Conditional Spectral Analysis of Replicated Multiple Time Series with Application to Nocturnal Physiology

This article considers the problem of analyzing associations between power spectra of multiple time series and cross-sectional outcomes when data are observed from multiple subjects. The motivating application comes from sleep medicine, where researchers are able to non-invasively record physiological time series signals during sleep. The frequency patterns of these signals, which can be quantified through the power spectrum, contain interpretable information about biological processes. An important problem in sleep research is drawing connections between power spectra of time series signals and clinical characteristics; these connections are key to understanding biological pathways through which sleep affects, and can be treated to improve, health. Such analyses are challenging as they must overcome the complicated structure of a power spectrum from multiple time series as a complex positive-definite matrix-valued function. This article proposes a new approach to such analyses based on a tensor-product spline model of Cholesky components of outcome-dependent power spectra. The approach flexibly models power spectra as nonparametric functions of frequency and outcome while preserving geometric constraints. Formulated in a fully Bayesian framework, a Whittle likelihood based Markov chain Monte Carlo (MCMC) algorithm is developed for automated model fitting and for conducting inference on associations between outcomes and spectral measures. The method is used to analyze data from a study of sleep in older adults and uncovers new insights into how stress and arousal are connected to the amount of time one spends in bed

Should tumor VEGF expression influence decisions on combining low-dose chemotherapy with antiangiogenic therapy? Preclinical modeling in ovarian cancer

Because of its low toxicity, low-dose (LD) chemotherapy is ideally suited for combination with antiangiogenic drugs. We investigated the impact of tumor vascular endothelial growth factor A (VEGF-A) expression on the efficacy of LD paclitaxel chemotherapy and its interactions with the tyrosine kinase inhibitor SU5416 in the ID8 and ID8-Vegf models of ovarian cancer. Functional linear models using weighted penalized least squares were utilized to identify interactions between Vegf, LD paclitaxel and antiangiogenic therapy. LD paclitaxel yielded additive effects with antiangiogenic therapy against tumors with low Vegf expression, while it exhibited antagonism to antiangiogenic therapy in tumors with high Vegf expression. This is the first preclinical study that models interactions of LD paclitaxel chemotherapy with antiangiogenic therapy and tumor VEGF expression and offers important lessons for the rational design of clinical trials

Homicide and geographic access to gun dealers in the United States

<p>Abstract</p> <p>Background</p> <p>Firearms are the most commonly used weapon to commit homicide in the U.S. Virtually all firearms enter the public marketplace through a federal firearms licensee (FFL): a store or individual licensed by the federal government to sell firearms. Whether FFLs contribute to gun-related homicide in areas where they are located, in which case FFLs may be a homicide risk factor that can be modified, is not known.</p> <p>Methods</p> <p>Annual county-level data (1993–1999) on gun homicide rates and rates of FFLs per capita were analyzed using negative binomial regression controlling for socio-demographic characteristics. Models were run to evaluate whether the relation between rates of FFLs and rates of gun homicide varied over the study period and across counties according to their level of urbanism (defined by four groupings, as below). Also, rates of FFLs were compared against FS/S – which is the proportion of suicides committed by firearm and is thought to be a good proxy for firearm availability in a region – to help evaluate how well the FFL variable is serving as a way to proxy firearm availability in each of the county types of interest.</p> <p>Results</p> <p>In major cities, gun homicide rates were higher where FFLs were more prevalent (rate ratio [RR] = 1.70, 95% CI 1.03–2.81). This association increased (p < 0.01) from 1993 (RR = 1.69) to 1999 (RR = 12.72), due likely to federal reforms that eliminated low-volume dealers, making FFL prevalence a more accurate exposure measure over time. No association was found in small towns. In other cities and in suburbs, gun homicide rates were significantly lower where FFLs were more prevalent, with associations that did not change over the years of the study period. FFL prevalence was correlated strongly (positively) with FS/S in major cities only, suggesting that the findings for how FFL prevalence relates to gun homicide may be valid for the findings pertaining to major cities but not to counties of other types.</p> <p>Conclusion</p> <p>Modification of FFLs through federal, state, and local regulation may be a feasible intervention to reduce gun homicide in major cities.</p

