Missing data due to non-wear are common in accelerometer studies measuring physical activity and sedentary behavior. Accelerometer output are high-dimensional time-series data that are episodic and often highly skewed, presenting unique challenges for handling missing data. Common methods for missing accelerometry either are ad-hoc, require restrictive parametric assumptions, or do not appropriately impute bouts. This study developed a flexible hot deck multiple imputation (MI; i.e., "replacing" missing data with observed values) procedure to handle missing accelerometry. For each missing segment of accelerometry, "donor pools" contained observed segments from either the same or different participants, and 10 imputed segments were randomly drawn from the donor pool according to selection weights, where the donor pool and selection weight depended on variables associated with non-wear and/or accelerometer-based measures. A simulation study of 2,550 women compared hot deck MI to two standard methods in the field: available case (AC) analysis (i.e., analyzing all observed accelerometry with no restriction on wear time or number of days) and complete case (CC) analysis (i.e., analyzing only participants that wore the accelerometer for ≥10 hours for 4-7 days). This was repeated using accelerometry from the entire 24-hour day and daytime (10am-8pm) only, and data were missing

Many clinical trials of treatments for patients hospitalized for COVID-19 use an ordinal scale recommended by the World Health Organization. The scale represents intensity of medical intervention, with higher scores for interventions more burdensome for the patient, and highest score for death. There is uncertainty about use of this ordinal scale in testing hypotheses. With the objective of assessing the power and Type I error of potential endpoints and analyses based on the ordinal scale, trajectories of the score over 28 days were simulated for scenarios based closely on results of two trials recently published. The simulation used transition probabilities for the ordinal scale over time. No one endpoint was optimal across scenarios, but a ranked measure of trajectory fared moderately well in all scenarios. Type I error was controlled at close to the nominal level for all endpoints. Because not tied to a particular population with regard to baseline severity, the use of transition probabilities allows plausible assessment of endpoints in populations with configurations of baseline score for which data is not yet published, provided some data on the relevant transition probabilities are available. The results could support experts in the choice of endpoint based on the ordinal scale.

In order to study the spatiotemporal features of urban centre residents during peak hours during workdays and rest days, based on taxi on and off location information and urban points of interest data, a Geographic Information System (GIS) Kernel density estimation (KED) is used. Combined with the K-means clustering algorithm, the peak hours of residents ‘travel and hotspot areas for boarding and alighting are identified, and the strength of the interaction between residents in each area is analysis using the structured Georgy Voronoi, and the spatiotemporal features of residents’ travel are summarized.

No abstract

No abstract