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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. Goals.
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Risk Factor Analysis (II) Presented by James M. Scanlan, Ph.D.U. of Washington Health Sciences CenterDept. of Psychiatry & Behavioral Sciences and Xiaowei Song, Ph.D. Dalhousie University Department of Medicine
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)
Examined all possible interactions between: • Age • Race • Apoe4 • Education • Main effects evident • NOInteractions
Solid: high education 3MS (at year 11) Dash: low education Blue=Caucasians, Red=African Americans Age groups (65-69, 70-74, 75-79, 80+)
Solid: Apoe- 3MS (at year 11) Dash: Apoe+ Blue=Caucasians, Red=African Americans Age groups (65-69, 70-74, 75-79, 80+)
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.
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.