MI.surv(k, m, data, conf.int = FALSE, alpha = 0.05)est A data frame with estimatesThis 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
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.
res<-MI.surv(k = 5 , m = 5 , data = ICCRD , conf.int = TRUE , alpha = 0.05 )
res
plot(res)
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