1. Is “Dead” the same as “Missing”?
Brenda Kurland, PhD
NACC Biostatistician
ADC Data Core Leaders Meeting
October 18, 2003

2. Outline Introduce targets of inference in regression models
Two examples of longitudinal data
Data missing due to dropout
Data recorded truncated by death
Having the right covariates in a regression model doesn’t guarantee having the right target of inference
Introduce topic in terms of regression models
Two examples – both longitudinal data, almost identical analysis approach. Yet what I’d argue to be the desired target of inference is different
In summary, discuss how subtle these assumptions are when we fit regression models.

3. Targets of Inference Sampling influences interpretation
Cognitive functioning was maintained, in the absence of disease
Treatment was effective, for those who followed the protocol
Deterioration was faster in African Americans than in whites, for ADC-registered patients
We report exclusions of enrolled subjects, but what conditioning is implicit?
Here’s a regression model. It’s about as straightforward as can be – an intercept and a single covariate. Only one Greek letter is used. The two subscripts i and j are only needed to remind us that the data are longitudinal. However, depending on the variables examined, sampling criteria, and exclusions, the interpretations can vary greatly for this simple model.
(Sometimes you didn’t fit the model you thought.)

4. Missing Data (Dropout) Example
Randomized Clinical Trial of 3 schizophrenia treatments
Response variable – Positive and Negative Syndrome Scale (PANSS)
8 weeks of treatment (after washout), PANSS at 0,1,2,4,6,8 weeks

5. Missing Data (Dropout) Example
Considerable dropout in all three treatment groups

6. Missing Data (Dropout) Fitted Models
Cross-sectional means
E(PANSS|observed)
Have different people at each timepoint
Linear mixed model
E(PANSS)
No effect of placebo
NACC
National Alzheimer’s Coordinating Center

7. Truncation Due to Death Example
NACC Minimum Data Set (MDS)
Mini-Mental State Examination (MMSE) for 500 living, 500 deceased
Criteria
At least 3 MMSE (2 if deceased)
30 days – 2 years between MMSE
MMSE within 1 year of death if deceased

8. Truncation Due to Death Fitted Models
GEE with indep. corr. (linear regression)
Random intercept
Random intercept and slope

9. Truncation Due to Death Fitted Models
GEE
E(MMSE|alive)
Closest to observed means
Linear Mixed Models
E(MMSE)
Resurrect deceased subjects
NACC
National Alzheimer’s Coordinating Center

10. Targets of Inference: Summary
Sometimes you want to condition, sometimes you don’t
Can’t always tell what you’re conditioning on
Slippery slope
Example: is there decline for “normal” elderly?
AD, makes sense to exclude
MCI, exclude?
Preclinical MCI?…
Don’t want to condition on being alive if the predictor (?) affects survival – example, Paula’s strokes. Conditioning on being alive, perceived health may be better soon after the stroke compared to before……
I think most clinical trials look at survival first, then ask about QOL or whatever- and those should condition. Laura Lee does in one step.
Preclinical phase a problem

11. Targets of Inference: Summary
“Dead” is not the same as “Missing”
Think about desired target of inference
Look at models of longitudinal data that ignore correlation among responses
Check functional form (i.e., linearity) for the regression model you will interpret