Extensions of flexible survival models to marginal structural and Markov multi-state modeling

对各种疾病进展的预后研究对于确定高死亡风险的患者和评估替代治疗在预防死亡和提高存活率方面的有效性至关重要。对具有不同临床和社会人口学特征和/或接受不同治疗的患者的临床结果进行比较，以得出关于这些特征和治疗对疾病进展和死亡率的影响的结论。这些结论的有效性关键取决于用来分析数据的统计方法的准确性。必须使用复杂的统计方法来应对以下挑战：(A)几个患者的特征和/或治疗可能会影响结果，(B)某些特征是相互关联的，(C)治疗往往取决于患者的特征，这也可能影响临床结果。拟议的研究项目依赖于统计学理论和计算技术的最新进展，以开发新的、灵活的统计方法，以应对与以下方面有关的进一步挑战：(D)疾病演变和临床结果(例如，不同原因造成的死亡)的多种可能的纵向路径，以及(E)一些重要的临床风险因素以及处方治疗的类型和/或剂量随时间的变化。通过允许临床研究人员即使从如此复杂的数据中得出正确的结论，拟议的项目将促进对复杂疾病进展的理解。这可能最终提高临床预后质量和治疗效果。

Prognostic studies of the progression of various diseases are essential to identify patients at high risk of mortality and to assess the effectiveness of alternative treatments in preventing deaths and increasing survival. The clinical outcomes for patients with different clinical and socio-demographic characteristics and/or receiving different treatments are compared, to derive conclusions regarding the impact of these characteristics and treatments on disease progression and mortality. The validity of such conclusions depends crucially on the accuracy of statistical methods employed to analyze the data. Sophisticated statistical methods are necessary to account for the following challenges: (a) several patient characteristics and/or treatments may influence the outcome, (b) some characteristics are correlated with each other, (c) treatment often depends on patient's characteristics that may also affect the clinical outcome. The proposed research project relies on recent progress in both statistical theory and computational techniques to develop new, flexible statistical methods to address further challenges related to (d) the multitude of possible longitudinal pathways of disease evolution and of clinical outcomes (for example, deaths due to different causes), and changes over time (e) in some important clinical risk factors and the type and/or dose of the prescribed treatment. By allowing clinical researchers to draw correct conclusions even from such complex data, the proposed project will advance understanding of the progression of complex diseases. This may ultimately enhance the quality of clinical prognosis and the effectiveness of the treatments.