title: Poisson regression models outperform the geometrical model in estimating the peak-to-trough ratio of seasonal variation: a simulation study.
abstract: BACKGROUND
  Several statistical methods of assessing seasonal variation are available. Brookhart and Rothman [3] proposed a second-order moment-based estimator based on the geometrical model derived by Edwards [1], and reported that this estimator is superior in estimating the peak-to-trough ratio of seasonal variation compared with Edwards' estimator with respect to bias and mean squared error. Alternatively, seasonal variation may be modelled using a Poisson regression model, which provides flexibility in modelling the pattern of seasonal variation and adjustments for covariates.
  
  
  METHODS
  Based on a Monte Carlo simulation study three estimators, one based on the geometrical model, and two based on log-linear Poisson regression models, were evaluated in regards to bias and standard deviation (SD). We evaluated the estimators on data simulated according to schemes varying in seasonal variation and presence of a secular trend. All methods and analyses in this paper are available in the R package Peak2Trough[13].
  
  
  RESULTS
  Applying a Poisson regression model resulted in lower absolute bias and SD for data simulated according to the corresponding model assumptions. Poisson regression models had lower bias and SD for data simulated to deviate from the corresponding model assumptions than the geometrical model.
  
  
  CONCLUSIONS
  This simulation study encourages the use of Poisson regression models in estimating the peak-to-trough ratio of seasonal variation as opposed to the geometrical model.
authors: Department of Cardiology, Center for Cardiovascular Research, Aalborg Hospital, Aarhus University Hospital, Sdr. Skovvej 15, DK-9000 Aalborg, Denmark. anluc@rn.dk
authors: A L
authors: AL
authors: Christensen
authors: S
authors: S
authors: Lundbye-Christensen
authors: C
authors: C
authors: Dethlefsen
mesh_terms: D008962:Models, Theoretical; D009010:Monte Carlo Method; D016012:Poisson Distribution; D012044:Regression Analysis; D012621:Seasons