Weighted Conditional Logistic Regression for CXO study

Usage

CXO_wt(data, exposure, event, Id, tvc = NULL)

Arguments

data: input dataframe
exposure: name of exposure variable
event: name of outcome variable
Id: person ID
tvc: name of time-varying confounder (optional)

Value

      weighted regression object from clogit

Examples

data(cases)
cfit <- CXO_wt(data=cases, exposure=ex, event=Event, Id=Id)
summary(cfit)
#> Call:
#> coxph(formula = Surv(rep(1, 24840L), case_period) ~ ex + strata(Id) + 
#>     offset(lw), data = cases_wt, method = "efron")
#> 
#>   n= 24840, number of events= 276 
#> 
#>       coef exp(coef) se(coef)     z Pr(>|z|)   
#> ex1 0.3363    1.3997   0.1215 2.767  0.00566 **
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#>     exp(coef) exp(-coef) lower .95 upper .95
#> ex1       1.4     0.7144     1.103     1.776
#> 
#> Concordance= 0.563  (se = 0.015 )
#> Likelihood ratio test= 7.74  on 1 df,   p=0.005
#> Wald test            = 7.65  on 1 df,   p=0.006
#> Score (logrank) test = 7.73  on 1 df,   p=0.005
#>