LRMI: Log-rank test with events uncertainty

In SurvMI: Multiple Imputation Method in Survival Analysis

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
df	Degree of freedom
covariates	covariates
ngroup	Number of groups
obsmean	Mean of observed events count across imputations
expmean	Mean of expected events count across imputations

Author(s)

Yiming Chen

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

See Also

uc_data_transform, LRMI.summ

Examples

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)

SurvMI documentation built on July 13, 2020, 9:07 a.m.