scholarly journals Observational Studies of Drug Safety in Multi-Database Studies: Methodological Challenges and Opportunities

2016 ◽  
Vol 4 (1) ◽  
pp. 9 ◽  
Author(s):  
Robert W. Platt ◽  
Colin R. Dormuth ◽  
Dan Chateau ◽  
Kristian Filion
Keyword(s):  
Drug Safety ◽  
Observational Studies ◽  
Methodological Challenges ◽  
Challenges And Opportunities

scholarly journals Reports of the AAAI 2016 Spring Symposium Series

AI Magazine ◽  
2017 ◽  
Vol 37 (4) ◽  
pp. 83-88
Author(s):  
Christopher Amato ◽  
Ofra Amir ◽  
Joanna Bryson ◽  
Barbara Grosz ◽  
Bipin Indurkhya ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Observational Studies ◽  
Human Error ◽  
Well Being ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Symposium Series ◽  
Research Platform ◽  
Challenges And Opportunities

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2016 Spring Symposium Series on Monday through Wednesday, March 21-23, 2016 at Stanford University. The titles of the seven symposia were (1) AI and the Mitigation of Human Error: Anomalies, Team Metrics and Thermodynamics; (2) Challenges and Opportunities in Multiagent Learning for the Real World (3) Enabling Computing Research in Socially Intelligent Human-Robot Interaction: A Community-Driven Modular Research Platform; (4) Ethical and Moral Considerations in Non-Human Agents; (5) Intelligent Systems for Supporting Distributed Human Teamwork; (6) Observational Studies through Social Media and Other Human-Generated Content, and (7) Well-Being Computing: AI Meets Health and Happiness Science.

scholarly journals Next-Generation Metagenomics: Methodological Challenges and Opportunities

2019 ◽  
Vol 23 (7) ◽  
pp. 327-333 ◽  
Author(s):  
Ilaria Laudadio ◽  
Valerio Fulci ◽  
Laura Stronati ◽  
Claudia Carissimi
Keyword(s):  
Next Generation ◽  
Methodological Challenges ◽  
Challenges And Opportunities

Challenges and Opportunities in Implementing Pharmacogenetic Testing in Clinical Settings

2021 ◽  
Vol 61 (1) ◽  
pp. 65-84 ◽  
Author(s):  
Wan-Chun Chang ◽  
Reo Tanoshima ◽  
Colin J.D. Ross ◽  
Bruce C. Carleton
Keyword(s):  
Health Care ◽  
Clinical Practice ◽  
Drug Safety ◽  
Practice Guidelines ◽  
Genetic Variants ◽  
Clinical Settings ◽  
Drug Selection ◽  
Clinical Implementation ◽  
Pharmacogenetic Testing ◽  
Challenges And Opportunities

The clinical implementation of pharmacogenetic biomarkers continues to grow as new genetic variants associated with drug outcomes are discovered and validated. The number of drug labels that contain pharmacogenetic information also continues to expand. Published, peer-reviewed clinical practice guidelines have also been developed to support the implementation of pharmacogenetic tests. Incorporating pharmacogenetic information into health care benefits patients as well as clinicians by improving drug safety and reducing empiricism in drug selection. Barriers to the implementation of pharmacogenetic testing remain. This review explores current pharmacogenetic implementation initiatives with a focus on the challenges of pharmacogenetic implementation and potential opportunities to overcome these challenges.

The Ethics of Postmarketing Observational Studies of Drug Safety Under Section 505(o)(3) of the Food, Drug, and Cosmetic Act

2012 ◽  
Vol 38 (4) ◽  
pp. 577-606 ◽  
Author(s):  
Barbara J. Evans
Keyword(s):  
Clinical Trials ◽  
Drug Safety ◽  
Observational Studies ◽  
Claims Data ◽  
Data Access ◽  
Pharmaceutical Companies ◽  
Insurance Claims ◽  
Privacy Concerns ◽  
Insurance Claims Data ◽  
Access To Data

