SurvMI (version 0.1.0)

Coxwt: Weighted Cox PH model estimation

Description

Estimate the Cox PH model by weighted partial likelihood. Event weights are calcualted with respect to event probabilities.

Usage

Coxwt(data_list,covariates,init=NULL,BS=FALSE,nBS=1000)

Arguments

data_list

The data list which has been transformed from the long format by the uc_data_transform function.

covariates

The vector of varaible on the RHS of the Cox model.

init

The initial value of covariates vector in the likelihood, length matches the length of covariates.

BS

T/F, whether conduct estimation via the Bootstrap method.

nBS

Number of BS, only effective if BS=TRUE.

Value

coefficients

Estimated vector of coefficients in the model

var

Estimated variance of the coefficients

hr

Estimated hazard ratios in the model

z

Wald test statistics

pvalue

Estimated two-sided p-value

coefficients_bs

Bootstrapped coefficient estimation

var_bs

Bootstrapped variance estimation

column_name

Column name

References

[1]Cook TD. Adjusting survival analysis for the presence of unadjudicated study events. Controlled clinical trials. 2000;21(3):208-222.

[2]Cook TD, Kosorok MR. Analysis of time-to-event data with incomplete event adjudication. Journal of the american statistical association. 2004;99(468):1140-1152.

[3]Snapinn SM. Survival analysis with uncertain endpoints. Biometrics. 1998;54(1):209-218.

See Also

CoxMI, Coxwt.summ.

Examples

# NOT RUN {
df_x<-data_sim(n=500,0.8,haz_c=0.5/365)
data_intrim<-uc_data_transform(data=df_x,
                               var_list=c("id_long","trt_long"),
                               var_list_new=c("id","trt"),
                               time="time_long",
                               prob="prob_long")
 fit<-Coxwt(data_list=data_intrim,covariates=c("trt"),init=c(1),BS=FALSE)
 Coxwt.summ(fit)

##an example if we would like to check the BS variance
# }
# NOT RUN {
fit2<-Coxwt(data_list=data_intrim,covariates=c("trt"),init=c(1),BS=TRUE, nBS = 100)
Coxwt.summ(fit2)
# }