Lan Ling, Bandyopadhyay Dipankar, Datta Somnath
Department of Biostatistics and Epidemiology, Augusta University, Augusta, GA 30912, USA.
Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, 23298, USA.
Stat Neerl. 2017 Jan;71(1):31-57. doi: 10.1111/stan.12099. Epub 2016 Oct 25.
Datasets examining periodontal disease records current (disease) status information of tooth-sites, whose stochastic behavior can be attributed to a multistate system with state occupation determined at a single inspection time. In addition, the tooth-sites remain clustered within a subject, and the number of available tooth-sites may be representative of the true PD status of that subject, leading to an 'informative cluster size' scenario. To provide insulation against incorrect model assumptions, we propose a nonparametric regression framework to estimate state occupation probabilities at a given time and state exit/entry distributions, utilizing weighted monotonic regression and smoothing techniques. We demonstrate the superior performance of our proposed weighted estimators over the un-weighted counterparts via. a simulation study, and illustrate the methodology using a dataset on periodontal disease.
研究牙周疾病的数据集记录了牙齿位点的当前（疾病）状态信息，其随机行为可归因于一个多状态系统，该系统的状态占据情况在单次检查时确定。此外，牙齿位点在个体内仍呈聚集状态，可用牙齿位点的数量可能代表该个体的真实牙周疾病状态，从而导致出现“信息性聚类大小”的情况。为了防止错误的模型假设，我们提出了一个非参数回归框架，利用加权单调回归和平滑技术来估计给定时间的状态占据概率以及状态退出/进入分布。通过模拟研究，我们证明了我们提出的加权估计器相对于未加权估计器具有更优的性能，并使用一个牙周疾病数据集来说明该方法。
