MIICD (version 2.1)

MI.ci: Cumulative incidence estimation for interval censored competing risks data using multiple imputation

Description

Uses multiple imputation to compute a cumulative incidence function for interval censored competing risks data

Usage

MI.ci(m, status, trans, data, conf.int = TRUE, cens.code, alpha = 0.05,
  ntimes = NULL)

Arguments

m

An integer, indicates the number of imputation to perform at each iteration

status

The name of the column where status are to be found

trans

Denomination of the event of interest in the status column

data

The input data (see details)

conf.int

Logical, computes the confidence interval

cens.code

Censor indicator in the status column of the data

alpha

Parametrize the confidence interval width

ntimes

Number of time points where the estimates should be computed (experimental)

Value

• est A data frame with estimates

  ... Other objects

Details

This function uses the multiple imputation approach to estimate the cumulative incidence function for interval censored competing risks data.

Estimates are computed using Rubin's rules (Rubin (1987)). Estimate of the cumulative incidence is computed as the mean of cumulative incidences over imputations. The variance is computed at each point by combining the within imputation variance and the between imputation variance augmented by an inflation factor to take into account the finite number of imputation. If conf.inf is required, the log-log transformation is used to compute the lower confidence interval.

Print and plot methods are available to handle results.

The data must contain at last three columns: left, right and status. For interval censored data, the left and right columns indicates the lower and the upper bounds of the intervals respectively. Inf in the right column stands for right censored observations. When an observation is right censored, the status column must contain the censor indicator specified by cens.code. The transition of interest must be specified by the trans parameter.

References

PAN, Wei. A Multiple Imputation Approach to Cox Regression with Interval-Censored Data. Biometrics, 2000, vol. 56, no 1, p. 199-203.

Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys.

Schenker, N. and Welsh, A. (1988). Asymptotic results for multiple imputation. The Annals of Statistics pages 1550-1566.

Tanner, M. A. and Wong, W. H. (1987). An application of imputation to an estimation problem in grouped lifetime analysis. Technometrics 29, 23-32.

Wei, G. C., & Tanner, M. A. (1991). Applications of multiple imputation to the analysis of censored regression data. Biometrics, 47(4), 1297-1309.

See Also

Surv, survfit

Examples

res <- MI.ci(m = 10, status = 'status', trans = 1, data = ICCRD,
 conf.int = TRUE, cens.code = 0, alpha = 0.05)
 res
 plot(res)