LRMI: Log-rank test with events uncertainty

Description

This function conducts the Log-rank test with respect to uncertain endpoints, by MI or weighted method.

Usage

LRMI(data_list, nMI, covariates, strata = NULL,...)

Arguments

data_list

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

nMI

Number of imputation (>1). If missing, weighted statistics would be output instead.

covariates

The categorical variable used in the Log-rank test. No need to factorlize numeric variables.

strata

Strata variable may required by the Log-rank test

…

Other arguments passed on to survdiff().

Value

est

Estimated LR statistics, either from the MI method or weighted method

var

Estimated variance matrix

est_mat

Matrix containing estimate of statistics from each imputed dataset

Var_mat

Array containing variances for each imputed dataset

Between Var

Between imputation variance

Within Var

Mean within imputed dataset variance

nMI

Number of imputed datasets

pvalue

Estimated two-sided Chi-square test p-value

Degree of freedom

covariates

ngroup

Number of groups

obsmean

Mean of observed events count across imputations

expmean

Mean of expected events count across imputations

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]Klein JP, Moeschberger ML. Survival Analysis : Techniques for Censored and Truncated Data. New York: Springer; 1997.

[4]Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York: Wiley; 1987

Examples

Run this code

# 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")

#nMI=10 used in the example below to reduce the time needed
#but a large number as nMI=1000 is recommended in practice
fit<-LRMI(data_list=data_intrim,nMI=10,covariates=c("trt"),strata=NULL)
LRMI.summ(fit)
# }