Biomedical subjects

Andrew Bate

Publications and source records attributed to Andrew Bate.

7 recordsLinked to original sources

PubMed2006

Extending the methods used to screen the WHO drug safety database towards analysis of complex associations and improved accuracy for rare events.

Post-marketing drug safety data sets are often massive, and entail problems with heterogeneity and selection bias. Nevertheless, quantitative methods have proven a very useful aid to help clinical experts in screening for previously unknown associations in these data sets. The WHO international drug safety database is the world's largest data set of its kind with over three million reports on suspected adverse drug reaction incidents. Since 1998, an exploratory data analysis method has been in routine use to screen for quantitative associations in this data set. This method was originally based on large sample approximations and limited to pairwise associations, but in this article we propose more accurate credibility interval estimates and extend the method to allow for the analysis of more complex quantitative associations. The accuracy of the proposed credibility intervals is evaluated through comparison to precise Monte Carlo simulations. In addition, we propose a Mantel-Haenszel-type adjustment to control for suspected confounders.

Databases, Factual↗

PubMed2006

Data mining in spontaneous reports.

The increasing size of spontaneous report data sets and the increasing capability for screening such data due to increases in computational power has led to a recent increase in interest and use of data mining on such data. While data mining plays an important role in the analysis of spontaneous reports, there is general debate on how and when data mining should be best performed. While the cornerstone principles for data mining of spontaneous reports have been in place since the 1960s, several significant changes have occurred to make their use widespread. Superficially the Bayesian methods seem unnecessarily complex, particularly given the nature of the data, but in practice implementation in Bayesian framework gives clear benefits. There are difficulties evaluating the performance of the methods, but they work and save resources in managing large data sets. The use of neural networks allows more sophisticated pattern recognition to be performed.

Adverse Drug Reaction Reporting Systems↗

PubMed2005

A bayesian recurrent neural network for unsupervised pattern recognition in large incomplete data sets.

A recurrent neural network, modified to handle highly incomplete training data is described. Unsupervised pattern recognition is demonstrated in the WHO database of adverse drug reactions. Comparison is made to a well established method, AutoClass, and the performances of both methods is investigated on simulated data. The neural network method performs comparably to AutoClass in simulated data, and better than AutoClass in real world data. With its better scaling properties, the neural network is a promising tool for unsupervised pattern recognition in huge databases of incomplete observations.

Algorithms↗

PubMed2003

Hepatic injury and pancreatitis during treatment with serotonin reuptake inhibitors: data from the World Health Organization (WHO) database of adverse drug reactions.

Severe hepatic adverse drug reactions have been occasionally reported in the literature for the selective serotonin reuptake inhibitors (SSRIs), venlafaxine and nefazodone. In addition, a few case reports have suggested a possible association between SSRI treatment and pancreatitis. To further investigate this issue, a Bayesian confidence propagation neural network (BCPNN) method was applied on the World Health Organization database of adverse drug reactions. This method identifies whether a drug/adverse drug reaction combination is reported more frequently to the database than expected on the basis of chance alone compared to general reporting in the database. A statistically significant unexpected high number of reports were found for nefazodone and hepatic injury, relative to the generality of the dataset but, for the other drug/adverse drug reaction combinations, no such association was found. The nefazodone finding is in accordance with data from other publications, suggesting that the risk of hepatic injury is increased. However, because of the nature of the BCPNN, the negative findings do not necessarily prove that there is no excess risk for hepatic injury/pancreatitis during treatment with drugs other than nefazodone. Further studies are required using alternative methodologies to demonstrate whether the selective serotonin reuptake inhibitors or venlafaxine may cause hepatic injury or pancreatitis.

Adverse Drug Reaction Reporting Systems↗

PubMed2002

A data mining approach for signal detection and analysis.

The WHO database contains over 2.5 million case reports, analysis of this data set is performed with the intention of signal detection. This paper presents an overview of the quantitative method used to highlight dependencies in this data set. The method Bayesian confidence propagation neural network (BCPNN) is used to highlight dependencies in the data set. The method uses Bayesian statistics implemented in a neural network architecture to analyse all reported drug adverse reaction combinations. This method is now in routine use for drug adverse reaction signal detection. Also this approach has been extended to highlight drug group effects and look for higher order dependencies in the WHO data. Quantitatively unexpectedly strong relationships in the data are highlighted relative to general reporting of suspected adverse effects; these associations are then clinically assessed.

Adverse Drug Reaction Reporting Systems↗

PubMedDate not supplied

A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions.

PURPOSE: A continuous systematic review of all combinations of drugs and suspected adverse reactions (ADRs) reported to a spontaneous reporting system, is necessary to optimize signal detection. To focus attention of human reviewers, quantitative procedures can be used to sift data in different ways. In various centres, different measures are used to quantify the extent to which an ADR is reported disproportionally to a certain drug compared to the generality of the database. The objective of this study is to examine the level of concordance of the various estimates to the measure used by the WHO Collaborating Centre for International ADR monitoring, the information component (IC), when applied to the dataset of the Netherlands Pharmacovigilance Foundation Lareb. METHODS: The Reporting Odds Ratio--1.96 standard errors (SE), proportional reporting ratio--1.96 SE, Yule's Q--1.96 SE, the Poisson probability and Chi-square test of all 17,330 combinations were compared with the IC minus 2 standard deviations. Additionally, the concordance of the various tests, in respect to the number of reports per combination, was examined. RESULTS: In general, sensitivity was high in respect to the reference measure when a combination of point- and precision estimate was used. The concordance increased dramatically when the number of reports per combination increased. CONCLUSION: This study shows that the different measures used are broadly comparable when four or more cases per combination have been collected.

Adverse Drug Reaction Reporting Systems↗

PubMedDate not supplied

Selective serotonin reuptake inhibitors in pregnant women and neonatal withdrawal syndrome: a database analysis.

BACKGROUND: Selective serotonin reuptake inhibitors (SSRIs) have been associated with withdrawal symptoms. We investigated whether use of these drugs in pregnant women might cause neonatal withdrawal syndrome. METHODS: An association between paroxetine and neonatal convulsions was identified in December, 2001, by the data mining method routinely used to screen the WHO database of adverse drug reactions. An information component (IC) measure was used to screen for unexpected adverse reactions relative to the information in the database. We then assessed cases of neonatal convulsions and neonatal withdrawal syndrome associated with drugs included in the anatomical therapeutic chemical groups N06AB and N06AX. FINDINGS: By November, 2003, a total of 93 suspected cases of SSRI-induced neonatal withdrawal syndrome had been reported, and were regarded as enough information to confirm a possible causal relation. 64 of the cases were associated with paroxetine, 14 with fluoxetine, nine with sertraline, and seven with citalopram. The IC-2 SD for the group became greater than 0 in the first quarter of 1991, and the IC increased to 2.68 (IC-2 SD 0.32) by the second quarter of 2003. For each individual compound, the IC-2 SD was greater than 0. INTERPRETATION: SSRIs, especially paroxetine, should be cautiously managed in the treatment of pregnant women with a psychiatric disorder.

Antidepressive Agents, Second-Generation↗