unit 8 pt 2

# unit 8 pt 2 - 5. Multivariate analysis: Analysis Unit 8:...

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Unit 8: Confounding and effect (measure) and effect (measure) modification Part 2 Dr. Borenstein 5. Multivariate analysis: Analysis ± Stratified analysis does not allow for simultaneous adjustment of many variables because cell sizes get too small and are unstable ± Most often, we adjust for many confounders at once ± Variables such as age, gender, race, and education are often incorporated into a model with the exposure to establish the independent effect of the exposure after adjusting for other variables Multivariate analysis is a tool to adjust for multiple ± Multivariate analysis is a tool to adjust for multiple variables simultaneously. Multivariate analysis, con’d ± Involves modeling the relation between E and D by means of a mathematical equation ± In the equation, the outcome (D) is considered the dependent variable ± The exposure(s) of interest, other strong risk factors for the D and one or more PCF are considered independent (predictor, explanatory) variables. Before including a variable in a Before including a variable in a model, ± We conduct stratified PCF-E and PCF-D and also stratified M-H crude and adjusted analysis to discern if each variable should be introduced into the mathematical model. Multivariate analysis equation Multivariate analysis equation (general form) Y = α + β 1 X 1 + β 2 X 2 + . . . + β n X n ++ X X +… + γ X 1 X 2 β =coefficients from mathematical model X=independent variable Y=outcome (state you wish to predict) Outcome = intercept + exposure (X 1 )+ confounder (X 2 ) + … + confounder(X n ) + + interaction term (effec modification) for + … + interaction term (effect-modification) for exposure X1 * confounder X2 Multiple regression model ± Outcome = continuous (BP cholesterol level Ab titers Y = α + β 1 X 1 + β 2 X 2 + . . . + β n X n Outcome = continuous (BP, cholesterol level, Ab titers) ± The coefficients of each independent (X) variable are directly interpretable in relation to the outcome ± The presence of each variable in the model means that we know their relation to Y independent of the other variables ± Commonly used for cross-sectional data, cohort data looking at baseline or longitudinal change (also incorporates random effects) ± Many variations of the multiple regression model

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Logistic regression model ± Outcome = natural log odds of disease Outcome natural log odds of disease ± Log (odds of disease)= α + β 1 X 1 + β 2 X 2 +..+ β n X n ± Coefficients ( β ) can be directly converted to the odds ratio, which describes the independent association of that parameter with the outcome ± Commonly used for case-control (frequency or unmatched) and cross-sectional data. Can be used for cohort data if time to event is not used for cohort data if time to event is not important and can adjust for follow-up time.
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## This note was uploaded on 07/15/2011 for the course PHC 6000 taught by Professor Staff during the Summer '08 term at University of South Florida.

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unit 8 pt 2 - 5. Multivariate analysis: Analysis Unit 8:...

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