Using a monotone single-index model to stabilize the propensity score in missing data problems and causal inference

The augmented inverse weighting method is one of the most popular methods for estimating the mean of the response in causal inference and missing data problems. An important component of this method is the propensity score. Popular parametric models for the propensity score include the logistic, probit, and complementary log-log models. A common feature of these models is that the propensity score is a monotonic function of a linear combination of the explanatory variables. To avoid the need to choose a model, we model the propensity score via a semiparametric single-index model, in which the score is an unknown monotonic nondecreasing function of the given single index. Under this new model, the augmented inverse weighting estimator (AIWE) of the mean of the response is asymptotically linear, semiparametrically efficient, and more robust than existing estimators. Moreover, we have made a surprising observation. The inverse probability weighting and AIWEs based on a correctly specified parametric model may have worse performance than their counterparts based on a nonparametric model. A heuristic explanation of this phenomenon is provided. A real-data example is used to illustrate the proposed methods.     

Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout,with applications to quality of life measurements from clinical trials

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.     

Functional,impulse-based quantification of plantar pressure patterns in typical adult gait

BackgroundDynamic pedobarography is used to measure the change in plantar pressure distribution during gait. Clinical methods of pedobarographic analysis lack, however, a standardized, functional segmentation or require costly motion capture technology and expertise. Furthermore, while commonly used pedobarographic measures are mostly based on peak pressures, progressive foot deformities also depend on the duration the pressure is applied, which can be quantified via impulse measures.Research QuestionOur objectives were to: (1) develop a standardized method for functionally segmenting pedobarographic data during gait without the need for motion capture; (2) compute pedobarographic measures that are based on each segment’s vertical impulse; and (3) obtain a normative set of such pedobarographic measures for non-disabled gait.MethodsPedobarographic data was collected during gait from sixty adults with normal feet. Using the maximum pressure map for each trial, an expert and novice rater independently identified the hallux, heel, medial forefoot, and lateral forefoot and computed nine normalized vertical impulse measures.ResultsFrom the computed impulse measures, the Heel-to-Forefoot Balance was 33.3 ± 5.5%, the Medial-Lateral Forefoot Balance (with hallux) 59.2 ± 8.0%, the Medial-Lateral Forefoot Balance (without hallux) 53.5 ± 7.7%, and the Hallux-to-Medial Forefoot Balance 21.0 ± 8.9% (mean ± standard deviation). The intra- and inter-rater reliability ranged between 0.93 and 1.00 and between 0.89 and 0.99, respectively (ICC(2,1)).SignificanceWe developed a simple, stand-alone method for pedobarographic segmentation that is mechanistically linked to relevant anatomical regions of the foot. The normative impulse measures exhibited excellent reliability. This normative dataset is currently used in the clinical assessment of different foot deformities and gait impairments, and in the evaluation of treatment outcomes.     

Bayesian adaptive lasso for additive hazard regression with current status data

Variable selection is a crucial issue in model building and it has received considerable attention in the literature of survival analysis. However, available approaches in this direction have mainly focused on time-to-event data with right censoring. Moreover, a majority of existing variable selection procedures for survival models are developed in a frequentist framework. In this article, we consider additive hazards model in the presence of current status data. We propose a Bayesian adaptive least absolute shrinkage and selection operator procedure to conduct a simultaneous variable selection and parameter estimation. Efficient Markov chain Monte Carlo methods are developed to implement posterior sampling and inference. The empirical performance of the proposed method is demonstrated by simulation studies. An application to a study on the risk factors of heart failure disease for type 2 diabetes patients is presented.     

基于肥厚型心肌病临床数据管理系统的设计与应用

目的设计一款简单易操作的数据管理系统,解决目前零散的肥厚型心肌病临床数据,将临床信息科学有效地进行整合管理,便于医生及科研人员查询、统计。方法采用Foxtable搭建一个数据库管理软件框架,并建立局域网外部数据源,实现数据的存储、查询、共享、导出。结果基于肥厚型心肌病数据管理系统的设计与应用能够满足临床医生和科研人员的基本工作需求,很大程度上节省了数据整理时间。结论该系统操作简单、移植性高、实用性强,广泛适用于各临床科室有科研项目的小型数据库管理。     

Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions

To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariate. To overcome these limitations, we exploited the kernel‐reweighted logistic regression (KELLER) algorithm, a method that is common in genetic studies, to estimate dFC in resting state functional magnetic resonance imaging (rs‐fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. This paper compares the performance of the proposed KELLER method with current methods (SWC and tapered‐SWC (T‐SWC) with different window lengths) based on both simulated and real rs‐fMRI data. Estimated dFC networks were assessed for detecting dynamically connected brain region pairs with hypothesis testing. Simulation results revealed that KELLER can detect dynamic connections with a statistical power of 87.35% compared with 70.17% and 58.54% associated with T‐SWC (p‐value = .001) and SWC (p‐value <.001), respectively. Results of these different methods applied on real rs‐fMRI data were investigated for two aspects: calculating the similarity between identified mean dynamic pattern and identifying dynamic pattern in default mode network (DMN). In 68% of subjects, the results of T‐SWC with window length of 100 s, among different window lengths, demonstrated the highest similarity to those of KELLER. With regards to DMN, KELLER estimated previously reported dynamic connection pairs between dorsal and ventral DMN while SWC‐based method was unable to detect these dynamic connections.