Use of Logistic Regression to Combine Two Causalit

Authors: Lionel Van Holle Vincent Bauchau

Publish Date: 2014/11/14

Volume: 37, Issue: 12, Pages: 1047-1057

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Abstract

We evaluated the use of logistic regression to model the probabilities of spontaneously reported vaccine–event pairs being adverse reactions following immunization ARFI using disproportionality and unexpectedness of timetoonset TTO distributions as predictive variables and the presence of events in the global product information as a dependent variableWe used spontaneous reports of adverse events from eight vaccines and their labels as proxies for ARFIs Three logistic regressions were built to predict ARFIs based on different combinations of the proportional reporting ratio PRR disproportionality measure and two Kolmogorov–Smirnov KS tests ‘between vaccines’ and the ‘between events’ of TTO distribution model 1 using the PRR estimate and its 95  lower confidence interval CI limit model 2 using the p values of the two KS tests and model 3 using the PRR point estimate and lower CI limit and both KS tests The performance of the regressions model fit statistics calibration and discrimination was measured on 100 bootstrap samplesLogistic regression allows estimation of the probability of a vaccine–event pair being an ARFI using two causality criteria at the population level assessed in spontaneous report data the strength of association disproportionality measure and temporality TTO distribution tests Logistic regression combines and weights these causality criteria based on their respective ability to predict known safety issuesThe unexpectedness of the timetoonset distribution for a given vaccine–event pair when compared with the timetoonset distribution of the same event reported following exposure to other vaccines appeared to be best predictor of the reported event being a known safety issueLogistic regression offers a framework in which quantified causality criteria can be combined to evaluate the probability of a vaccine–event pair being an adverse reaction following immunization based on our existing knowledge of vaccine safety profiles

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