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Activity Number:
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247
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #301094 |
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Title:
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Evaluating Vaccine Safety Data Using Markov Chains
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Author(s):
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Martin L. Lee*+ and Ken Zangwill and Joel Ward
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Companies:
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University of California, Los Angeles and University of California, Los Angeles
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Address:
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3941 Eureka Dr., Studio City, CA, 91604-3110,
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Keywords:
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vaccine safety ; Markov model ; chi-square tests
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Abstract:
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The analysis of vaccine safety data from population cohorts poses a number of issues. These include the rarity of adverse outcomes, nonexperimental data, and a complex correlation structure. Different approaches have been suggested including case-control and case-cohort designs. Others have used the "risk-interval" method, whereby vaccinees act as their own control and intervals before and/or after vaccination are compared with an at-risk period immediately postvaccination. We present an alternative approach which models the probability of events within these periods using a first-order Markov chain. This model is simple and provides chi-square tests of the relationship between vaccination and events of interest. It allows for determination of whether the Markov process is stationary and first order. We have applied these methods to a population-based dataset evaluating the relationship of gender to the incidence of adverse events following vaccination. We have demonstrated that our data fit this probability model and also determined associations that have been shown with more complex models using similar data. This model can be used for the evaluation of vaccine safety.
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