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Time‐to‐event survival statistics in ophthalmology: Methodological research
Author(s) -
Layton Christopher J.,
Layton Danielle M.
Publication year - 2020
Publication title -
clinical and experimental ophthalmology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.3
H-Index - 74
eISSN - 1442-9071
pISSN - 1442-6404
DOI - 10.1111/ceo.13848
Subject(s) - medicine , survival analysis , event (particle physics) , statistics , relevance (law) , quality of life (healthcare) , psychological intervention , surgery , mathematics , physics , nursing , quantum mechanics , psychiatry , political science , law
Importance Understanding the outcomes of interventions over time is essential for clinical decision making in surgical specialties. Background 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. Design Manual search of the top 20 Ophthalmology journals from a recent year of the established literature (2014). Samples A total of 4565 articles were identified, of which 218 reported outcomes of treatment over time in humans. Methods 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. Main Outcome Measures Reporting quality of survival curves and life tables from each sampled article is assessed according to the established criteria. Results 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%). Conclusions and Relevance 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.