varying) Cox


1.
?
2. Kruskal-Wallis Test
ANOVA
2. Friedman Test
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2. Logistic regression model
4. Poisson Regression Model
1. Mixed model
(Logistic Regression Model)→Odds Ratio (OR)
(Conditional Logistic Regression Model) →Matched-Pairs Odds Ratio (mOR)
Cox’s Proportional Hazard Model →
Hazard Ratio (HR)
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1) (Randomization) (experimental study)
2) (Restriction)
SMRSIR
(Conditional Logistic Regression Model)

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• (Censored data)
• Person-time information
• Time-varying covariates
Poisson regression
Logistic regression
• Fixed cohort
Kaplan-Meier survival analysis Log rank test


Baseline (
Cox’s Proportional Hazard Model
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Time-dependent HR
()
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Time-dependent risk factors for survival
A separate Cox analysis is carried out using the specific
value of the time-dependent variable at the beginning of
that specific time window
the analysis as one RR
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Cox model with Time-dependent covariates!!
Must NOT be an effect of the
exposure → Not an intermediate
intermediate
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1) Associated with disease (as a cause or a proxy for a cause but
NOT as an effect of the disease)
2) Associated with exposure (must be imbalanced across
exposure categories)
3) Must NOT be an effect of the exposure → Not an
intermediate
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Time-dependent Cox SAS Global Forum 2012
“Survival” Guide to Using TimeDependent Covariates Teresa M. Powell, MS and Melissa E. Bagnell, MPH
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HTN_1HTN_8 Time varying hypertension status
PROC PHREG DATA =SURVIVAL; CLASS RACE; MODEL time*censor(0) = sex marital race age weight htn_1/ TIES = EFRON RL; RUN;
(Teresa M. Powell, 2012)
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SAS
MODEL (start, stop)*censor2(0) = sex marital race age weight hyper/
TIES =EFRON RL;
Not designed to account for the competing risk of death
→They can overestimate risk of disease in elderly
individuals with high mortality.
(Sarah D., 2010)
Cox proportional with competing risk An Important Consideration in Studies of Older Adults (Sarah
D., 2010)

If the competing risk is low → <10%
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