Comput Methods Programs Biomed - Poisson regression models outperform the geometrical model in estimating the peak-to-trough ratio of seasonal variation: a simulation study.

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Resumo

TRODUCTION: 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.METHOD: 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.CONCLUSION: 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.

Resumo Limpo

troduct sever statist method assess season variat avail brookhart rothman propos secondord momentbas estim base geometr model deriv edward report estim superior estim peaktotrough ratio season variat compar edward estim respect bias mean squar error altern season variat may model use poisson regress model provid flexibl model pattern season variat adjust covariatesmethod base mont carlo simul studi three estim one base geometr model two base loglinear poisson regress model evalu regard bias standard deviat sd evalu estim data simul accord scheme vari season variat presenc secular trend method analys paper avail r packag peaktroughresult appli poisson regress model result lower absolut bias sd data simul accord correspond model assumpt poisson regress model lower bias sd data simul deviat correspond model assumpt geometr modelconclus simul studi encourag use poisson regress model estim peaktotrough ratio season variat oppos geometr model

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