Time-to-event survival statistics in ophthalmology: Methodological research

Understanding the outcomes of interventions over time is essential for clinical decision making in surgical specialties.

Analysis of survival time (or time to event) is complicated when loss to follow up occurs. This article explores transparent data analysis methods where missing ("censored") data are present.

Manual search of the top 20 Ophthalmology journals from a recent year of the established literature (2014).

A total of 4565 articles were identified, of which 218 reported outcomes of treatment over time in humans.

Pertinent details to assist the use of Kaplan-Meier and life table actuarial statistics are explained, and criteria that define whether each has high, acceptable or poor quality are explored. The quality of reporting from the literature sample is analysed.

Reporting quality of survival curves and life tables from each sampled article is assessed according to the established criteria.

In total, 31.2% of samples (n = 68) presented survival curves, 53.2% (n = 116) presented life tables, 22% (n = 48) presented both, whilst 46.8% (n = 102) presented neither; 2% of survival curves and 13% of life tables were high quality, with quality of life tables significantly better than survival curves (P = .0042). 90.36% (n = 197) of articles reported time to event data which was classified as poor: due to poor analysis of survival curves (n = 50, 43.10%) poor analysis of life tables (n = 45, 66.18%); and complete omission of survival graphics (n = 102, 46.97%).

Ophthalmology research that follows patient outcomes over time can be analysed with "time-to-event" statistics, and reported with transparency. This analysis showed that important contextural information was omitted from 90% of ophthalmic studies, and this could impact patient decision making.