Hybrid statistical and mechanistic mathematical model guides mobile health intervention for chronic pain

Nearly a quarter of visits to the Emergency Department are for conditions that could have been managed via outpatient treatment; improvements that allow patients to quickly recognize and receive appropriate treatment are crucial. The growing popularity of mobile technology creates new opportunities for real-time adaptive medical intervention, and the simultaneous growth of big data sources allows for preparation of personalized recommendations. Here we focus on the reduction of chronic suffering in the sickle cell disease community. Sickle cell disease is a chronic blood disorder in which pain is the most frequent complication. There currently is no standard algorithm or analytical method for real-time adaptive treatment recommendations for pain. Furthermore, current state-of-the-art methods have difficulty in handling continuous-time decision optimization using big data. Facing these challenges, in this study we aim to develop new mathematical tools for incorporating mobile technology into personalized treatment plans for pain. We present a new hybrid model for the dynamics of subjective pain that consists of a dynamical systems approach using differential equations to predict future pain levels, as well as a statistical approach tying system parameters to patient data (both personal characteristics and medication response history). Pilot testing of our approach suggests that it has significant potential to predict pain dynamics given patients' reported pain levels and medication usages. With more abundant data, our hybrid approach should allow physicians to make personalized, data driven recommendations for treating chronic pain.Comment: 13 pages, 15 figures, 5 table

Kappa statistic for clustered dichotomous responses from physicians and patients

The bootstrap method for estimating the standard error of the kappa statistic in the presence of clustered data is evaluated. Such data arise, for example, in assessing agreement between physicians and their patients regarding their understanding of the physician-patient interaction and discussions. We propose a computationally efficient procedure for generating correlated dichotomous responses for physicians and assigned patients for simulation studies. The simulation result demonstrates that the proposed bootstrap method produces better estimate of the standard error and better coverage performance compared to the asymptotic standard error estimate that ignores dependence among patients within physicians with at least a moderately large number of clusters. An example of an application to a coronary heart disease prevention study is presented

Twin-Singleton Differences in Neonatal Brain Structure

Twin studies suggest that global and regional brain volumes are highly heritable. However, estimates of heritability vary across development. Given that all twin studies are open to the potential criticism of non-generalizability due to differences in intrauterine environment between twins and singletons, these age effects may reflect the influence of perinatal environmental factors, which are unique to twins and which may be especially evident early in life. To address this question, we compared brain volumes and the relationship of brain volumes to gestational age in 136 singletons (67 male, 69 female) and 154 twins (75 male, 79 female; 82 DZ, 72 MZ) who had received high resolution MRI scans of the brain in the first month of life. Intracranial volume, total white matter, and ventricle volumes did not differ between twins and singletons. However, cerebrospinal fluid and frontal white matter volume was greater in twins compared to singletons. While gray matter volumes at MRI did not differ between groups, the slope of the relationship between total and cortical gray matter and gestational age at the MRI scan was steeper in MZ twins compared to DZ twins. Post-hoc analyses suggested that gray matter development is delayed in MZ twins in utero and that they experience ‘catch-up’ growth in the first month of life. These differences should be taken into account when interpreting and designing studies in the early postnatal period

Discordance of prenatal and neonatal brain development in twins

Discordance of birth weight has been observed in twin pairs, though little is known about prenatal and early neonatal discordance of head and brain size, and the role that zygosity and chorionicity play in discordances of early brain development in twins

Prenatal Mild Ventriculomegaly Predicts Abnormal Development of the Neonatal Brain

Many psychiatric and neurodevelopmental disorders are associated with mild enlargement of the lateral ventricles thought to have origins in prenatal brain development. Little is known about development of the lateral ventricles and the relationship of prenatal lateral ventricle enlargement with postnatal brain development

Cardiorespiratory fitness levels and body mass index of pre-adolescent children and older adults during the COVID-19 pandemic