Adaptive Bayesian Time–Frequency Analysis of Multivariate Time Series

<p>This article introduces a nonparametric approach to multivariate time-varying power spectrum analysis. The procedure adaptively partitions a time series into an unknown number of approximately stationary segments, where some spectral components may remain unchanged across segments, allowing components to evolve differently over time. Local spectra within segments are fit through Whittle likelihood-based penalized spline models of modified Cholesky components, which provide flexible nonparametric estimates that preserve positive definite structures of spectral matrices. The approach is formulated in a Bayesian framework, in which the number and location of partitions are random, and relies on reversible jump Markov chain and Hamiltonian Monte Carlo methods that can adapt to the unknown number of segments and parameters. By averaging over the distribution of partitions, the approach can approximate both abrupt and slowly varying changes in spectral matrices. Empirical performance is evaluated in simulation studies and illustrated through analyses of electroencephalography during sleep and of the El Niño-Southern Oscillation. Supplementary materials for this article are available online.</p

Rest-activity rhythm profiles associated with manic-hypomanic and depressive symptoms

Rest-activity rhythm (RAR) disturbances are associated with mood disorders. But there remains a need to identify the particular RAR profiles associated with psychiatric symptom dimensions. Establishing such profiles would support the development of tools that track the 24-h sleep-wake phenotypes signaling clinical heterogeneity. We used data-driven clustering to identify RAR profiles in 145 adults aged 36-82 years (mean = 60, standard deviation = 9). Then we evaluated psychiatric symptom dimensions (including positive and negative affect, depressive, manic-hypomanic, panic-agoraphobic, and substance use symptoms) associated with these empirically-derived RAR profiles. Clustering identified three sub-groups characterized, on average, by: (1) earlier and more robust RARs ("earlier/robust," n = 55, 38%); (2) later and irregular RARs ("later/irregular," n = 31, 21%); and (3) later RARs and a narrower active period ("later/narrower," n = 59, 41%). Compared with the "earlier/robust" group: the "later/irregular" group had higher levels of lifetime manic-hypomanic symptoms (β (standard error) = 0.80 (0.22) higher standardized symptom units, p = 0.0004) and lifetime depression symptoms (β (standard error) = 0.73 (0.21) higher standardized symptom units, p = 0.0009); the "later/narrower" group had more lifetime depression symptoms (β (standard error) = 0.48 (0.18) higher standardized symptom units, p = 0.0076). These associations persisted after adjustments for sleep continuity and duration, suggesting that RARs are distinct behavioral correlates of clinical heterogeneity. Longitudinal studies are needed to confirm whether RAR characteristics contribute to the risk of manic and/or depressive episodes, and whether they reflect the consequences of psychiatric disturbance (e.g., on quality of life or disability). Opportunities to monitor or intervene on objectively-assessed RARs could facilitate better mental health related outcomes

The association between physical activity and a composite measure of sleep health

PurposePhysical activity has been associated with several individual dimensions of sleep. However, the association between physical activity and sleep health, a construct that emphasizes the multidimensional nature of sleep, has not been explored. This analysis examined the relationship between physical activity and a composite measure of sleep health.MethodsA total of 114 adults (66% female, 60.3 ± 9.2&nbsp;years) were included in the analyses. Participants reported daily light-intensity physical activity (LPA) and moderate- and vigorous-intensity physical activity (MVPA) via diary, while wearing a pedometer (Omron HJ-720ITC) to measure daily steps. Sleep health was measured using the RU_SATED questionnaire, which addresses regularity of sleep patterns, satisfaction with sleep, daytime alertness, and sleep timing, efficiency, and duration. Multiple linear regression, binary logistic regression, and analysis of covariance (ANCOVA) were utilized for analyses.ResultsMean sleep health score was 9.6 ± 2.4 (0 [poor]-12 [good]). Participants reported 62.9 ± 66.0 and 51.2 ± 51.2&nbsp;min/day of LPA and MVPA, respectively, and took 5585.5 ± 2806.7 steps/day. Greater MVPA was associated with better sleep health (β = 0.27, P = 0.005) and sleep health scores differed between those reporting &lt; 30&nbsp;min/day and ≥ 60&nbsp;min/day of MVPA (P = 0.004). Greater MVPA was associated with higher odds of having good sleep satisfaction (OR = 1.58 [1.14-2.20], P &lt; 0.01), timing (OR = 2.07 [1.24-3.46], P &lt; 0.01), and duration (OR = 1.48 [1.02-2.18], P = 0.04). Pedometer-based physical activity and LPA were not related to sleep health or its individual dimensions.ConclusionsIn middle- to older-aged adults, higher-intensity activity, but not lower-intensity or volume of activity, was associated with greater sleep health. These data suggest that physical activity intensity may be important for sleep health