## Brief description

This web application is an implementation of sample size calculation methods for one sample non-parametric survival test/confidence interval (based on the Kaplan–Meier estimator) in JavaScript.

A large sample approximation to the variance of the Kaplan–Meier estimator, an exponential survival distribution, and a uniform entry over [0, accural time] are assumed.
For theoretical background, see Fleming & Harrington (1991) and Andersen, Borgan, Gill & Keiding (1993).
Moreover, various transformations for the survival function are supported.
Transformed confidence intervals perform better than the usual linear (the identity transformation) confidence intervals (Borgan & Liestøl, 1990).
The default is the log-log transformation according to the SAS/LIFETEST Procedure.
The well known SWOG's calculator (One Sample Nonparametric Survival) assume the log transformation.
Thereofre, if one use the SWOG's calculator, then one should use the log transformed confidence interval at analysis.
However, the SWOG's web site has not explicitly introduced this fact.
The required sample size and the performance depend on the method of the transformation.
One should be careful about it.
We submitted a research paper to a journal about this results.

## アプリ

## References

- Fleming TR, Harrington DP. Counting Processes and Survival Analysis. New York: Wiley, 1991, 236–237, Example 6.3.1.
- Andersen PK, Borgan Ø, Gill RD, Keiding N. Statistical Models Based on Counting Processes. New York: Springer-Verlag, 1993, 176–287, Section IV.1–3.
- Borgan Ø, Liestøl K. A note on confidence interval and bands for the survival curves based on transformations. Scandinavian Journal of Statistics 1990; 17(1): 35–41.

## Update history

- 2016/04/06 Translated to English
- 2016/03/21 Test release

## To cite this page

- Nagashima K. A sample size estimation tool for one sample non-parametric tests for a median survival time [Internet]. 2016 Mar 21 [cited 20XX YYY ZZ]; Available from: http://nshi.jp/en/js/onesurvmst/.