LRMI {SurvMI}

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

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)