Louisa Smith
SER: June 14, 2023
Department of Health Sciences
The Roux Institute
Northeastern University
Frailty is a syndrome of vulnerability more common in older adults
A frailty index is a quantitative measure of the aggregate burden of age-related health deficits
FI = # of deficits / # of possible deficits
9.8% of 200,000+ participants had complete data
38% had data for >80% of deficits (>27/33)
Complete-case
Exclude those with any missing items
Proration
Adjust denominator (person-mean imputation)
Multiple imputation
Of individual items / total score
Throwing away a lot of data, strong assumptions
Different weighting across domains
Computationally intensive, still requires assumptions
Model how the distribution of missing data depends on missingness pattern
For example, a missingness pattern in which a given deficit is missing may be associated with a higher probability of that deficit
Can’t tell from the observed data – by definition we are missing the item in that missingness pattern
A simple model for a single variable with missingness:
E[Y∣R,X]=β0+β1X+δI(R=r0)
where δ parameterizes how much different the distribution (expectation) of Y is in observations with missing data patterns where it is missing (r0)
The delta adjustment approach can be done in the context of multiple imputation, e.g., with MICE
With multiple missing variables, interpretation of sensitivity parameter δ is different
For a given item Y, we collapsed missingness patterns into:
Most items are binary
Standardized means seem more interpretable
Synthetic AoU dataset
Observations with missing data are quite different, but it’s not clear that reasonable non-random missingness makes any difference
Thanks to Chelsea Wong MD, Ariela Orkaby MD, Brianne Olivieri-Mui PhD