Penalized Methods as Universal Tools for Data Analysis
Lab 2. Data augmentation – Offset method on summarized data
Consider the association between chocolate consumption and risk of stroke in a
prospective cohort of middle-aged and elderly men (Larsson et al. Neurology, 2012).
The multivariable relative risk of stroke comparing the highest quartile of chocolate
consumption (median 62.9 g/week) with the lowest quartile (median 0 g/week) was
0.83 (95% CI 0.70–0.99). Given that the means must be higher than the medians, the
RRs at issue are for roughly 70g (2.5 oz) per week.
The two-way table corresponding to the multivariable adjusted RR and 95% CI is the
following.
. iri 236 279 46068 45163
|
Exposed
Unexposed |
Total
-----------------+------------------------+-----------Cases |
236
279 |
515
Person-time |
46068
45163 |
91231
-----------------+------------------------+-----------|
|
Incidence rate | .0051229
.0061776 |
.005645
a) Poisson regression on summarized data
Create a dataset from the two-way table above and fit a Poisson regression model
to estimate the exposure-disease relative risk.
Greenland S., Orsini N., IMM, KI, Sept 16-17, 2013
1
b) Poisson regression on augmented data with a null centre prior
Enter the data corresponding to a prior with 0.95 probability on RR between
0.8 and 1.25.
Fit a Poisson regression model on augmented data. Is the posterior relative risk
similar to information-weighted averaging?
c) Poisson regression on augmented data with a non-null centre prior
The pooled relative risk of stroke for approximately 70 gr per week of
chocolate consumption was 0.707 (95% CI 0.565–0.885). Consider the results
of this meta-analysis to inform the prior and augment the observed data.
Fit a Poisson regression model on augmented data.
Greenland S., Orsini N., IMM, KI, Sept 16-17, 2013
2