Drug safety data mining with a tree-based scan statistic

2013 ◽  
Vol 22 (5) ◽  
pp. 517-523 ◽  
Author(s):  
Martin Kulldorff ◽  
Inna Dashevsky ◽  
Taliser R. Avery ◽  
Arnold K. Chan ◽  
Robert L. Davis ◽  
...  
Keyword(s):  
Data Mining ◽  
Drug Safety ◽  
Safety Data ◽  
Scan Statistic

scholarly journals C-C5-01: Drug Safety Data Mining with a Tree-Based Scan Statistic

2011 ◽  
Vol 9 (3-4) ◽  
pp. 180-180
Author(s):  
J. Brown ◽  
I. Dashevsky ◽  
B. Fireman ◽  
L. Herrinton ◽  
D. McClure ◽  
...  
Keyword(s):  
Data Mining ◽  
Drug Safety ◽  
Safety Data ◽  
Scan Statistic

scholarly journals Legislation and current developments in adverse drug reaction reporting in Mongolia: how far are we?

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Zuzaan Zulzaga ◽  
Erdenetuya Myagmarsuren ◽  
Herman J. Woerdenbag ◽  
Eugene P. van Puijenbroek
Keyword(s):  
Adverse Drug Reaction ◽  
Drug Reaction ◽  
Drug Safety ◽  
Reporting System ◽  
Adverse Drug Reaction Reporting ◽  
Safety Data ◽  
Regulatory System ◽  
Basic Structure ◽  
Reaction Reporting ◽  
Adr Reporting

AbstractMonitoring adverse drug reactions is a vital issue to ensure drug safety and to protect the general public from medication-related harmful effects. In order to properly monitor drug safety, a regulatory system needs to be in place as well as an infrastructure that allows for analyzing national and international safety data. In Mongolia, adverse drug reaction (ADR) reporting activities have been implemented in the past decade. During this period, the basic structure and legal basis of an adverse drug reaction monitoring system was established. Because of the fragmented but growing healthcare system and the complexity of pharmaceutical issues in Mongolia, a sustainable process for the development of the adverse drug reaction reporting system is a key issue. The aim of this article is to disclose the Mongolian situation for the rest of the world and to share experiences on how an ADR reporting system can be developed towards a higher and more advanced level to contribute to both national and international drug safety issues. In this article, we review the features of the Mongolian health care and pharmaceutical systems, as well as the current development of the adverse drug reaction reporting system.

Data mining in drug safety

2007 ◽  
pp. xxxiii-xlvi ◽  
Author(s):  
Manfred Hauben ◽  
Andrew Bate
Keyword(s):  
Data Mining ◽  
Drug Safety

scholarly journals Two-stage Bayesian hierarchical modeling for blinded and unblinded safety monitoring in randomized clinical trials

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Junhao Liu ◽  
Jo Wick ◽  
Renee’ H. Martin ◽  
Caitlyn Meinzer ◽  
Dooti Roy ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Drug Safety ◽  
Safety Data ◽  
Safety Monitoring ◽  
Safety Signal ◽  
Two Stage ◽  
Bayesian Hierarchical ◽  
Potential Safety ◽  
Stage 1

Abstract Background Monitoring and reporting of drug safety during a clinical trial is essential to its success. More recent attention to drug safety has encouraged statistical methods development for monitoring and detecting potential safety signals. This paper investigates the potential impact of the process of the blinded investigator identifying a potential safety signal, which should be further investigated by the Data and Safety Monitoring Board with an unblinded safety data analysis. Methods In this paper, two-stage Bayesian hierarchical models are proposed for safety signal detection following a pre-specified set of interim analyses that are applied to efficacy. At stage 1, a hierarchical blinded model uses blinded safety data to detect a potential safety signal and at stage 2, a hierarchical logistic model is applied to confirm the signal with unblinded safety data. Results Any interim safety monitoring analysis is usually scheduled via negotiation between the trial sponsor and the Data and Safety Monitoring Board. The proposed safety monitoring process starts once 53 subjects have been enrolled into an eight-arm phase II clinical trial for the first interim analysis. Operating characteristics describing the performance of this proposed workflow are investigated using simulations based on the different scenarios. Conclusions The two-stage Bayesian safety procedure in this paper provides a statistical view to monitor safety during the clinical trials. The proposed two-stage monitoring model has an excellent accuracy of detecting and flagging a potential safety signal at stage 1, and with the most important feature that further action at stage 2 could confirm the safety issue.

scholarly journals Statistical Power for Postlicensure Medical Product Safety Data Mining

2017 ◽  
Vol 5 (1) ◽  
pp. 6 ◽  
Author(s):  
Judith C. Maro ◽  
Michael D. Nguyen ◽  
Inna Dashevsky ◽  
Meghan A. Baker ◽  
Martin Kulldorff
Keyword(s):  
Data Mining ◽  
Statistical Power ◽  
Safety Data ◽  
Medical Product ◽  
Product Safety

scholarly journals Systematic review of statistical methods for safety data in malaria chemoprevention in pregnancy trials

2019 ◽  
Author(s):  
Noel Patson ◽  
Mavuto Mukaka ◽  
Kennedy N Otwombe ◽  
Lawrence Kazembe ◽  
Don P Mathanga ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Missing Data ◽  
Drug Safety ◽  
Safety Data ◽  
Malaria Prevention ◽  
Antimalarial Drugs ◽  
Controlled Trials ◽  
Safety Outcomes ◽  
In Pregnancy

Abstract Background Drug safety assessments in clinical trials present unique analytical challenges. Some of these include adjusting for individual follow-up time, repeated measurements of multiple outcomes and missing data among others. Furthermore, pre-specifying appropriate analysis becomes difficult as some safety endpoints are unexpected. Although existing guidelines such as CONSORT encourage thorough reporting of adverse events (AEs) in clinical trials, they provide limited details for safety data analysis. The limited guidelines may influence suboptimal analysis by failing to account for some analysis challenges above. A typical example where such challenges exist are trials of antimalarial drugs for malaria prevention during pregnancy. Lack of proper standardized evaluation of the safety of antimalarial drugs has limited the ability to draw conclusions about safety. We have therefore conducted a systematic review to establish the current practice in statistical analysis for antimalarial drug safety in pregnancy.Methods We searched PubMed, Embase, Scopus, Malaria in Pregnancy Library and Cochrane Central Register of Controlled Trials for original English articles reporting Phase III (randomized controlled trials) RCTs on antimalarial drugs for malaria prevention in pregnancy published from January 2010 to July 2019.Results Eighteen trials were included in this review that collected multiple longitudinal safety outcomes including AEs. Statistical analysis and reporting of the safety outcomes in all the trials used descriptive statistics; proportions/counts (n=18, 100%) and mean/median (n=2, 11.1%). Results presentation included tabular (n=16, 88.9%) and text description (n=2, 11.1%). Univariate inferential methods were reported in most trials (n=16, 88.9%); including Chi-square/Fisher`s exact test (n=12, 66.7%), t-test (n=2, 11.1%) and Mann-Whitney/Wilcoxon test (n=1, 5.6%). Multivariable methods, including Poisson and negative binomial were reported in few trials (n=4, 22.2%). Assessment of a potential link between missing efficacy data and safety outcomes was not reported in any of the trials that reported efficacy missing data (n=7, 38.9%).Conclusion The review demonstrated that statistical analysis of safety data in antimalarial drugs for malarial chemoprevention in pregnancy RCTs are inadequate. The analysis insufficiently account for multiple safety outcomes potential dependence, follow-up time and informative missing data which can compromise antimalarial drug safety evidence development, based on the available data.

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