Overview of Recent Developments for Interval-Censored Data - Interval-Censored-Time-to-Event Data: Methods and Applications - page 22

(Turnbull (1976)). Although several procedures have been developed for the

one-sample estimation problem, there does not exist much literature on the
topic. The same is true for the investigation of the use of parametric models

and inference procedures for the analysis of interval-censored data. One ma-

jor reason for this is that in most situations, there does not exist much prior

information about the variable under study, and thus one may prefer nonpara-

metric or semiparametric approaches rather than parametric approaches.

The implementation of the available inference procedures is clearly im-

portant and, for this, an essential part is the availability of some software

packages. Although there exist some functions or a toolbox in R and SAS,

there is no commercially available statistical software yet that provides an

extensive coverage for interval-censored data. This is perhaps due to the com-

plexity of both the algorithms and the theory behind it. Chapters 13 and 14

discuss two R packages for nonparametric comparison of survival functions

based on interval-censored data.

Bibliography

Anderson, P. K., Borgan, O., Gill, R. D., and Keiding, N. (1995). Statistical

Models Based on Counting Processes. New York: Springer.

Barrett, J. K., Siannis, F., and Farewell, V. T. (2011). A semi-competing
risks model for data with interval-censoring and informative observation:

An application to the mrc cognitive function and ageing study. Statistics

in Medicine 30, 1{10.

Betensky, R. A. (2000). On nonidentifiability and noninformative censoring

for current status data. Biometrika 87, 218{221.

Chen, B., Yi, G. Y., and Cook, R. J. (2010). Analysis of interval-censored