MIICD (version 2.1)

MI.surv: Survival estimation for interval censored data using multiple imputation

Description

Uses multiple imputation to compute the survival function with interval censored data

Usage

MI.surv(m, data, conf.int = TRUE, alpha = 0.05)

Arguments

m

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

data

The input data (see details)

conf.int

Logical, computes the confidence interval

alpha

Parametrize the confidence interval width

Value

• est A data frame with estimates

Details

This function uses the multiple imputation aproach to estimate the survival function for interval censored data.

Estimates arecomputed using Rubin's rules (Rubin (1987)). Survival function estimate is computed as the mean of survival 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 two columns: left and right. For interval censored data, the left and the right columns indicates lower and upper bounds of intervals respectively. Inf in the right column stands for right censored observations.

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.surv( m = 10 , data = ICCRD , conf.int = TRUE )
  res
  plot( res )
  #plot( res , fun = 'event')