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Details of talk

TitleDevelopment, validation and calibration of mortality risk prediction models: results from 123,697 middle-aged and older Australian men
PresenterGrace Joshy (Australian National University)
Author(s)Grace Joshy, Emily Banks, Anthony Lowe, Rory Wolfe, Leonie Tickle, Bruce Armstrong, Mark Clements

Evidence-based guidelines to target preventive services or treatment often
require knowledge of a personís predicted risk of death. However, robust
prediction models to identify individuals, especially middle-aged and older men,
are lacking. Further, the methods for calibrating predictions and transferring
results from cohort studies to populations are under-developed. We aimed to
develop methods and validate a prediction score for 5-year mortality. Using 45
and Up Study questionnaire data linked to death data, associations of all-cause
mortality with 40 health measures were assessed. Multiple imputations by chained
equations were used. Prediction models were validated internally using 10-fold
cross-validation and survival estimated calibrated to the Australian population
using Australian Health Survey (AHS) data. For re-calibration, we compared (i)
the marginal predicted risk integrated across exposure distribution with (ii)
the observed age-specific all-cause mortality. Of 123,697 men aged $\ge$ 45
years at baseline, 12,160 died during a median follow-up of 5.9 years. 
Following age-adjustment, self-reported health was the strongest predictor of
all-cause mortality (C-index=0∑827, 95\%CI 0.824-0.831). Three prediction models
for all-cause mortality were validated, with predictors: (i) age group and
self-rated health; (ii) variables common to the 45 and Up Study and the AHS; and
(iii) all variables selected using stepwise regression. Models calibrated well
with observed all-cause mortality rates. Moreover, we found theoretical and
empirical evidence for bias in published re-calibration methods, including in
one which centres the exposure measures. Calibrated estimates with life tables
could be used to predict mortality risks.