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Hence, a lower deviance indicates a higher likelihood and better fit to the data.
Deviance is a measure of how well the statistical model describes the observed data, with a lower deviance implying a better fit (see Appendix for details).
The prediction region of the model with the cloglog link contains all observed data points, while not extending far beyond the studies with lowest observed Se and Sp. This is reflected in a lower deviance information criterion (DIC), a measure of model fit, for the cloglog model formulation compared to the logit formulation.
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A significantly lower deviance from a model with a lag time of k years means that using the smoothed pattern of residential locations from k years ago significantly explains overall NHL risk.
A model with lower deviance provides a better fit to the data and hence will be preferred.
Predictions of the ERC based on a natural spline model and a linear model are presented in Figure 2. The lower deviance of the natural spline (deviance = 25.84, 27 df) compared with that of the linear model (deviance = 29.25, 28 df) suggests a slightly better fit [chi square test (1 df), p = 0.06].
The model with the offset has a lower global deviance then the model without the offset but the difference is not large.
Lower deviance and AIC mean better fit to data.
The model with lower deviance information criterion (DIC) was preferred.
Model I provided a better fit to the data compared to the conditional independence model, which is indicated by its much lower deviance = 2974.3.
For each outcome, fixed and random effects models were evaluated, and the better fitting model was selected based on the lower Deviance Information Criterion value [ 31].
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