Clustered spatio-temporal varying coefficient regression model.

In regression analysis for spatio-temporal data, identifying clusters of spatial units over time in a regression coefficient could provide insight into the unique relationship between a response and covariates in certain subdomains of space and time windows relative to the background in other parts of the spatial domain and the time period of interest. In this article, we propose a varying coefficient regression method for spatial data repeatedly sampled over time, with heterogeneity in regression coefficients across both space and over time. In particular, we extend a varying coefficient regression model for spatial-only data to spatio-temporal data with flexible temporal patterns. We consider the detection of a potential cylindrical cluster of regression coefficients based on testing whether the regression coefficient is the same or not over the entire spatial domain for each time point. For multiple clusters, we develop a sequential identification approach. We assess the power and identification of known clusters via a simulation study. Our proposed methodology is illustrated by the analysis of a cancer mortality dataset in the Southeast of the U.S.

Regularized spatial and spatio-temporal cluster detection.

Spatial and spatio-temporal cluster detection are important tools in public health and many other areas of application. Cluster detection can be approached as a multiple testing problem, typically using a space and time scan statistic. We recast the spatial and spatio-temporal cluster detection problem in a high-dimensional data analytical framework with Poisson or quasi-Poisson regression with the Lasso penalty. We adopt a fast and computationally-efficient method using a novel sparse matrix representation of the effects of potential clusters. The number of clusters and tuning parameters are selected based on (quasi-)information criteria. We evaluate the performance of our proposed method including the false positive detection rate and power using a simulation study. Application of the method is illustrated using breast cancer incidence data from three prefectures in Japan.

A Flexible Method for Identifying Spatial Clusters of Breast Cancer Using Individual-Level Data.

Prior research has shown that cancer risk varies by geography, but scan statistics methods for identifying cancer clusters in case-control studies have been limited in their ability to identify multiple clusters and adjust for participant-level risk factors. We develop a method to identify geographic patterns of breast cancer odds using the Wisconsin Women's Health Study, a series of 5 population-based case-control studies of female Wisconsin residents aged 20-79 enrolled in 1988-2004 (cases=16,076, controls=16,795). We create sets of potential clusters by overlaying a 1 km grid over each county-neighborhood and enumerating a series of overlapping circles. Using a two-step approach, we fit a penalized binomial regression model to the number of cases and trials in each grid cell, penalizing all potential clusters by the least absolute shrinkage and selection operator (Lasso). We use BIC to select the number of clusters, which are included in a participant-level logistic regression model. We identify 15 geographic clusters, resulting in 23 areas of unique geographic odds ratios. After adjustment for known risk factors, confidence intervals narrowed but breast cancer odds ratios did not meaningfully change; one additional hotspot was identified. By considering multiple overlapping spatial clusters simultaneously, we discern gradients of spatial odds across Wisconsin.

Subjective sleep measurement: comparing sleep diary to questionnaire.

PURPOSE: The sleep diary is the gold standard of self-reported sleep duration, but its comparability to sleep questionnaires is uncertain. The purpose of this study was to compare self-reported sleep duration between a sleep diary and a sleep questionnaire and to test whether sleep-related disorders were associated with diary-questionnaire differences in sleep duration. PARTICIPANTS AND METHODS: We compared self-reported sleep duration from 5,432 questionnaire-sleep diary pairs in a longitudinal cohort of 1,516 adults. Participants reported sleep information in seven-day sleep diaries and in questionnaires. Research staff abstracted average sleep durations for three time periods (overall; weekday; weekend) from diaries and questionnaires. For each time period, we evaluated diary-questionnaire differences in sleep duration with Welch's two-sample t-tests. Using linear mixed effects regression, we regressed overall diary-questionnaire sleep duration difference on several participant characteristics: reporting any insomnia symptoms, having sleep apnea, sex, body mass index, smoking status, Short Form-12 Physical Health Composite Score, and Short Form-12 Mental Health Composite Score. RESULTS: The average diary-reported overall sleep duration (7.76 hrs) was longer than that of the questionnaire (7.07 hrs) by approximately 41 mins (0.69 hrs, 95% confidence interval: 0.62, 0.76 hrs). Results were consistent across weekday- and weekend-specific differences. Demographic-adjusted linear mixed effects models tested whether insomnia symptoms or sleep apnea were associated with diary-questionnaire differences in sleep duration. Insomnia symptoms were associated with a 17 min longer duration on the diary relative to the questionnaire (ß=0.28 hrs, 95% confidence interval: 0.22, 0.33 hrs), but sleep apnea was not significantly associated with diary-questionnaire difference. Female sex was associated with greater diary-questionnaire duration differences, whereas better self-reported health was associated with lesser differences. CONCLUSION: Diaries and questionnaires are somewhat disparate methods of assessing subjective sleep duration, although diaries report longer duration relative to questionnaires, and insomnia symptoms may contribute to greater perceived differences.

The ruminal bacterial community in lactating dairy cows has limited variation on a day-to-day basis.

Dairy cows rely on a complex ruminal microbiota to digest their host-indigestible feed. Our ability to characterize this microbiota has advanced significantly due to developments in next-generation sequencing. However, efforts to sample the rumen, which typically involves removing digesta directly from the rumen via a cannula, intubation, or rumenocentesis, is costly and labor intensive. As a result, the majority of studies characterizing the rumen microbiota are conducted on samples collected at a single time point. Currently, it is unknown whether there is significant day-to-day variation in the rumen microbiota, a factor that could strongly influence conclusion drawn from studies that sample at a single time point. To address this, we examined day-to-day changes in the ruminal microbiota of lactating dairy cows using next-generation sequencing to determine if single-day sampling is representative of sampling across 3 consecutive days. We sequenced single-day solid and liquid fractions of ruminal digesta collected over 3 consecutive days from 12 cannulated dairy cows during the early, middle, and late stages of a single lactation cycle using the V4 region of the bacterial 16S rRNA gene. We then generated 97% similarity operational taxonomic units (OTUs) from these sequences and showed that any of the individual samples from a given 3-day sampling period is equivalent to the mean OTUs determined from the combined 3-d data set. This finding was consistent for both solid and liquid fractions of the rumen, and we thus conclude that there is limited day-to-day variability in the rumen microbiota.