Influence of random measurement error on estimated rates of headache chronification and remission

To examine the potential influence of random measurement error on estimated rates of chronification and remission.

Studies of headache chronification and remission examine the proportion of headache sufferers that move across a boundary of 15 headache days per month between 2 points in time. At least part of that apparent movement may represent measurement error or random variation in headache activity over time.

A mathematical simulation was conducted to examine the influence of varying degrees of measurement error on rates of chronic migraine onset and remission. Using data from the American Migraine Prevalence and Prevention Study, we estimated a starting distribution of headache days from 0 to 30 in the migraine population. Assuming various levels of measurement error, we then simulated 2 sets of data for Time 1 and Time 2. The "individuals" in this study were assumed to have no real change in headache frequency from Time 1 to Time 2. The observed variations in headache frequency were those influenced by imputed random variance to resemble typical measurement error or natural variability. Using this simulation approach, we estimated the amount of chronification and remission rates that might be attributed simply to statistical artifacts such as unreliability or regression to the mean.

As the degree of measurement error increased, the amounts of illusory chronification and remission increased substantially. For example, if the headache frequency of sufferers randomly varies by only 2 headache days each month due to chance alone, a substantial degree of illusory chronification (0.6% to 1.3%) and illusory remission (10.3% to 23.5%) rates are expected simply due to random variation.

Random variation, without real change, has the potential to influence estimated rates of progression and remission in longitudinal migraine studies. The magnitude of random variation needed to fully reproduce observed rates of progression and remission are implausibly large. Recommendations are offered to improve estimation of rates of progression and remission, reducing the influence of random variation.