Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr., Research Triangle Park, NC, USA.
Department of Public Health Sciences, Medical University of South Carolina, 135 Cannon St., Charleston, SC, USA.
Biostatistics. 2019 Oct 1;20(4):666-680. doi: 10.1093/biostatistics/kxy023.
The introduction of spatial and temporal frailty parameters in survival models furnishes a way to represent unmeasured confounding in the outcome of interest. Using a Bayesian accelerated failure time model, we are able to flexibly explore a wide range of spatial and temporal options for structuring frailties as well as examine the benefits of using these different structures in certain settings. A setting of particular interest for this work involved using temporal frailties to capture the impact of events of interest on breast cancer survival. Our results suggest that it is important to include these temporal frailties when there is a true temporal structure to the outcome and including them when a true temporal structure is absent does not sacrifice model fit. Additionally, the frailties are able to correctly recover the truth imposed on simulated data without affecting the fixed effect estimates. In the case study involving Louisiana breast cancer-specific mortality, the temporal frailty played an important role in representing the unmeasured confounding related to improvements in knowledge, education, and disease screenings as well as the impacts of Hurricane Katrina and the passing of the Affordable Care Act. In conclusion, the incorporation of temporal, in addition to spatial, frailties in survival analysis can lead to better fitting models and improved inference by representing both spatially and temporally varying unmeasured risk factors and confounding that could impact survival. Specifically, we successfully estimated changes in survival around the time of events of interest.
在生存模型中引入时空脆弱性参数为表示感兴趣结局的未测量混杂提供了一种方法。使用贝叶斯加速失效时间模型，我们能够灵活地探索广泛的时空结构脆弱性选择，并检查在某些情况下使用这些不同结构的益处。这项工作的一个特别感兴趣的设置涉及使用时间脆弱性来捕捉感兴趣事件对乳腺癌生存的影响。我们的结果表明，当结局存在真实的时间结构时，包含这些时间脆弱性很重要，而当结局不存在真实的时间结构时，包含这些脆弱性不会牺牲模型拟合度。此外，脆弱性能够在不影响固定效应估计的情况下正确恢复模拟数据中施加的真实情况。在涉及路易斯安那州乳腺癌特异性死亡率的案例研究中，时间脆弱性在代表与知识、教育和疾病筛查的改善以及卡特里娜飓风和《平价医疗法案》通过相关的未测量混杂方面发挥了重要作用。总之，在生存分析中纳入时空脆弱性除了空间脆弱性之外，还可以通过表示可能影响生存的空间和时间变化的未测量风险因素和混杂，从而导致更好的拟合模型和改进的推断。具体来说，我们成功地估计了在感兴趣事件发生前后的生存变化。
