of causality assessments but also to make those assessments more accountable (Hutchinson and Lane, 1989). In other words, it is easier to see how the assessment methods were used to reach the conclusions. Most algorithms are presented in the form of a flowchart or a questionnaire, which asks a series of questions and assigns a score on the basis of the assessor's answers to those questions. The score is then used to assign a categorical probability rating such as definite, probable, possible, or unlikely.
The third approach is Bayesian analysis (Lane et al., 1987). It is based on Bayes' theorem and calculates the posterior probability of vaccine causation (the probability that the event was caused by the vaccine) from estimates of the prior probability (the probability that the vaccine caused the adverse event prior to observing the particular facts of the individual case) and a series of likelihood ratios for each pertinent element of the observed case. Each likelihood ratio is calculated by dividing the probability of observing what actually occurred, under the hypothesis that the vaccine was the cause, by the probability of observing the same occurrence given nonvaccine causation. The Bayesian approach not only provides a direct estimate of the Did It? probability for a given case but it is also accountable in terms of documenting the component estimates that go into calculating the posterior probability. The prior probability relates to the first two headings of information from cases reported above and is often based on epidemiologic data, when available, whereas individual case information is used to construct the likelihood ratios for the third through seventh headings. Full Bayesian analyses are often complicated and time-consuming. Moreover, because the data necessary to estimate the component prior probabilities and likelihood ratios may be unavailable, quantitative expression of the assessor's uncertainty is often highly subjective, even if based on expert opinion.
In evaluating the case reports available to the committee, the committee adopted an informal Bayesian approach. The main elements of the case reports used in the committee's assessments included the individual's medical history, the timing of onset of the adverse event following vaccine administration, specific characteristics of the adverse event, and follow-up information concerning its evolution. Each relevant piece of case information was assessed for its strength of evidence for vaccine versus nonvaccine causation. When such information (particularly concerning timing) was unavailable, the committee usually found it difficult or impossible to infer causality for that case.
The individual's medical history was taken into account in considering the role of alternative etiologic candidates (which affects the prior probability of vaccine causation). For example, a history of abnormal neurologic development or seizures prior to receipt of a vaccine reduces the probability that the encephalopathy or residual seizure disorder that developed after vaccination was caused by the vaccine.