Multilevel Latent Class Models with Dirichlet Mixing Distribution

Latent class analysis (LCA) and latent class regression (LCR) are widely used for modeling multivariate categorical outcomes in social sciences and biomedical studies. Standard analyses assume data of different respondents to be mutually independent, excluding application of the methods to familial and other designs in which participants are clustered. In this paper, we develop multilevel latent class model, in which subpopulation mixing probabilities are treated as random effects that vary among clusters according to a common Dirichlet distribution. We apply the Expectation-Maximization (EM) algorithm for model fitting by maximum likelihood (ML). This approach works well, but is computationally intensive when either the number of classes or the cluster size is large. We propose a maximum pairwise likelihood (MPL) approach via a modified EM algorithm for this case. We also show that a simple latent class analysis, combined with robust standard errors, provides another consistent, robust, but less efficient inferential procedure. Simulation studies suggest that the three methods work well in finite samples, and that the MPL estimates often enjoy comparable precision as the ML estimates. We apply our methods to the analysis of comorbid symptoms in the Obsessive Compulsive Disorder study. Our models\u27 random effects structure has more straightforward interpretation than those of competing methods, thus should usefully augment tools available for latent class analysis of multilevel data

Hypothesis Testing for an Extended Cox Model with Time-Varying Coefficients

The log-rank test has been widely used to test a treatment effect under the Cox model for censored time-to-event outcomes, though it may lose power substantially when the model\u27s proportional hazards assumption does not hold. In this paper, we consider an extended Cox model that uses B-splines or smoothing splines to model a time-varying treatment effect and propose score test statistics for the treatment effect. Our proposed new tests combine statistical evidence from both the magnitude and the shape of the time-varying hazard ratio function, and thus are omnibus and powerful against various types of alternatives. In addition, the new testing framework is applicable to any choices of spline basis functions, including B-splines and smoothing splines. Simulation studies confirm that the proposed tests perform well in finite samples and were frequently more powerful than conventional tests alone in many settings. The new methods are applied to the HIVNET 012 Study, a randomized clinical trial to assess the efficacy of single-dose Nevirapin against mother-to-child HIV transmission conducted by the HIV Prevention Trial Network

GENERALIZED MULTILEVEL FUNCTIONAL REGRESSION

We introduce Generalized Multilevel Functional Linear Models (GMFLM), a novel statistical framework motivated by and applied to the Sleep Heart Health Study (SHHS), the largest community cohort study of sleep. The primary goal of SHHS is to study the association between sleep disrupted breathing (SDB) and adverse health effects. An exposure of primary interest is the sleep electroencephalogram (EEG), which was observed for thousands of individuals at two visits, roughly 5 years apart. This unique study design led to the development of models where the outcome, e.g. hypertension, is in an exponential family and the exposure, e.g. sleep EEG, is multilevel functional data. We show that GMFLMs are, in fact, generalized multilevel mixed effect models. Two consequences of this result are that: 1) the mixed effects inferential machinery can be used for GMFLM and 2) functional regression models can be extended naturally to include, for example, additional covariates, random effects and nonparametric components. We propose and compare two inferential methods based on the parsimonious decomposition of the functional space

Smoking Cessation Smartphone App Use Over Time: Predicting 12-Month Cessation Outcomes in a 2-Arm Randomized Trial

