Statistical Methods for Early Disease Prediction and Treatment Strategy Estimation Using Biomarker Signatures

神经精神障碍给患者、家庭和卫生保健系统带来了巨大的负担，因此 强调迫切需要开发治疗疾病的方法。神经精神障碍的研究进展 (例如，阿尔茨海默病、帕金森氏病)面临着独特的挑战，包括这些障碍 通常起病晚，进展慢，诊断标准是基于主观的临床症状， 而且存在实质性的疾病和受试者的异质性。在拟议的工作中，我们的目标是应对这些挑战 通过利用多个生物标记物的互补作用，包括全基因组的多态， 全脑神经成像、生物体液和全面的神经精神病学评估。我们开发 先进的分析工具，具有更高的分辨率和更高的准确性，可以解释生物机制 综合动态的全系统信息，整合多种来源的生物标志物。 这些方法适用于由调查小组收集的或从大型国际组织获得的临床数据。 为了建立神经退行性疾病最早病变的模型，评估治疗方案 回应，并为早期干预临床试验的设计和最佳个性化的发现提供信息 治疗。具体地说，在目标1中，我们发展了多层半参数变换模型的有效方法 评估和测试不同类型复杂表型的遗传变异风险，以告知基因 咨询，提高临床试验效率。我们的方法不依赖于全系基因分型，并提供了 除人口子结构外，家庭特有的子结构，以更好地控制混杂和减少虚假 全基因组关联研究中的发现率。在目标2中，我们发展了大规模的非线性动力系统 通过具有随机屈曲的常微分方程组了解早期病变和 识别有临床前体征的受试者。我们的方法提供了生物标志物集成的多领域集成 动力学。在目标3中，我们建立了动态风险模型，并结合动态网络结构进行估计 随着疾病进展而平稳演变的生物标志物图谱，可用于早期疾病诊断。我们占到了 生物标记物的不规则测量和生物标记物之间的生物网络依赖性。在目标4中，我们开发了 双重稳健和高效的机器学习方法，识别预测性标记，估计最优个性化 并确定可能从治疗中获得最大好处的亚组，将风险降至最低。 在每个目标中，我们将通过广泛的模拟研究来验证所提出的方法，并展示其 通过应用于真实世界的临床研究，具有实用价值。我们建立了所提问题的理论性质 方法运用现代经验过程理论和统计学习理论。总之，最先进的分析 这里提出的方法将大大提高分析的准确性，我们的统计和临床相结合 专业知识将确保我们的方法直接转化回临床和转化性研究社区。

Neuropsychiatric disorders pose an immense burden on patients, families, and health care systems, thus underscoring the urgent need to develop disease-modifying treatment. Research on neuropsychiatric disorders (e.g., Alzheimer’s disease, Parkinson’s disease) faces unique challenges, including the fact that these disorders typically have a late onset and slow progression, the diagnostic criteria are based on subjective clinical symptoms, and there is substantial disease and subject heterogeneity. In the proposed work, we aim to tackle these challenges by leveraging complementary contributions from multiple biomarkers, including genome-wide polymorphisms, whole brain neuroimaging, biofluids, and comprehensive neuropsychiatric assessments. We develop sophisticated analytic tools with higher resolution and improved accuracy by accounting for biological mechanisms of disease, synthesizing dynamic system-wide information, and integrating multiple sources of biomarkers. These methods are applied to clinical data collected by the investigative team or available from large international consortia in order to model the earliest pathological changes of neurodegenerative disease, assess treatment responses, and inform the design of early-intervention clinical trials and the discovery of optimal personalized therapies. Specifically, in Aim 1, we develop efficient methods for multi-level semiparametric transformation models to estimate and test the risk of genetic variants on various types of complex phenotypes to inform genetic counseling and improve clinical trial efficiency. Our methods do not rely on full pedigree genotyping and provide family-specific substructure, in addition to population substructure, to better control confounding and reduce false discovery rates in genome-wide association studies. In Aim 2, we develop large-scale nonlinear dynamic systems through ordinary differential equations with random inflections to understand early pathological changes and identify subjects with preclinical signs. Our method provides multi-domain integration of ensembles of biomarker dynamics. In Aim 3, we develop dynamic hazards models and incorporate dynamic network structures to estimate biomarker profiles that evolve smoothly with disease progression for earlier disease diagnosis. We account for irregularly measured biomarkers and biological network dependence among biomarkers. In Aim 4, we develop doubly robust and efficient machine learning methods to identify predictive markers, estimate optimal individualized therapies, and identify subgroups who may receive the greatest benefit from therapy, with minimal risk. In each aim, we will validate the proposed methods through extensive simulation studies and demonstrate their practical value via application to real-world clinical studies. We establish theoretical properties of the proposed methods using modern empirical process theory and statistical learning theory. Together, the state-of-the-art analytic methods proposed here will substantially improve analytic accuracy, and our combined statistical and clinical expertise will ensure that our methods are translated directly back to the clinical and translational research community.