Estimated birthweight-specific mortality curves for the 4 components are in dashes.
We also estimated birthweight-specific mortality curves for the primary and secondary distributions in a 2-component normal mixture [ 6].
Fourth, we use the N rep sets of estimated birthweight-specific mortality curves to create overall estimates of the risk functions and accompanying confidence bounds, as described below.
The model-implied mortality curve, a weighted average of estimated birthweight-specific mortality curves for the four components, is in solid.
Third, we apply PMLR to the birthweight and mortality outcome data in each sample, which yields estimated birthweight-specific mortality curves for that sample.
Let r ∧ j ; 1 (x ), r ∧ j ; 2 (x ),..., r ∧ j ; N r e p (x ) denote the estimated birthweight-specific mortality curves for component j (1 <= j <= k ) originating from the N rep samples.
Figure 1d reveals considerable uncertainty in estimating birthweight-specific mortality, especially in the HBW range.
Here we consider estimating birthweight-specific mortality curves within each component of the normal mixture model in Equation (1).
We used the same data set to estimate birthweight-specific mortality curves for the lower residual and predominant distributions in a contaminated normal model [ 5].
The second paper in our two-part series reveals that, when coupled with methodology for estimating birthweight-specific mortality curves within each component, this paper's approach to describing a birthweight distribution can also reveal heterogeneity in mortality.
