1. Risk Factor Analysis (II)
Presented by
James M. Scanlan, Ph.D. U. of Washington Health Sciences Center Dept. of Psychiatry & Behavioral Sciences
and
Xiaowei Song, Ph.D.
Dalhousie University
Department of Medicine

2. Goals
Examine combinations of major risk factors in regressions predicting 3MS in last year of study
Include combined CHD/health risk index
Examine results for possible interactions
Controlling for baseline 3MS scores, which variables are most predictive of final 3MS ( thus predictive of decline rather than lower but constant 3MS values)

3. Index using 5 Risk Factor Items

4. Examined all possible interactions between:
Age
Race
Apoe4
Education
Main effects evident
NO Interactions

7. 3MS (at year 11) Age groups (65-69, 70-74, 75-79, 80+)
Solid: high education
3MS (at year 11)
Dash: low education
Blue=Caucasians, Red=African Americans
Age groups (65-69, 70-74, 75-79, 80+)

8. Solid: Apoe- 3MS (at year 11) Dash: Apoe+
Blue=Caucasians, Red=African Americans
Age groups (65-69, 70-74, 75-79, 80+)

9. Solid: low risk index 3MS (at year 11) Dash: high risk index
Blue=Caucasians, Red=African Americans
Age groups (65-69, 70-74, 75-79, 80+)
Two possible reasons about why the risk factor index did not look very good in this plot:
1. Only 5 risk items are available. 2. The precision is lowered down by dichotomization.

10. Summary
Age, education, race, Apoe4 and CHD risk index influence 3MS results in regression
Age, education and ethnicity first three variables in regression uncontrolled for initial 3MS value
Main effects, but no interactions evident
When initial 3MS score is controlled, age, CHD risk and education remain significant predictors of final 3MS score, but ethnicity is not.
Results suggest that black ethnicity may influence initial starting 3MS score, but not necessarily the course of decline or change in 3MS scores.