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- Hopstadius, Johan, et al.
(author)
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Impact of Stratification on Adverse Drug Reaction Surveillance
- 2008
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In: Drug Safety. - 0114-5916 .- 1179-1942. ; 31:11, s. 1035-1048
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Journal article (peer-reviewed)abstract
- BACKGROUND AND OBJECTIVES: Automated screening for excessive adverse drug reaction (ADR) reporting rates has proven useful as a tool to direct clinical review in large-scale drug safety signal detection. Some measures of disproportionality can be adjusted to eliminate any undue influence on the ADR reporting rate of covariates, such as patient age or country of origin, by using a weighted average of stratum-specific measures of disproportionality. Arguments have been made in favour of routine adjustment for a set of common potential confounders using stratification. The aim of this paper is to investigate the impact of using adjusted observed-to-expected ratios, as implemented for the Empirical Bayes Geometric Mean (EBGM) and the information component (IC) measures of disproportionality, for first-pass analysis of the WHO database. METHODS: A simulation study was carried out to investigate the impact of simultaneous adjustment for several potential confounders based on stratification. Comparison between crude and adjusted observed-to-expected ratios were made based on random allocation of reports to a set of strata with a realistic distribution of stratum sizes. In a separate study, differences between the crude IC value and IC values adjusted for (combinations of) patient sex, age group, reporting quarter and country of origin, with respect to their concordance with a literature comparison were analysed. Comparison was made to the impact on signal detection performance of a triage criterion requiring reports from at least two countries before a drug-ADR pair was highlighted for clinical review. RESULTS: The simulation study demonstrated a clear tendency of the adjusted observed-to-expected ratio to spurious (and considerable) underestimation relative to the crude one, in the presence of any very small strata in a stratified database. With carefully implemented stratification that did not yield any very small strata, this tendency could be avoided. Routine adjustment for potential confounders improved signal detection performance relative to the literature comparison, but the magnitude of the improvement was modest. The improvement from the triage criterion was more considerable. DISCUSSION AND CONCLUSIONS: Our results indicate that first-pass screening based on observed-to-expected ratios adjusted with stratification may lead to missed signals in ADR surveillance, unless very small strata are avoided. In addition, the improvement in signal detection performance due to routine adjustment for a set of common confounders appears to be smaller than previously assumed. Other approaches to improving signal detection performance such as the development of refined triage criteria may be more promising areas for future research.
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- Norén, G. Niklas, et al.
(author)
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Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery
- 2013
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In: Statistical Methods in Medical Research. - : SAGE Publications. - 0962-2802 .- 1477-0334. ; 22:1, s. 57-69
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Journal article (peer-reviewed)abstract
- Large observational data sets are a great asset to better understand the effects of medicines in clinical practice and, ultimately, improve patient care. For an empirical pattern in observational data to be of practical relevance, it should represent a substantial deviation from the null model. For the purpose of identifying such deviations, statistical significance tests are inadequate, as they do not on their own distinguish the magnitude of an effect from its data support. The observed-to-expected (OE) ratio on the other hand directly measures strength of association and is an intuitive basis to identify a range of patterns related to event rates, including pairwise associations, higher order interactions and temporal associations between events over time. It is sensitive to random fluctuations for rare events with low expected counts but statistical shrinkage can protect against spurious associations. Shrinkage OE ratios provide a simple but powerful framework for large-scale pattern discovery. In this article, we outline a range of patterns that are naturally viewed in terms of OE ratios and propose a straightforward and effective statistical shrinkage transformation that can be applied to any such ratio. The proposed approach retains emphasis on the practical relevance and transparency of highlighted patterns, while protecting against spurious associations.
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- Norén, G. Niklas, et al.
(author)
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Temporal pattern discovery in longitudinal electronic patient records
- 2010
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In: Data mining and knowledge discovery. - : Springer Science and Business Media LLC. - 1384-5810 .- 1573-756X. ; 20:3, s. 361-387
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Journal article (peer-reviewed)abstract
- Large collections of electronic patient records provide a vast but still underutilised source of information on the real world use of medicines. They are maintained primarily for the purpose of patient administration, but contain a broad range of clinical information highly relevant for data analysis. While they are a standard resource for epidemiological confirmatory studies, their use in the context of exploratory data analysis is still limited. In this paper, we present a framework for open-ended pattern discovery in large patient records repositories. At the core is a graphical statistical approach to summarising and visualising the temporal association between the prescription of a drug and the occurrence of a medical event. The graphical overview contrasts the observed and expected number of occurrences of the medical event in different time periods both before and after the prescription of interest. In order to effectively screen for important temporal relationships, we introduce a new measure of temporal association, which contrasts the observed-to-expected ratio in a time period immediately after the prescription to the observed-to-expected ratio in a control period 2 years earlier. An important feature of both the observed-to-expected graph and the measure of temporal association is a statistical shrinkage towards the null hypothesis of no association, which provides protection against highlighting spurious associations. We demonstrate the usefulness of the proposed pattern discovery methodology by a set of examples from a collection of over two million patient records in the United Kingdom. The identified patterns include temporal relationships between drug prescriptions and medical events suggestive of persistent and transient risks of adverse events, possible beneficial effects of drugs, periodic co-occurrence, and systematic tendencies of patients to switch from one medication to another.
