Assessing seasonality in clinical research

Abstract

Abstract Background: Seasonal patterns are assumed in many fields of medicine. However, biological processes are full of variations and the possibility of chance findings can often not be ruled out. Methods: Using simulated data we assess whether autocorrelation is helpful to minimize chance findings and test to support the presence of seasonality. Results: Autocorrelation required to cut time curves into pieces. These pieces were compared with one another using linear regression analysis. Four examples with imperfect data are given. In spite of substantial differences in the data between the first and second year of observation, and in spite of otherwise inconsistent patterns, significant positive autocorrelations were constantly demonstrated with correlation coefficients around 0.40 (SE 0.14). Conclusions: Our data suggest that autocorrelation is helpful to support the presence of seasonality of disease, and that it does so even with imperfect data.