BACKGROUND: Little is known about how individuals engage over time with smartphone app interventions and whether this engagement predicts health outcomes. OBJECTIVE: In the context of a randomized trial comparing 2 smartphone apps for smoking cessation, this study aimed to determine distinct groups of smartphone app log-in trajectories over a 6-month period, their association with smoking cessation outcomes at 12 months, and baseline user characteristics that predict data-driven trajectory group membership. METHODS: Functional clustering of 182 consecutive days of smoothed log-in data from both arms of a large (N=2415) randomized trial of 2 smartphone apps for smoking cessation (iCanQuit and QuitGuide) was used to identify distinct trajectory groups. Logistic regression was used to determine the association of group membership with the primary outcome of 30-day point prevalence of smoking abstinence at 12 months. Finally, the baseline characteristics associated with group membership were examined using logistic and multinomial logistic regression. The analyses were conducted separately for each app. RESULTS: For iCanQuit, participants were clustered into 3 groups: "1-week users" (610/1069, 57.06%), "4-week users" (303/1069, 28.34%), and "26-week users" (156/1069, 14.59%). For smoking cessation rates at the 12-month follow-up, compared with 1-week users, 4-week users had 50% higher odds of cessation (30% vs 23%; odds ratio [OR] 1.50, 95% CI 1.05-2.14; P=.03), whereas 26-week users had 397% higher odds (56% vs 23%; OR 4.97, 95% CI 3.31-7.52; P<.001). For QuitGuide, participants were clustered into 2 groups: "1-week users" (695/1064, 65.32%) and "3-week users" (369/1064, 34.68%). The difference in the odds of being abstinent at 12 months for 3-week users versus 1-week users was minimal (23% vs 21%; OR 1.16, 95% CI 0.84-1.62; P=.37). Different baseline characteristics predicted the trajectory group membership for each app. CONCLUSIONS: Patterns of 1-, 3-, and 4-week smartphone app use for smoking cessation may be common in how people engage in digital health interventions. There were significantly higher odds of quitting smoking among 4-week users and especially among 26-week users of the iCanQuit app. To improve study outcomes, strategies for detecting users who disengage early from these interventions (1-week users) and proactively offering them a more intensive intervention could be fruitful

An Activity Index for Raw Accelerometry Data and Its Comparison with Other Activity Metrics

Accelerometers have been widely deployed in public health studies in recent years. While they collect high-resolution acceleration signals (e.g., 10–100 Hz), research has mainly focused on summarized metrics provided by accelerometers manufactures, such as the activity count (AC) by ActiGraph or Actical. Such measures do not have a publicly available formula, lack a straightforward interpretation, and can vary by software implementation or hardware type. To address these problems, we propose the physical activity index (AI), a new metric for summarizing raw tri-axial accelerometry data. We compared this metric with the AC and another recently proposed metric for raw data, Euclidean Norm Minus One (ENMO), against energy expenditure. The comparison was conducted using data from the Objective Physical Activity and Cardiovascular Health Study, in which 194 women 60–91 years performed 9 lifestyle activities in the laboratory, wearing a tri-axial accelerometer (ActiGraph GT3X+) on the hip set to 30 Hz and an Oxycon portable calorimeter, to record both tri-axial acceleration time series (converted into AI, AC, and ENMO) and oxygen uptake during each activity (converted into metabolic equivalents (METs)) at the same time. Receiver operating characteristic analyses indicated that both AI and ENMO were more sensitive to moderate and vigorous physical activities than AC, while AI was more sensitive to sedentary and light activities than ENMO. AI had the highest coefficients of determination for METs (0.72) and was a better classifier of physical activity intensity than both AC (for all intensity levels) and ENMO (for sedentary and light intensity). The proposed AI provides a novel and transparent way to summarize densely sampled raw accelerometry data, and may serve as an alternative to AC. The AI’s largely improved sensitivity on sedentary and light activities over AC and ENMO further demonstrate its advantage in studies with older adults

Experimental study on working capacity of carbon canister based on Euro VI

In order to study the gasoline working capacity and durability of the carbon canister, the gasoline working capacity test of the carbon canister was conducted under different test conditions. The results showed that the gasoline working capacity of the canister carbon decreased with the increase of fuel vapor loading rate. The fuel vapor volume ratio of the inlet has little effect on the gasoline working capacity. After 300 gasoline working capacity test cycles, the working capacity of butane decreased by about 20%. The fuel vapor adsorption amount in first cycle of each carbon canister is far greater than the desorption amount in first cycle, and also far greater than the adsorption and desorption amount in the subsequent cycles, which indicated that a large amount of fuel vapor occupied the active sites after the first use of the carbon canister and cannot desorb

