Standardised survival probabilities: a useful and informative tool for reporting regression models for survival data.
Br J Cancer
; 127(10): 1808-1815, 2022 11.
Article
em En
| MEDLINE
| ID: mdl-36050446
ABSTRACT
BACKGROUND:
When interested in studying the effect of a treatment (or other exposure) on a time-to-event outcome, the most popular approach is to estimate survival probabilities using the Kaplan-Meier estimator. In the presence of confounding, regression models are fitted, and results are often summarised as hazard ratios. However, despite their broad use, hazard ratios are frequently misinterpreted as relative risks instead of relative rates.METHODS:
We discuss measures for summarising the analysis from a regression model that overcome some of the limitations associated with hazard ratios. Such measures are the standardised survival probabilities for treated and untreated survival probabilities if everyone in the population received treatment and if everyone did not. The difference between treatment arms can be calculated to provide a measure for the treatment effect.RESULTS:
Using publicly available data on breast cancer, we demonstrated the usefulness of standardised survival probabilities for comparing the experience between treated and untreated after adjusting for confounding. We also showed that additional important research questions can be addressed by standardising among subgroups of the total population.DISCUSSION:
Standardised survival probabilities are a useful way to report the treatment effect while adjusting for confounding and have an informative interpretation in terms of risk.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
Tipo de estudo:
Etiology_studies
/
Risk_factors_studies
Limite:
Female
/
Humans
Idioma:
En
Revista:
Br J Cancer
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Suécia