reference risk ratios.

Two sets of blinded clinical trial data were used for case studies to demonstrate the use the iSM tool. Two particular adverse events, myocardial infarction (MI) and serious infection, were monitored. Monte Carlo simulation was conducted to evaluate the operating characteristics of pre-specified parameters. It showed that after adjusting for exposure, the BPPT and MaxSPRT yielded similar results in identifying a pre-specified signals while the SPRT method failed to detect such signals.

Statistical methods shown for the case studies, as well as the application of the user-friendly iSM tool, greatly enhance the quantitative monitoring of safety events of interest in ongoing clinical trials The BPPT and MaxSPRT methods seem more sensitive in picking-up early signals than the SPRT method when the number of safety events is small.
Statistical methods shown for the case studies, as well as the application of the user-friendly iSM tool, greatly enhance the quantitative monitoring of safety events of interest in ongoing clinical trials The BPPT and MaxSPRT methods seem more sensitive in picking-up early signals than the SPRT method when the number of safety events is small.
The technological complexities and broad operational scope of eSource impede coordinated, inter-organizational action on advancing at-scale solutions.

We introduce an architectural framework for articulating technological considerations across organizations. The architecture neither implies nor endorses solution implementations; rather, it proposes solution functionality based upon principles and good clinical practices.

Key technology considerations include patterns of anticipated use, implications to the current state of clinical trial operations, and the need for new technologies (i.e., IoT, Big Data, Predictive Analytics).

Technology considerations drive implications beyond technology-influencing regulatory, process, and ethical realms of clinical research.
Technology considerations drive implications beyond technology-influencing regulatory, process, and ethical realms of clinical research.
Due to the extreme heterogeneity of lupus and the lack of consensus among stakeholders, pharmaceutical and biotechnology companies have had limited success in developing treatments for lupus. For this reason, the Lupus Foundation of America (LFA), researchers at the Center for the Study of Drug Development at Tufts University School of Medicine (Tufts CSDD) and an advisory committee of 13 international lupus experts collaborated to launch the Addressing Lupus Pillars for Health Advancement (ALPHA) project.

To inform the ALPHA project, 17 in-depth interviews among lupus experts and a global survey among lupus drug development and clinical care professionals was conducted to identify, characterize, and prioritize fundamental barriers and validate findings.

The global survey received 127 responses from experts across 20 countries. Results of the in-depth interviews and the survey findings were consistent. Top barriers to developing new medical treatments for lupus included the lack of a clear definition of the disease with respondents identifying 30 autoimmune conditions that may be lupus-related; lack of predictive biomarkers; flaws in clinical trial designs; and a lack of reliable outcome measures.

The study findings encourage drug development professionals to validate disease-specific measures and to identify if specific symptoms are caused by lupus. This original research also provides a methodology that can be applied to highly heterogenous diseases where low consensus on diagnosis and treatment exists among drug development and health professionals.
The study findings encourage drug development professionals to validate disease-specific measures and to identify if specific symptoms are caused by lupus. This original research also provides a methodology that can be applied to highly heterogenous diseases where low consensus on diagnosis and treatment exists among drug development and health professionals.
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