Calculating the longitudinal extension of the average attributable fraction (LE-AAF) for many risk factors (RFs) requires a two-stage computational process using only those combinations of RFs observed in the dataset. We first screen candidates RFs in a Cox Model, and assuming piecewise constant hazards, use pooled logistic regression to model the probability of death as a function of combinations of selected RFs. We average the iterative differencing of the attributable fractions calculated for all overlapping subsets of co-occurring RFs to obtain a LE-AAF for each RF that is additive and symmetrical. We illustrate by partitioning the additive proportions of death from 10 different groupings of acute and chronic diseases, on a national sample of older persons from the US (Medicare Beneficiary Survey) over a 4-year period and compare with results reported by the National Center for Healthcare Statistics. We conclude that careful screening of RFs with analysis restricted to extant combinations greatly reduces computational burden. LE-AAF accounted for a cumulative total of 66% of the deaths in our sample, compared with the 83% accounted for by the National Center for Healthcare Statistics.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Average attributable fraction
- Cause of death
- Cox regression
- High dimension
- Logistic regression