Methods for estimating remission rates from cross-sectional survey data: application and validation using data from a national migraine study

Knowledge about remission rates can affect treatment decisions and facilitate etiologic discoveries. However, little is known about remission of many chronic episodic disorders, including migraine. This is partly due to the fact that medical records do not fully capture the history of these conditions, since patients might stop seeking care once they no longer have symptoms. For these disorders, remission rates would typically be obtained from prospective observational studies. Prospective studies of remission for chronic episodic conditions are rarely conducted, however, and suffer from many analytical challenges, such as outcome-dependent dropout. Here the authors propose an alternative approach that is appropriate for use with cross-sectional survey data in which reported age of onset was recorded. The authors estimated migraine remission rates using data from a 2004 national survey. They took a Bayesian approach and modeled sex- and age-specific remission rates as a function of incidence and prevalence. The authors found that remission rates were an increasing function of age and were similar for men and women. Follow-up survey data from migraine cases (2005) were used to validate the methods. The remission curves estimated from the validation data were very similar to the ones from the cross-sectional data.