In 2007, Congress granted the Food and Drug Administration (FDA) new powers to order pharmaceutical companies to conduct drug safety studies and clinical trials in the postmarketing period after drugs are approved. The methodologies include observational studies that examine patients' insurance claims data and clinical records to infer whether drugs are safe in actual clinical practice. Such studies offer a valuable tool for improving drug safety, but they raise ethical and privacy concerns because they would entail widespread use of patients' health information in commercial research by drug manufacturers. This is the first article to explore the ethics of these section 505(o)(3) observational studies, so named after the section of the Food, Drug, and Cosmetic Act that authorizes them.Data access problems threaten to make the FDA's section 505(o)(3) study requirements unenforceable. Under existing federal privacy regulations, it appears highly unlikely that pharmaceutical companies will have reliable access to crucial data resources, such as insurance claims data and healthcare records, to use in these studies. State privacy laws present another potential barrier to data access. If pharmaceutical companies do manage to gain access to the needed data, this will raise serious privacy concerns because section 505(o)(3) observational studies do not appear to be covered by any of the major federal regulations that afford ethical and privacy protections to persons whose data are used in research.If the FDA's program of section 505(o)(3) observational studies fails because of the above problems, this failure will have a number of bad consequences: the public will be exposed to avoidable drug safety risks; taxpayers may be forced to bear the costs of having the FDA conduct drug safety investigations that would have been funded by drug manufacturers if data had been available; and, perhaps most troubling, the FDA may be forced to order postmarketing clinical trials to answer questions that could have been answered using observational studies. Problems with access to data for section 505(o)(3) studies thus could directly imperil human research subjects by forcing a needless over-reliance on risky postmarketing drug safety trials.This Article concludes by describing a promising new legal pathway for resolving these problems. Congress has provided the FDA a new set of powers that if skillfully exercised will allow the agency: (1) to facilitate pharmaceutical companies' appropriate access to data for use in section 505(o)(3) observational studies, (2) to impose strict ethical and privacy protections for persons whose data are used in these studies, and (3) to mobilize private-sector funding to generate much-needed evidence of the safety of FDA-approved drugs.

scholarly journals Therapy of Pediatric-Onset Multiple Sclerosis: State of the Art, Challenges, and Opportunities

2021 ◽  
Vol 12 ◽  
Author(s):  
Monica Margoni ◽  
Francesca Rinaldi ◽  
Paola Perini ◽  
Paolo Gallo
Keyword(s):  
Multiple Sclerosis ◽  
Observational Studies ◽  
State Of The Art ◽  
Therapeutic Agents ◽  
Efficacy And Safety ◽  
Disease Modifying Drugs ◽  
Current State ◽  
Challenges And Opportunities ◽  
Children And Young Adults ◽  
Pediatric Onset

Treatment of pediatric-onset multiple sclerosis (POMS) has been tailored after observational studies and data obtained from clinical trials in adult-onset multiple sclerosis (AOMS) patients. There are an increasing number of new therapeutic agents for AOMS, and many will be formally studied for use also in POMS. However, there are important efficacy and safety concerns regarding the use of these therapies in children and young adults. This review will discuss the current state of the art of POMS therapy and will focus on the newer therapies (oral and infusion disease-modifying drugs) and on those still currently under investigation.

scholarly journals Combining Propensity Score and Random Coefficient Modelling as an Approach to Analyse Complex Longitudinal Data

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Stefan Clos ◽  
Peter Donnan ◽  
Petra Rauchhaus
Keyword(s):  
Propensity Score ◽  
Longitudinal Data ◽  
Drug Safety ◽  
Observational Studies ◽  
Large Population ◽  
Population Based ◽  
Random Coefficient ◽  
Historical Change ◽  
Analysis Of Covariance ◽  
Data Set

ABSTRACT ObjectivesWe looked for an approach to analyze/visualize a set of repeated measures of renal laboratory data (eGFR [estimated Glomerular Filtration Rate] from an observational population-based data set) as safety parameters in a longitudinal design and calculate annual changes in different sub-cohorts. Previous meta-analyses had struggled to address this problem (due to poor data quality and strong heterogeneity in underlying historical studies) and previous large population-based observational studies had only looked into binary outcomes. Particular challenges lay (1) in the complexity of the data set with irregularly spaced observation points, (2) in the observational character of the data with associated bias and confounding by indication and co-medication and (3) in the change of lab method during the observation period. ResultsOut of a population base of 400.000 we analysed linked longitudinal data of more than 1000 eligible patients with over 1000.000 prescription records of index drug or co-prescription drugs. Data were provided by the Dundee University Health Informatics Centre (HIC). We addressed the differences in covariates (which typically can lead to biased estimates of treatment effects in observational studies) via individual propensity scores (to reduce this bias by balancing the covariates in the two groups) and a hierarchical modelling approach. A Random Coefficient Model (via proc mixed in SAS 9.3) proved a much more powerful statistical tool than analysis of covariance of the summary measure during follow-up, particularly as the latter approach is less efficient when applied to longitudinal data with missing data points and irregularly spaced repeated measures.Visualization was achieved with the SAS 9.3 GPLOT procedure combined with a spline function. The historical change in lab method was addressed via a conversion of lab results to an internationally recognized standard (IDMS aligned method). We were able to achieve plausible and more precise estimates of the annual decline in eGFR in the patient group of interest than previous attempts from other research publications. This led to a publication in a high profile journal. ConclusionOur approach of combining a Propensity Score and Random Coefficient Modelling was successful to answer a question in drug safety using repeated measurement data from a longitudinal observational population based data set. This approach may be useful for other research questions in Drug Safety or in Comparative Clinical Effectiveness Research for continuous outcome measures.

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