Development and application of an automated algorithm to identify a window of consecutive days of accelerometer wear for large-scale studies

Abstract Background Some accelerometer studies ask participants to document in a daily log when the device was worn. These logs are used to inform the window of consecutive days to extract from the accelerometer for analysis. Logs can be missing or inaccurate, which can introduce bias in the data. To mitigate this bias, we developed a simple computer algorithm that used data within the accelerometer to identify the window of consecutive wear days. To evaluate the algorithm’s performance, we compared how well it agreed to the window of days identified by visual inspection and participant logs. Findings Participants were older women (mean age 79 years) in a cohort study that aimed to examine the relationship of objective physical activity on cardiovascular health. The study protocol requested that participants wear an accelerometer 24 h per day over nine calendar days (to capture seven consecutive wear days) and to complete daily logs. A stratified sample with (n = 75) and without (n = 100) participant logs were selected. The Objective Physical Activity and Cardiovascular Health (OPACH) algorithm was applied to the accelerometer data to identify a window of up to seven consecutive wear days. Participant logs documented dates the device was first put on, worn, and removed. Using pre-established guidelines, two independent raters visually reviewed the accelerometer data and characterized the dates representing up to seven consecutive days of 24-h wear. Average agreement level between the two raters was 90%. The percent agreement was compared between the three methods. The OPACH algorithm and visual inspection had 83% agreement in identifying a window with the same total number of days, if one or more shifts in calendar dates were allowed. For visual inspection vs. logs and algorithm vs. logs, this agreement was 81 and 74%, respectively. Conclusion The OPACH algorithm can be efficiently and readily applied in large-scale accelerometer studies for the identification of a window of consecutive days of accelerometer wear. This algorithm was comparable to visual inspection and participant logs and might provide a quicker and more cost-effective alternative to selecting which data to extract from the accelerometer for analysis. Trial Registration: clinicaltrials.gov identifier: NCT0000061

Parameterizing and Validating Existing Algorithms for Identifying Out-of-Bed Time Using Hip-Worn Accelerometer Data from Older Women

Objective: To parameterize and validate two existing algorithms for identifying out-of-bed time using 24-hour hip-worn accelerometer data from older women. Approach: Overall, 628 women (80±6 years old) wore ActiGraph GT3X+ accelerometers 24 hours/day for up to 7 days and concurrently completed sleep-logs. Trained staff used a validated visual analysis protocol to measure in-bed periods on accelerometer tracings (criterion). The Tracy and McVeigh algorithms were adapted for optimal use in older adults. A training set of 314 women was used to choose two key thresholds by maximizing the sum of sensitivity and specificity for each algorithm and data (vertical axis, VA, and vector magnitude, VM) combination. Data from the remaining 314 women were then used to test agreement in waking wear time (i.e., out-of-bed time while wearing the accelerometer) by computing sensitivity, specificity, and kappa comparing the algorithm output with the criterion. Waking wear time-adjusted means of sedentary time, light-intensity physical activity (light PA) and moderate-to-vigorous-intensity physical activity (MVPA) were then estimated and compared. Main results: Waking wear time agreement with the criterion was high for Tracy_VA, Tracy_VM, McVeigh_VA, and highest for McVeigh_VM. Compared to the criterion, McVeigh_VM had mean sensitivity=0.92, specificity=0.87, kappa=0.80, and overall mean difference (±SD) of -0.04±2.5 hours/day. Minutes of sedentary time, light PA, and MVPA adjusted for waking wear time using the criterion measure and McVeigh_VM were not statistically different (p \u3e0.43 | all). Significance: The McVeigh algorithm with optimal parameters using VM performed best compared to criterion sleep-log assisted visual analysis and is suitable for automated identification of waking wear time in older women when visual analysis is not feasible

Association of Light Physical Activity Measured by Accelerometry and Incidence of Coronary Heart Disease and Cardiovascular Disease in Older Women

