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

In MIICD: Multiple Imputation for Interval Censored Data

Description

Uses multiple imputation schemes to compute the survival function when data are interval censored

Usage

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

Arguments

k	An integer, indicates the number of iteration to perform
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

Details

This function uses multiple imputation aproach to estimate the survival function when data are interval censored. Estimates are #' computed using Rubin's rules (Rubin (1987)). The survival 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. At each iteration, the survival function is updated and multiple imputation is performed using the updated estimate. 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 right columns indicate lower and upper bounds of intervals, respectively. Inf in the right column stands for right censored observations

Value

est A data frame with estimates

Author(s)

Marc Delord <[e-mail]>

References

Delord, M. & Genin, E. Multiple Imputation for Competing Risks Regression with Interval Censored Data Journal of Statistical Computation and Simulation, 2015

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(k = 5 , m = 5 , data = ICCRD , conf.int = TRUE , alpha = 0.05 )
 res
 plot(res)

Example output

Survival estimate for interval censored data using data augmentation and multiple imputation

Call:
NULL

Interval-censored response for survival estimate:

No.Observation: 150 

Patern:

                   type
Cause               exact interval-censored right-censored
  event of interest    37                98              0
  right-censored        0                 0             15

$est
A 56 x 6 data frame of required estimates
        time      surv          sd         uci       lci        NA
1 0.00000000 1.0000000 0.000000000 0.000000000 1.0000000 1.0000000
3 0.02220189 0.9986577 0.004445050 0.004445050 1.0000000 0.9899456
4 0.12676875 0.9959732 0.007811316 0.007811316 0.9999831 0.9806633
5 0.21379481 0.9919463 0.010879771 0.010879771 0.9997636 0.9706223
6 0.37316291 0.9865772 0.013045923 0.013045923 0.9987766 0.9610076
7 0.45597592 0.9865772 0.020071069 0.020071069 0.9997981 0.9472386

MIICD documentation built on May 2, 2019, 11:01 a.m.