For health plan data, there are a few different analysis options. For early-onset events, a self-controlled risk interval design can be used. First, decide on a risk window, such as 1 to 21 days after vaccination, and a control window, such as 22 to 42 days after vaccination. For each vaccinated child in the age group of interest, count how many of them had an adverse event in the risk and control windows, respectively. Suppose that the two windows are of the same length and that there are a total of n adverse events in the two windows combined. The number of adverse events in the risk window then has a binomial distribution with parameters n and p =½.
Since this is a self-controlled analysis, it is only time-varying confounders that may need to be adjusted for, and there is no need to worry about gender, genetics, stable environmental factors, study site, etc. For some adverse events where the incidence rate changes rapidly from one week of age to the next, an age adjustment must be made. If the age distribution of the disease is know, this can easily be done by use of an offset term in a logistic regression model. The same is true if there are strong seasonal trends in the incidence rate. An alternative way to adjust for seasonality is to use a case-centered approach, as proposed by Fireman et al. (2009).
The choice of risk and control windows depends on the vaccine and the adverse event. Sometimes, it is worthwhile to have a washout period between the two windows. To avoid day-of-week effects, the two windows should have the same number of days in any modulus of seven. For example, the risk window may be 1 to 14 days and the control window 22 to 70 days or the risk window may be 1 to 2 days and the control window days 8 to 9 together with days 15 to 16. Theoretically, it is also possible to use a comparison window before vaccination, but that can introduce confounding by indication or contraindication.
In VAERS data, the age of the vaccinated child is one of the variables collected. To evaluate whether a vaccine is safe outside the recommended schedule, it is hence possible to look at specific predefined age groups. This can be done by the same methods that are used for all age groups combined, such as proportional reporting ratios (Evans et al., 2001).