Effective Sample Size for the Kaplan-Meier Estimator: A Valuable Measure of Uncertainty?

Toby Hackmann, Doranne Thomassen, Anne M. Stiggelbout, Saskia le Cessie, Hein Putter, Liesbeth C. de Wreede and Ewout W. Steyerberg

The American Statistician, 2026, vol. 80, issue 1, 100-108

Abstract: Sample size is an essential indicator of the uncertainty in clinical research results. When studies present time-to-event outcomes with Kaplan-Meier curves, these are often accompanied by the remaining number of patients at risk in a table below the curve. The number at risk at time t informs about uncertainty of the hazard at t, rather than the uncertainty of the estimated survival probability until t,Ŝ(t). We aim to review the role of the effective sample size of Ŝ(t) to reflect the uncertainty in survival probability estimation. Effective sample size is defined as the size of a hypothetical sample with complete follow-up until time t, that would give the same variance as the variance of the Kaplan-Meier estimate Ŝ(t). We consider hypothetical scenarios and a publicly available dataset with patients treated for colon cancer. These illustrations support that effective sample size provides a readily interpretable measure of uncertainty for survival curves in the presence of censoring. We show that effective sample size can also quantify the loss of information when the reporting for an ongoing study is moved to an earlier time point. In conclusion, effective sample size is a valuable measure of uncertainty in survival analysis.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00031305.2025.2542390 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:amstat:v:80:y:2026:i:1:p:100-108

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UTAS20

DOI: 10.1080/00031305.2025.2542390

Access Statistics for this article

The American Statistician is currently edited by Eric Sampson

More articles in The American Statistician from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().