B-Spline Modeling of Inertial Measurements for Evaluating Stroke Rehabilitation Effectiveness.

Patients who experience upper-limb paralysis after stroke require continual rehabilitation. Rehabilitation must be evaluated for appropriate treatment adjustment; such evaluation can be performed using inertial measurement units (IMUs) instead of standard scales or subjective evaluations. However, IMUs produce large quantities of discretized data, and using these data directly is challenging. In this study, B-splines were used to estimate IMU trajectory data for objective evaluations of hand function and stability by using machine learning classifiers and mathematical indices. IMU trajectory data from a 2018 study on upper-limb rehabilitation were used to validate the proposed method. Features extracted from B -spline trajectories could be used to classify individuals in the 2018 study with high accuracy, and the proposed indices revealed differences between these groups. Compared with conventional rehabilitation evaluation methods, the proposed method is more objective and effective.

On summary ROC curve for dichotomous diagnostic studies: an application to meta-analysis of COVID-19.

In a systematic review of a diagnostic performance, summarizing performance metrics is crucial. There are various summary models in the literature, and hence model selection becomes inevitable. However, most existing large-sample-based model selection approaches may not fit in a meta-analysis of diagnostic studies, typically having a rather small sample size. Researchers need to effectively determine the final model for further inference, which motivates this article to investigate existing methods and to suggest a more robust method for this need. We considered models covering several widely-used methods for bivariate summary of sensitivity and specificity. Simulation studies were conducted based on different number of studies and different population sensitivity and specificity. Then final models were selected using several existing criteria, and we compared the summary receiver operating characteristic (sROC) curves to the theoretical ROC curve given the generating model. Even though parametric likelihood-based criteria are often applied in practice for their asymptotic property, they fail to consistently choose appropriate models under the limited number of studies. When the number of studies is as small as 10 or 5, our suggestion is best in different scenarios. An example for summary ROC curves for chemiluminescence immunoassay (CLIA) used in COVID-19 diagnosis is also illustrated.

Distribution-free model selection for longitudinal zero-inflated count data with missing responses and covariates.

In many medical and social science studies, count responses with excess zeros are very common and often the primary outcome of interest. Such count responses are usually generated under some clustered correlation structures due to longitudinal observations of subjects. To model such longitudinal count data with excess zeros, the zero-inflated binomial (ZIB) models for bounded outcomes, and the zero-inflated negative binomial (ZINB) and zero-inflated poisson (ZIP) models for unbounded outcomes all are popular methods. To alleviate the effects of deviations from model assumptions, a semiparametric (or, distribution-free) weighted generalized estimating equations has been proposed to estimate model parameters when data are subject to missingness. In this article, we further explore important covariates for the response variable. Without assumptions on the data distribution, a model selection criterion based on the expected weighted quadratic loss is proposed to select an appropriate subset of covariates, especially when count responses have excess zeros and data are subject to nonmonotone missingness in both responses and covariates. To understand the selection effects of the percentages of excess zeros and missingness, we design various scenarios for covariate selection in the mean model via simulation studies and a real data example regarding the study of cardiovascular disease is also presented for illustration.

Model selection based on resampling approaches for cluster longitudinal data with missingness in outcomes.

In medical and health studies, longitudinal and cluster longitudinal data are often collected, where the response variable of interest is observed repeatedly over time and along with a set of covariates. Model selection becomes an active research topic but has not been explored largely due to the complex correlation structure of the data set. To address this important issue, in this paper, we concentrate on model selection of cluster longitudinal data especially when data are subject to missingness. Motivated from the expected weighted quadratic loss of a given model, data perturbation and bootstrapping methods are used to estimate the loss and then the model that has the smallest expected loss is selected as the best model. To justify the proposed model selection method, we provide various numerical assessments and a real application regarding the asthma data set is also analyzed for illustration.

Estimating the Probability of Rare Events Occurring Using a Local Model Averaging.

In statistical applications, logistic regression is a popular method for analyzing binary data accompanied by explanatory variables. But when one of the two outcomes is rare, the estimation of model parameters has been shown to be severely biased and hence estimating the probability of rare events occurring based on a logistic regression model would be inaccurate. In this article, we focus on estimating the probability of rare events occurring based on logistic regression models. Instead of selecting a best model, we propose a local model averaging procedure based on a data perturbation technique applied to different information criteria to obtain different probability estimates of rare events occurring. Then an approximately unbiased estimator of Kullback-Leibler loss is used to choose the best one among them. We design complete simulations to show the effectiveness of our approach. For illustration, a necrotizing enterocolitis (NEC) data set is analyzed.