By Lyle D. Broemeling
Written through a biostatistics specialist with over two decades of expertise within the box, Bayesian equipment in Epidemiology offers statistical tools utilized in epidemiology from a Bayesian point of view. It employs the software program package deal WinBUGS to hold out the analyses and gives the code within the textual content and for obtain online.
The e-book examines examine designs that examine the organization among publicity to hazard components and the incidence of disorder. It covers introductory adjustment recommendations to match mortality among states and regression tips on how to learn the organization among quite a few danger components and disorder, together with logistic regression, uncomplicated and a number of linear regression, categorical/ordinal regression, and nonlinear versions. The textual content additionally introduces a Bayesian procedure for the estimation of survival via lifestyles tables and illustrates different methods to estimate survival, together with a parametric version in response to the Weibull distribution and the Cox proportional risks (nonparametric) version. utilizing Bayesian tips on how to estimate the lead time of the modality, the writer explains the right way to monitor for a disorder between participants that don't express any indicators of the sickness.
With many examples and end-of-chapter routines, this e-book is the 1st to introduce epidemiology from a Bayesian viewpoint. It indicates epidemiologists how those Bayesian versions and strategies are worthy in learning the organization among ailment and publicity to probability factors.
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Extra resources for Bayesian Methods in Epidemiology
The difference method to estimate lead time and the life table to compare survival between the study and control groups are unique to epidemiology, but will be interpreted with a Bayesian approach. Screening for chronic disease gained interest in the 1960s as a result of clinical experience that showed that disease detected at an earlier stage had better prognosis than disease detected at a later stage. Thus, interest in screening is based on the hypothesis that it would shift the diagnosis to an earlier stage and treatment would have a better change to make an impact on the development of diseases such as breast cancer.
Simple and multiple linear regression models are used in epidemiology to determine associations between a dependent and several independent variables and the subject is vast. If one refers to the latest issue of the American Journal of Epidemiology, one will mostly likely find a regression analysis that is employed to find some type of association between disease and various risk factors and cofounders. The models considered in this chapter differ from the logistic model in that the dependent variable is quantitative.
The presentation of regression analysis is initiated with the definition of a simple linear regression model, which has one dependent variable and one independent variable, with the goal being to establish an association between the two. For example, the dependent variable might be systolic blood pressure, with the independent variable indicating two groups, where the subjects are with and without coronary artery disease. Simple linear regression has very strict assumptions, such as the dependent variable must be normally distributed, and the variance of the dependent variable must be constant over all values of the independent variable.