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