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21 Cards in this Set

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 Regression models control for confounding using math models Choice of Regression (10-5) 4 chars of Proportional Hazards (Cox's) 1) Binary outcome 2) Variable follow-up 3) Start and end time knows for individuals 4) Assumptions are satisfied Effect estimate of Cox's exp(b) estimates the incidence rate ratio. 3 chars of Logistic Regression 1) Binary outcome 2) Fixed/defined follow-up 3) Binomial assumptions are satisfied Effect estimation of logistic regression exp(b) = odds ratio (risk ratio if disease is rare) 4 chars of Poisson Regression 1) Binary outcome 2) variable follow-up 3a) start and end times known for individuals or 3b) data stratified into mututally exclusive groups according to E and confounder, number of disease cases and follow-up time are knows for these strata 4) Rare disease (and other Poisson assumptions) Effect estimate of Poisson Regression exp(b) = incidence rate ratio 3 chars of Linear Regression 1) Continuous outcome (BP, LDL) 2) Followup is fixed/defined 3) Linear regression assumptions satisfied Effect estimation of Linear regression b, the estimated regression coefficient is a difference in mean values 7 Issues to consider for model construction 1) Model Type 2) Independence observations 3) Disease variable scale (continuous) 4) Covariate Definitions 5) Building model (selection) 6) Additivity 7) Unknowns Pitfalls for Model Type -logistic regression for variable follow-up -Assuming OR is RR when disease NOT rare Pitfalls for Independence observations -wrong unit analysis (BP rather than people_ -Ignores tight matching -time series/growth curve data Pitfalls for Disease variable scale (continuous) Need to log transform or other transform 3 issues of covariate definitions 1) Categorical vs. continuous 2) Categories 3) Scale (if continuous) Pitfalls for Categorical vs. Continuous Continuous covars when relationship nonlinear (should inspect data) Pitfalls for Categories Torturing the data (pick quintiles) Pitfalls for scale (if continuous) covariates w/ extreme variability (consider log-transform) Pitfalls for building models -automatic selection algorithm -colinearity (consider including demographics and known risk factors) Pitfalls for Additivity -effect modification Pitfalls for unknowns failure to consider