Li Zhigang, Frost H R, Tosteson Tor D, Zhao Lihui, Liu Lei, Lyons Kathleen, Chen Huaihou, Cole Bernard, Currow David, Bakitas Marie
Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA.
Department of Preventive Medicine, Northwestern University, Chicago, IL, 60611, USA.
Stat Med. 2017 Dec 20;36(29):4692-4704. doi: 10.1002/sim.7445. Epub 2017 Aug 17.
Palliative medicine is an interdisciplinary specialty focusing on improving quality of life (QOL) for patients with serious illness and their families. Palliative care programs are available or under development at over 80% of large US hospitals (300+ beds). Palliative care clinical trials present unique analytic challenges relative to evaluating the palliative care treatment efficacy which is to improve patients' diminishing QOL as disease progresses towards end of life (EOL). A unique feature of palliative care clinical trials is that patients will experience decreasing QOL during the trial despite potentially beneficial treatment. Often longitudinal QOL and survival data are highly correlated which, in the face of censoring, makes it challenging to properly analyze and interpret terminal QOL trend. To address these issues, we propose a novel semiparametric statistical approach to jointly model the terminal trend of QOL and survival data. There are two sub-models in our approach: a semiparametric mixed effects model for longitudinal QOL and a Cox model for survival. We use regression splines method to estimate the nonparametric curves and AIC to select knots. We assess the model performance through simulation to establish a novel modeling approach that could be used in future palliative care research trials. Application of our approach in a recently completed palliative care clinical trial is also presented.
姑息医学是一门跨学科专业，专注于改善重症患者及其家属的生活质量（QOL）。美国超过80%的大型医院（300张床位以上）都设有姑息治疗项目或正在开展相关项目。相对于评估姑息治疗的疗效而言，姑息治疗临床试验存在独特的分析挑战，因为随着疾病进展至生命末期（EOL），姑息治疗的疗效是改善患者逐渐下降的生活质量。姑息治疗临床试验的一个独特之处在于，尽管治疗可能有益，但患者在试验期间的生活质量仍会下降。纵向生活质量和生存数据通常高度相关，面对删失情况，这使得正确分析和解释末期生活质量趋势具有挑战性。为解决这些问题，我们提出一种新颖的半参数统计方法，对生活质量和生存数据的末期趋势进行联合建模。我们的方法中有两个子模型：一个用于纵向生活质量的半参数混合效应模型和一个用于生存的Cox模型。我们使用回归样条法估计非参数曲线，并使用AIC选择节点。我们通过模拟评估模型性能，以建立一种可用于未来姑息治疗研究试验的新颖建模方法。还介绍了我们的方法在最近完成的一项姑息治疗临床试验中的应用。
