Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
Br J Cancer. 2022 Nov;127(10):1808-1815. doi: 10.1038/s41416-022-01949-6. Epub 2022 Sep 1.
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.
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.
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.
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.
当对研究治疗（或其他暴露）对时间事件结局的影响感兴趣时，最流行的方法是使用 Kaplan-Meier 估计器估计生存概率。在存在混杂的情况下，拟合回归模型，结果通常以风险比进行总结。然而，尽管它们被广泛使用，但风险比经常被误解为相对风险，而不是相对比率。
我们讨论了从回归模型中总结分析的措施，这些措施克服了与风险比相关的一些限制。这些措施是治疗和未治疗的标准化生存概率：如果每个人都接受治疗，那么如果每个人都不接受治疗，那么生存概率。可以计算治疗臂之间的差异，以提供治疗效果的度量。
使用乳腺癌的公开可用数据，我们展示了标准化生存概率在调整混杂因素后比较治疗和未治疗经验的有用性。我们还表明，通过在总人群的亚组中标准化，可以解决其他重要的研究问题。
标准化生存概率是一种在调整混杂因素的同时报告治疗效果的有用方法，并且在风险方面具有信息丰富的解释。
