ABSTRACT
OBJECTIVE: We assessed the prevalence of fear of falling (FoF) in a sample of people with chronic stroke and compared multiple variables (balance, anxiety, depression, activity and participation, and stroke severity) in people with and without FoF. METHOD: This study was a secondary analysis of data collected from a cross-sectional study of mobility after stroke in 77 participants with chronic stroke (>6 mo poststroke). RESULTS: Of the 77 participants, 51 (66%) reported experiencing FoF. People with FoF had significantly decreased balance (p<.001) and activity and participation (p=.006) and significantly increased anxiety (p=.007). People with FoF also had significantly worse stroke severity (p=.001). CONCLUSION: FoF is a prevalent concern in the chronic stroke population. The presence of FoF was associated with a variety of negative consequences. Occupational therapy practitioners should address FoF to help clients manage FoF and possibly improve recovery.
ABSTRACT
Ensemble methods using the same underlying algorithm trained on different subsets of observations have recently received increased attention as practical prediction tools for massive datasets. We propose Subsemble: a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a clever form of V-fold cross-validation to output a prediction function that combines the subset-specific fits. We give an oracle result that provides a theoretical performance guarantee for Subsemble. Through simulations, we demonstrate that Subsemble can be a beneficial tool for small to moderate sized datasets, and often has better prediction performance than the underlying algorithm fit just once on the full dataset. We also describe how to include Subsemble as a candidate in a SuperLearner library, providing a practical way to evaluate the performance of Subsemlbe relative to the underlying algorithm fit just once on the full dataset.
ABSTRACT
In most experimental and observational studies, participants are not followed in continuous time. Instead, data is collected about participants only at certain monitoring times. These monitoring times are random and often participant specific. As a result, outcomes are only known up to random time intervals, resulting in interval-censored data. In contrast, when estimating variable importance measures on interval-censored outcomes, practitioners often ignore the presence of interval censoring, and instead treat the data as continuous or right-censored, applying ad hoc approaches to mask the true interval censoring. In this article, we describe targeted minimum loss-based estimation (TMLE) methods tailored for estimation of binary variable importance measures with interval-censored outcomes. We demonstrate the performance of the interval-censored TMLE procedure through simulation studies and apply the method to analyze the effects of a variety of variables on spontaneous hepatitis C virus clearance among injection drug users, using data from the "International Collaboration of Incident HIV and HCV in Injecting Cohorts" project.