IntroductionThe social and behavioral effects of the COVID-19 pandemic have impacted the health and physiology of most people, including those never diagnosed with COVID-19. While the impact of the pandemic has been felt across the lifespan, its effects on cardiorespiratory fitness (commonly considered a reflection of total body health) of older adults and children may be particularly profound due to social distancing and stay-at-home advisories, as well as the closure of sport facilities and non-essential businesses. The objective of this investigation was to leverage baseline data from two ongoing clinical trials to determine if cardiorespiratory fitness and body mass index were different during COVID-19 relative to before COVID-19 in older adults and children.MethodsHealthy older individuals (N = 593; 65–80 years) and 200 typically developing children (8–10 years) completed a graded maximal exercise test and had their height and weight measured.ResultsResults revealed that older adults and children tested during COVID-19 had significantly lower cardiorespiratory fitness levels than those tested before COVID-19 shutdowns (older adults: 30% lower; children: 53% lower; p's ≤ 0.001). In addition, older adults and children tested during COVID-19 had significantly higher BMI (older adults: 31.34 ± 0.57 kg/m2, p = 0.004; children: 19.27 ± 0.44 kg/m2, p = 0.05) than those tested before COVID-19 shutdowns (older adults: 29.51 ± 0.26 kg/m2, children: 18.13 ± 0.35 kg/m2). However, these differences in BMI did not remain significant when controlling for cardiorespiratory fitness.DiscussionResults from this investigation indicate that the COVID-19 pandemic, and behavior changes taken to reduce potential exposure, may have led to lower cardiorespiratory fitness levels in older adults and children, as well as higher body mass index. These findings provide relevant public health information as lower cardiorespiratory fitness levels and higher body mass indexes recorded during the pandemic could have far-reaching and protracted health consequences. Public health guidance is needed to encourage physical activity to maintain cardiorespiratory fitness and healthy body composition.Clinical trial registrationOlder adults: https://clinicaltrials.gov/ct2/show/NCT02875301, identifier: NCT02875301; Children: https://clinicaltrials.gov/ct2/show/NCT03592238, identifier: NCT03592238

A discrete-time survival model with random effects for designing and analyzing repeated low-dose challenge experiments

Repeated low-dose (RLD) challenge designs are important in HIV vaccine research. Current methods for RLD designs rely heavily on an assumption of homogeneous risk of infection among animals, which, upon violation, can lead to invalid inferences and underpowered study designs. We propose to fit a discrete-time survival model with random effects that allows for heterogeneity in the risk of infection among animals and allows for predetermined challenge dose changes over time. Based on this model, we derive likelihood ratio tests and estimators for vaccine efficacy. A two-stage approach is proposed for optimizing the RLD design under cost constraints. Simulation studies demonstrate good finite sample properties of the proposed method and its superior performance compared to existing methods. We illustrate the application of the heterogeneous infection risk model on data from a real simian immunodeficiency virus vaccine study using Rhesus Macaques. The results of our study provide useful guidance for future RLD experimental design

Bias-corrected and doubly robust inference for the three-level longitudinal cluster-randomized trials with missing continuous outcomes and small number of clusters: Simulation study and application to a study for adults with serious mental illnesses

Longitudinal cluster-randomized designs have been popular tools for comparative effective research in clinical trials. The methodologies for the three-level hierarchical design with longitudinal outcomes need to be better understood under more pragmatic settings; that is, with a small number of clusters, heterogeneous cluster sizes, and missing outcomes. Generalized estimating equations (GEEs) have been frequently used when the distribution of data and the correlation model are unknown. Standard GEEs lead to bias and an inflated type I error rate due to the small number of available clinics and non-completely random missing data in longitudinal outcomes. We evaluate the performance of inverse probability weighted (IPW) estimating equations, with and without augmentation, for two types of missing data in continuous outcomes and individual-level treatment allocation mechanisms combined with two bias-corrected variance estimators. Our intensive simulation results suggest that the proposed augmented IPW method with bias-corrected variance estimation successfully prevents the inflation of false positive findings and improves efficiency when the number of clinics is small, with moderate to severe missing outcomes. Our findings are expected to aid researchers in choosing appropriate analysis methods for three-level longitudinal cluster-randomized designs. The proposed approaches were applied to analyze data from a longitudinal cluster-randomized clinical trial involving adults with serious mental illnesses