tetano
Editor, Senior Moderator
Drug Saf. 2011 Dec 1. doi: 10.2165/11596630-000000000-00000. [Epub ahead of print]
Identifying Adverse Events of Vaccines Using a Bayesian Method of Medically Guided Information Sharing.
Crooks CJ, Prieto-Merino D, Evans SJ.
Source
Division of Epidemiology and Public Health, University of Nottingham, Nottingham, UK.
Abstract
Background: The detection of adverse events following immunization (AEFI) fundamentally depends on how these events are classified. Standard methods impose a choice between either grouping similar events together to gain power or splitting them into more specific definitions. We demonstrate a method of medically guided Bayesian information sharing that avoids grouping or splitting the data, and we further combine this with the standard epidemiological tools of stratification and multivariate regression. Objective: The aim of this study was to assess the ability of a Bayesian hierarchical model to identify gastrointestinal AEFI in children, and then combine this with testing for effect modification and adjustments for confounding. Study Design: Reporting odds ratios were calculated for each gastrointestinal AEFI and vaccine combination. After testing for effect modification, these were then re-estimated using multivariable logistic regression adjusting for age, sex, year and country of report. A medically guided hierarchy of AEFI terms was then derived to allow information sharing in a Bayesian model. Setting: All spontaneous reports of AEFI in children under 18 years of age in the WHO VigiBase? (Uppsala Monitoring Centre, Uppsala, Sweden) before June 2010. Reports with missing age were included in the main analysis in a separate category and excluded in a subsequent sensitivity analysis. Exposures: The 15 most commonly prescribed childhood vaccinations, excluding influenza vaccines. Main Outcome Measures: All gastrointestinal AEFI coded by WHO Adverse Reaction Terminology. Results: A crude analysis identified 132 signals from 655 reported combinations of gastrointestinal AEFI. Adjusting for confounding by age, sex, year of report and country of report, where appropriate, reduced the number of signals identified to 88. The addition of a Bayesian hierarchical model identified four further signals and removed three. Effect modification by age and sex was identified for six vaccines for the outcomes of vomiting, nausea, diarrhoea and salivary gland enlargement. Conclusion: This study demonstrated a sequence of methods for routinely analysing spontaneous report databases that was easily understandable and reproducible. The combination of classical and Bayesian methods in this study help to focus the limited resources for hypothesis testing studies towards adverse events with the strongest support from the data.
PMID:
22136183
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/22136183
Identifying Adverse Events of Vaccines Using a Bayesian Method of Medically Guided Information Sharing.
Crooks CJ, Prieto-Merino D, Evans SJ.
Source
Division of Epidemiology and Public Health, University of Nottingham, Nottingham, UK.
Abstract
Background: The detection of adverse events following immunization (AEFI) fundamentally depends on how these events are classified. Standard methods impose a choice between either grouping similar events together to gain power or splitting them into more specific definitions. We demonstrate a method of medically guided Bayesian information sharing that avoids grouping or splitting the data, and we further combine this with the standard epidemiological tools of stratification and multivariate regression. Objective: The aim of this study was to assess the ability of a Bayesian hierarchical model to identify gastrointestinal AEFI in children, and then combine this with testing for effect modification and adjustments for confounding. Study Design: Reporting odds ratios were calculated for each gastrointestinal AEFI and vaccine combination. After testing for effect modification, these were then re-estimated using multivariable logistic regression adjusting for age, sex, year and country of report. A medically guided hierarchy of AEFI terms was then derived to allow information sharing in a Bayesian model. Setting: All spontaneous reports of AEFI in children under 18 years of age in the WHO VigiBase? (Uppsala Monitoring Centre, Uppsala, Sweden) before June 2010. Reports with missing age were included in the main analysis in a separate category and excluded in a subsequent sensitivity analysis. Exposures: The 15 most commonly prescribed childhood vaccinations, excluding influenza vaccines. Main Outcome Measures: All gastrointestinal AEFI coded by WHO Adverse Reaction Terminology. Results: A crude analysis identified 132 signals from 655 reported combinations of gastrointestinal AEFI. Adjusting for confounding by age, sex, year of report and country of report, where appropriate, reduced the number of signals identified to 88. The addition of a Bayesian hierarchical model identified four further signals and removed three. Effect modification by age and sex was identified for six vaccines for the outcomes of vomiting, nausea, diarrhoea and salivary gland enlargement. Conclusion: This study demonstrated a sequence of methods for routinely analysing spontaneous report databases that was easily understandable and reproducible. The combination of classical and Bayesian methods in this study help to focus the limited resources for hypothesis testing studies towards adverse events with the strongest support from the data.
PMID:
22136183
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/22136183