SurvMI (version 0.1.0)

CoxMI: Cox PH model with MI method

Description

CoxMI function estimated Cox model with uncertain endpoints by using MI method. Users have to provide survival data in a long format with rows for all potential events, together with corresponding event probabilities. The long format data should be transformed by the uc_data_transform function into a data list before feed into the function.

Usage

CoxMI(data_list,nMI=1000,covariates=NULL,id=NULL,...)

Arguments

data_list

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

nMI

Number of imputations (>1).

covariates

Vector of covariates on the RHS of Cox model. Categorical variables need to be encoded as factor variables before entering the model. This encoding has to be done before the data transform step.

id

Vector of id variable if Andersen-Gill model is required.

…

Other arguments passed on to coxph().

Value

est

Estimated vector of coefficients in the model

var

Estimated variance of the coefficients

betamat

Matrix containing estimate of coefficient 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 p-value

en

Expected events count - mean event count of imputed datasets

Details

Calculates the estimated parameters as in the usual Cox proportional hazards model when event uncertainties present. The data are assumed to consist of potential event times with probabilities or weights between 0 and 1 corresponding to the probability that an event occurred at each time.

References

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

See Also

Coxwt, CoxMI.summ.

Examples

# NOT RUN {
set.seed(128)
df_x<-data_sim(n=500,true_hr=0.8,haz_c=0.5/365)
df_x$f.trt<-as.factor(df_x$trt_long)
data_intrim<-uc_data_transform(data=df_x,
                               var_list=c("id_long","f.trt"),
                               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<-CoxMI(data_list=data_intrim,nMI=10,covariates=c("trt"))
CoxMI.summ(fit)

# }
# NOT RUN {
fit<-CoxMI(data_list=data_intrim,nMI=1000,covariates=c("trt"),id=c("id"))
CoxMI.summ(fit)
# }