Human embryonic dopamine-neuron transplants survive in patients with severe Parkinson's disease and result in some clinical benefit in younger but not in older patients.

A common data mining task is the search for associations in large databases. Here we consider the search for "interestingly large" counts in a large frequency table, having millions of cells, most of which have an observed frequency of 0 or 1. We first construct a baseline or null hypothesis expected frequency for each cell, and then suggest and compare screening criteria for ranking the cell deviations of observed from expected count. A criterion based on the results of fitting an empirical Bayes model to the cell counts is recommended. An example compares these criteria for searching the FDA Spontaneous Reporting System database maintained by the Division of Pharmacovigilance and Epidemiology. In the example, each cell count is the number of reports combining one of 1,398 drugs with one of 952 adverse events (total of cell counts = 4.9 million), and the problem is to screen the drug-event combinations for possible further investigation.

Evidence from randomized controlled studies supports the effectiveness of data-driven computer-based reminder systems to improve prevention services in the ambulatory care setting.

Introduction Discovery of new adverse drug events (ADEs) in the post-approval period is an important goal of the health system. Data mining methods that can transform data into meaningful knowledge to inform patient safety have proven to be essential. New opportunities have emerged to harness data sources that have not been used within the traditional framework. This article provides an overview of recent methodological innovations and data sources used in support of ADE discovery and analysis.

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Signal detection algorithms (SDAs) are recognized as vital tools in pharmacovigilance. However, their performance characteristics are generally unknown. By leveraging a unique gold standard recently made public by the Observational Medical Outcomes Partnership and by conducting a unique systematic evaluation, we provide new insights into the diagnostic potential and characteristics of SDAs routinely applied to FDAs adverse event reporting system. We find that SDAs can attain reasonable predictive accuracy in signaling adverse events. Two performance classes emerge, indicating that the class of approaches addressing confounding and masking effects benefits safety surveillance. Our study shows that not all events are equally detectable, suggesting that specific events might be monitored more effectively through other sources. We provide performance guidelines for several operating scenarios to inform the trade-off between sensitivity and specificity for specific use cases. We also propose an approach and apply it to identify optimal signaling thresholds given specific misclassification tolerances.

Often the literature makes assertions of medical product effects on the basis of ‘ p < 0.05’. The underlying premise is that at this threshold, there is only a 5% probability that the observed effect would be seen by chance when in reality there is no effect. In observational studies, much more than in randomized trials, bias and confounding may undermine this premise. To test this premise, we selected three exemplar drug safety studies from literature, representing a case–control, a cohort, and a self-controlled case series design. We attempted to replicate these studies as best we could for the drugs studied in the original articles. Next, we applied the same three designs to sets of negative controls: drugs that are not believed to cause the outcome of interest. We observed how often p < 0.05 when the null hypothesis is true, and we fitted distributions to the effect estimates. Using these distributions, we compute calibrated p-values that reflect the probability of observing the effect estimate under the null hypothesis, taking both random and systematic error into account. An automated analysis of scientific literature was performed to evaluate the potential impact of such a calibration. Our experiment provides evidence that the majority of observational studies would declare statistical significance when no effect is present. Empirical calibration was found to reduce spurious results to the desired 5% level. Applying these adjustments to literature suggests that at least 54% of findings with p < 0.05 are not actually statistically significant and should be reevaluated. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

This paper considers the franlework of the so-called "market basket problem", in which a database of transactions is mined for the occurrence of unusually frequent item sets. h~ our case, "unusually frequent" involves estimates of the frequency of each item set divided by a baseline frequency computed as if items occurred independently. The focus is on obtaining reliable estimates of this measure of interestingness for all item sets, even item sets with relatively low frequencies. For example, in a medical database of patient histories, unusual item sets including the item "patient death" (or other serious adverse event) might hopefully be flagged with as few as 5 or 10 occurrences of" the item set, it being unacceptable to require that item sets occur in as many as 0.1% of millions of patient reports before the data mining algorithm detects a signal. Similar considerations apply in fraud detection applications.Thus we abandon the requirement that interesting item sets must contain a relatively large fixed minimal support, and adopt a criterion based on the results of fitting an empirical Bayes model to the item set counts. The model allows us to define a 95% Bayesian lower confidence limit for the "interestingness" measure of every item set, whereupon the item sets can be ranked according to their empirical Bayes confidence limits. For item sets of size J > 2, we also distinguish between muhi-item associations that can be explained by the observed J(J-l)12 pairwise associations, and item sets that are significantly more frequent than their pairwise associations would suggest. Such item sets can uncover complex or synergistic mechanisms generating multi-item associations. This methodology has been applied within the U.S. Food and Drug Administration (FDA) to databases of adverse drug reaction reports and within AT&T to customer international calling histories. We also present graphical techniques for exploring and understanding the modeling results.