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- Strandell, Johanna, et al.
(author)
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The Development and Evaluation of Triage Algorithms for Early Discovery of Adverse Drug Interactions
- 2013
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In: Drug Safety. - : Adis. - 0114-5916 .- 1179-1942. ; 36:5, s. 371-388
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Journal article (peer-reviewed)abstract
- Background Around 20 % of all adverse drug reactions (ADRs) are due to drug interactions. Some of these will only be detected in the postmarketing setting. Effective screening in large collections of individual case safety reports (ICSRs) requires automated triages to identify signals of adverse drug interactions. Research so far has focused on statistical measures, but clinical information and pharmacological characteristics are essential in the clinical assessment and may be of great value in first-pass filtering of potential adverse drug interaction signals. less thanbrgreater than less thanbrgreater thanObjective The aim of this study was to develop triages for adverse drug interaction surveillance, and to evaluate these prospectively relative to clinical assessment. less thanbrgreater than less thanbrgreater thanMethods A broad set of variables were considered for inclusion in the triages, including cytochrome P450 (CYP) activity, explicit suspicions of drug interactions as noted by the reporter, dose and treatment overlap, and a measure of interaction disproportionality. Their unique contributions in predicting signals of adverse drug interactions were determined through logistic regression. This was based on the reporting in the WHO global ICSR database, VigiBase (TM), for a set of known adverse drug interactions and corresponding negative controls. Three triages were developed, each producing an estimated probability that a given drug-drug-ADR triplet constitutes an adverse drug interaction signal. The triages were evaluated against two separate benchmarks derived from expert clinical assessment: adverse drug interactions known in the literature and prospective adverse drug interaction signals. For reference, the triages were compared with disproportionality analysis alone using the same benchmarks. less thanbrgreater than less thanbrgreater thanResults The following were identified as valuable predictors of adverse drug interaction signals: plausible CYP metabolism; notes of suspected interaction by the reporter; and reports of unexpected therapeutic response, altered therapeutic effect with dose information and altered therapeutic effect when only two drugs had been used. The new triages identified reporting patterns corresponding to both prospective signals of adverse drug interactions and already established ones. They perform better than disproportionality analysis alone relative to both benchmarks. less thanbrgreater than less thanbrgreater thanConclusions A range of predictors for adverse drug interaction signals have been identified. They substantially improve signal detection capacity compared with disproportionality analysis alone. The value of incorporating clinical and pharmacological information in first-pass screening is clear.
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- Strandell, Johanna, et al.
(author)
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Triage algorithms for early discovery of adverse drug interactions
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Other publication (other academic/artistic)abstract
- Background: Most methodological research for broad surveillance of drug interactions in large collections of suspected ADR reports has focused on measures of disproportionality. However, recent results indicate that reported clinical information and pharmacological characteristics may be at least as valuable to detect adverse drug interactions early. Objective: To develop triage algorithms for adverse drug interaction surveillance, and to evaluate the algorithms prospectively relative to expert clinical assessment. Methods: A previously developed reference set based on Stockley’s Drug Interactions was used to train the algorithms. Logistic regression was used to set the relative weights of the different indicators (information potentially suggestive adverse drug interactions such as pharmacological properties including cytochrome P450 (CYP) activity; explicit suspicions of drug interactions as noted by the reporter in different forms; clinical details such as dose and treatment overlap; and a measure of disproportionality based on the total number of reports on two drugs and one ADR together) of each algorithm. Three triage algorithms were designed. All are logistic regression models producing an estimated probability that a given case series constitutes an adverse drug interaction signal. Two of them are data driven: one which used a very broad set of indicators (full data-driven) and one which used a more narrow set (lean data-driven). The third was manually derived (lean clinical) as a simplified version of the full data-driven algorithm. An independent evaluation set was constructed that consisted of 100 randomly selected case series in the WHO Global Individual Case Safety Report (ICSR) Database, VigiBase, from January 1990 to February 2011. Each algorithm’s ranking of case series was evaluated against an evaluation set. In a complementary analysis the algorithm were compared to a pure disproportionality analysis. Results: The two lean algorithms were comparable in performance. However both outperformed the full data-driven algorithm on the independent evaluation set. The areas under the curve (AUC) for the receiver operating characteristics (ROC) curves were as follows: 71% (lean clinical) and 69% (lean data-driven). For a false positive rate (FPR) of up to 0.04 the lean algorithms classifies about 14,000 case series as potential interaction signals. Thresholds corresponding to greater FPRs are unlikely to be feasible in practice. The algorithms clearly outperform disproportionality analysis alone. Conclusions: The value of incorporating clinical and pharmacological information in first-pass screening for adverse drug interactions is clear. Two triage algorithms have been proposed that each effectively identify adverse drug interaction signals and clearly outperforming pure disproportionality analysis in this respect.
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