IMPORTANCE To our knowledge, no studies have examined light physical activity (PA) measured by accelerometry and heart disease in older women. OBJECTIVE To investigate whether higher levels of light PA were associated with reduced risks of coronary heart disease (CHD) or cardiovascular disease (CVD) in older women. DESIGN, SETTING, AND PARTICIPANTS Prospective cohort study of older women from baseline (March 2012 to April 2014) through February 28, 2017, for up to 4.91 years. The setting was community-dwelling participants from the Women's Health Initiative. Participants were ambulatory women with no history of myocardial infarction or stroke. EXPOSURES Data from accelerometers worn for a requested 7 days were used to measure light PA. MAIN OUTCOMES AND MEASURES Cox proportional hazards regression models estimated hazard ratios (HRs) and 95% CIs for physician-adjudicated CHD and CVD events across light PA quartiles adjusting for possible confounders. Light PA was also analyzed as a continuous variable with and without adjustment for moderate to vigorous PA (MVPA). RESULTS Among 5861 women (mean [SD] age, 78.5 [6.7] years), 143 CHD events and 570 CVD events were observed. The HRs for CHD in the highest vs lowest quartiles of light PA were 0.42 (95% CI, 0.25-0.70; P for trend <. 001) adjusted for age and race/ethnicity and 0.58 (95% CI, 0.34-0.99; P for trend = .004) after additional adjustment for education, current smoking, alcohol consumption, physical functioning, comorbidity, and self-rated health. Corresponding HRs for CVD in the highest vs lowest quartiles of light PA were 0.63 (95% CI, 0.49-0.81; P for trend <. 001) and 0.78 (95% CI, 0.60-1.00; P for trend = .004). The HRs for a 1-hour/day increment in light PA after additional adjustment for MVPA were 0.86 (95% CI, 0.73-1.00; P for trend = .05) for CHD and 0.92 (95% CI, 0.85-0.99; P for trend = .03) for CVD. CONCLUSIONS AND RELEVANCE The present findings support the conclusion that all movement counts for the prevention of CHD and CVD in older women. Large, pragmatic randomized trials are needed to test whether increasing light PA among older women reduces cardiovascular risk.National Heart, Lung, and Blood Institute (NHLBI) [R01 HL105065]; NHLBI [T32HL079891-11]; National Institutes of Health; US Department of Health and Human Services [HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, HHSN268201600004C]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Comparison of Questionnaire and Device Measures of Physical Activity and Sedentary Behavior in a Multi-Ethnic Cohort of Older Women

Background: Limited data are available regarding the correlation between questionnaire and device-measured physical activity (PA) and sedentary behavior (SB) in older women. Methods:We evaluated these correlations in 5,992 women, aged 63 and older, who completed the Women’s Health Initiative (WHI) and Community Healthy Activities Model Program for Seniors (CHAMPS) PA questionnaires and the CARDIA SB questionnaire prior to wearing a hip-worn accelerometer for 7 consecutive days. Accelerometer-measured total, light, and moderate-to-vigorous PA (MVPA), and total SB time were defined according to cutpoints established in a calibration study. Spearman coefficients were used to evaluate correlations between questionnaire and device-measures. Results: Mean time spent in PA and SB was lower for questionnaire than accelerometer measures, with variation in means according to age, race/ethnicity, bodymass index, and functional status. Overall, correlations between questionnaires and accelerometer measures were moderate for total PA, MVPA, and SB (r ≈ 0.20–0.40). Light intensity PA correlated weakly for WHI (r ≈ 0.01–0.06) and was variable for CHAMPS (r ≈ 0.07–0.22). Conclusion: Questionnaire and accelerometer estimates of total PA, MVPA, and SB have at best moderate correlations in older women and should not be assumed to be measuring the same behaviors or quantity of behavior. Light intensity PA is poorly measured by questionnaire. Because light intensity activities account for the largest proportion of daily activity time in older adults, and likely contribute to its health benefits, further research should investigate how to improve measurement of light intensity PA by questionnaires