Non-Stationary Time Series Model for Station-Based Subway Ridership During COVID-19 Pandemic: Case Study of New York City.

The COVID-19 pandemic in 2020 has caused sudden shocks in transportation systems, specifically the subway ridership patterns in New York City (NYC), U.S. Understanding the temporal pattern of subway ridership through statistical models is crucial during such shocks. However, many existing statistical frameworks may not be a good fit to analyze the ridership data sets during the pandemic, since some of the modeling assumptions might be violated during this time. In this paper, utilizing change point detection procedures, a piecewise stationary time series model is proposed to capture the nonstationary structure of subway ridership. Specifically, the model consists of several independent station based autoregressive integrated moving average (ARIMA) models concatenated together at certain time points. Further, data-driven algorithms are utilized to detect the changes of ridership patterns as well as to estimate the model parameters before and during the COVID-19 pandemic. The data sets of focus are daily ridership of subway stations in NYC for randomly selected stations. Fitting the proposed model to these data sets enhances understanding of ridership changes during external shocks, both in relation to mean (average) changes and the temporal correlations.

A Fast Detection Method of Break Points in Effective Connectivity Networks.

There is increasing interest in identifying changes in the underlying states of brain networks. The availability of large scale neuroimaging data creates a strong need to develop fast, scalable methods for detecting and localizing in time such changes and also identify their drivers, thus enabling neuroscientists to hypothesize about potential mechanisms. This paper presents a fast method for detecting break points in exceedingly long time series neurogimaging data, based on vector autoregressive (Granger causal) models. It uses a multi-step strategy based on a regularized objective function that leads to fast identification of candidate break points, followed by clustering steps to select the final set of break points and subsequent estimation with false positives control of the underlying Granger causal networks. The latter provide insights into key changes in network connectivity that led to the presence of break points. The proposed methodology is illustrated on synthetic data varying in their length, dimensionality, number of break points, strength of signal and also applied to EEG data related to visual tasks.

Penalized estimation of threshold auto-regressive models with many components and thresholds.

Thanks to their simplicity and interpretable structure, autoregressive processes are widely used to model time series data. However, many real time series data sets exhibit non-linear patterns, requiring nonlinear modeling. The threshold Auto-Regressive (TAR) process provides a family of non-linear auto-regressive time series models in which the process dynamics are specific step functions of a thresholding variable. While estimation and inference for low-dimensional TAR models have been investigated, high-dimensional TAR models have received less attention. In this article, we develop a new framework for estimating high-dimensional TAR models, and propose two different sparsity-inducing penalties. The first penalty corresponds to a natural extension of classical TAR model to high-dimensional settings, where the same threshold is enforced for all model parameters. Our second penalty develops a more flexible TAR model, where different thresholds are allowed for different auto-regressive coefficients. We show that both penalized estimation strategies can be utilized in a three-step procedure that consistently learns both the thresholds and the corresponding auto-regressive coefficients. However, our theoretical and empirical investigations show that the direct extension of the TAR model is not appropriate for high-dimensional settings and is better suited for moderate dimensions. In contrast, the more flexible extension of the TAR model leads to consistent estimation and superior empirical performance in high dimensions.

Joint Structural Break Detection and Parameter Estimation in High-Dimensional Non-Stationary VAR Models.

Assuming stationarity is unrealistic in many time series applications. A more realistic alternative is to assume piecewise stationarity, where the model can change at potentially many change points. We propose a three-stage procedure for simultaneous estimation of change points and parameters of high-dimensional piecewise vector autoregressive (VAR) models. In the first step, we reformulate the change point detection problem as a high-dimensional variable selection one, and solve it using a penalized least square estimator with a total variation penalty. We show that the penalized estimation method over-estimates the number of change points, and propose a selection criterion to identify the change points. In the last step of our procedure, we estimate the VAR parameters in each of the segments. We prove that the proposed procedure consistently detects the number and location of change points, and provides consistent estimates of VAR parameters. The performance of the method is illustrated through several simulated and real data examples.

PROMIS and legacy measures compared in a supportive care intervention for breast cancer patients and caregivers: Experience from a randomized trial.

OBJECTIVE: Accurate and efficient measurement of patient-reported outcomes is key in cancer symptom management trials. The newer Patient Reported Outcomes Measurement Information System (PROMIS) and previously developed measures of similar conceptual content (legacy) are available to measure symptoms and functioning. This report compares the performance of two sets of measures, PROMIS and legacy, in a recently completed trial of a supportive care intervention that enrolled breast cancer patients and their friend or family caregivers. METHODS: Patient-caregiver dyads (N = 256) were randomized to either reflexology delivered by caregivers or usual care control. Post-intervention, PROMIS and legacy measures of symptoms and functioning were analyzed in relation to trial arm, while adjusting for baseline values. Responsiveness of the two sets of measures was assessed using effect sizes and P-values for the effect of trial arm on patients' and caregivers' symptom and functioning outcomes. RESULTS: Similar conclusions about intervention effects were found using PROMIS and legacy measures for pain, fatigue, sleep, anxiety, physical, and social functioning. Different conclusions were obtained for patient and caregiver depression: legacy measures indicated the efficacy of reflexology, while PROMIS depression measure did not. CONCLUSION: Evidence of similar responsiveness supports the use of either set of measures for symptoms and functioning in clinical and general populations. Differences between PROMIS and legacy measures of depression need to be considered when choosing instruments for use in trials of supportive care interventions and in clinical practice.

Saw Palmetto for Symptom Management During Radiation Therapy for Prostate Cancer.

CONTEXT: Lower urinary tract symptoms (LUTSs) affect 75%-80% of men undergoing radiation therapy (RT) for prostate cancer. OBJECTIVES: To determine the safety, maximum tolerated dose (MTD), and preliminary efficacy of Serenoa repens commonly known as saw palmetto (SP) for management of LUTS during RT for prostate cancer. METHODS: The dose finding phase used the time-to-event continual reassessment method to evaluate safety of three doses (320, 640, and 960 mg) of SP. Dose-limiting toxicities were assessed for 22 weeks using the Common Terminology Criteria for Adverse Events for nausea, gastritis, and anorexia. The exploratory randomized controlled trial phase assessed preliminary efficacy of the MTD against placebo. The primary outcome of LUTS was measured over 22 weeks using the International Prostate Symptom Score. Additional longitudinal assessments included quality of life measured with the Functional Assessment of Cancer Therapy-Prostate. RESULTS: The dose finding phase was completed by 27 men who reported no dose-limiting toxicities and with 20 participants at the MTD of 960 mg daily. The exploratory randomized controlled trial phase included 21 men, and no statistically significant differences in the International Prostate Symptom Score were observed. The prostate-specific concerns score of the Functional Assessment of Cancer Therapy-Prostate improved in the SP group (P = 0.03). Of 11 men in the placebo group, two received physician-prescribed medications to manage LUTS compared with none of the 10 men in the SP group. CONCLUSION: SP at 960 mg may be a safe herbal supplement, but its efficacy in managing LUTS during RT needs further investigation.