Incorporating Functional Response Time Effects into a Signal Detection Theory Model

Signal detection theory (SDT; Tanner & Swets in Psychological Review 61:401-409, 1954) is a dominant modeling framework used for evaluating the accuracy of diagnostic systems that seek to distinguish signal from noise in psychology. Although the use of response time data in psychometric models has increased in recent years, the incorporation of response time data into SDT models remains a relatively underexplored approach to distinguishing signal from noise. Functional response time effects are hypothesized in SDT models, based on findings from other related psychometric models with response time data. In this study, an SDT model is extended to incorporate functional response time effects using smooth functions and to include all sources of variability in SDT model parameters across trials, participants, and items in the experimental data. The extended SDT model with smooth functions is formulated as a generalized linear mixed-effects model and implemented in the gamm4 R package. The extended model is illustrated using recognition memory data to understand how conversational language is remembered. Accuracy of parameter estimates and the importance of modeling variability in detecting the experimental condition effects and functional response time effects are shown in conditions similar to the empirical data set via a simulation study. In addition, the type 1 error rate of the test for a smooth function of response time is evaluated.

The impact of aspirin exposure prior to intensive care unit admission on the outcomes for patients with sepsis-associated acute respiratory failure

Objectives: This present study aimed to infer the association between aspirin exposure prior to ICU admission and the clinical outcomes of patients with Sepsis-associated acute respiratory failure (S-ARF). Methods: We obtained data from the Medical Information Mart for Intensive Care IV 2.0. Patients were divided into pre-ICU aspirin exposure group and Non-aspirin exposure group based on whether they took aspirin before ICU admission. The primary outcome is 28-day mortality. Augmented inverse propensity weighted was used to explore the average treatment effect (ATE) of the pre-ICU aspirin exposure. A generalized additive mixed model was used to analyze the longitudinal data of neutrophil to lymphocyte ratio (NLR), red cell distribution width (RDW), oxygenation index (P/F), dynamic lung compliance (Cdyn), mechanical power (MP), and mechanical power normalized to predicted body weight (WMP) in the two groups. A multiple mediation model was constructed to explore the possible mediators between pre-ICU aspirin exposure and outcomes of patients with S-ARF. Results: A total of 2090 S-ARF patients were included in this study. Pre-ICU aspirin exposure decreased 28-day mortality (ATE, -0.1945, 95% confidence interval [CI], -0.2786 to -0.1103, p < 0.001), 60-day mortality (ATE, -0.1781, 95% Cl, -0.2647 to -0.0915, p < 0.001), and hospital mortality (ATE, -0.1502, 95%CI, -0.2340 to -0.0664, p < 0.001). In subgroup analysis, the ATE for 28-day mortality, 60-day mortality, and hospital mortality were not statistically significant in the coronary care unit group, high-dose group (over 100 mg/d), and no invasive mechanical ventilation (IMV) group. After excluding these non-beneficiaries, Cdyn and P/F ratio of the pre-ICU aspirin exposure group increased by 0.31mL/cmH₂O (SE, 0.21, p = 0.016), and 0.43 mmHg (SE, 0.24, p = 0.041) every hour compared to that of non-aspirin exposure group after initialing IMV. The time-weighted average of NLR, Cdyn, WMP played a mediating role of 8.6%, 24.7%, and 13% of the total effects of pre-ICU aspirin exposure and 28-day mortality, respectively. Conclusion: Pre-ICU aspirin exposure was associated with decreased 28-day mortality, 60-day mortality, and hospital mortality in S-ARF patients except those admitted to CCU, and those took a high-dose aspirin or did not receive IMV. The protective effect of aspirin may be mediated by a low dynamic level of NLR and a high dynamic level of Cdyn and WMP. The findings should be interpreted cautiously, given the sample size and potential for residual confounding.

Association between Elevated Magnesium Intake and Reduced Risk of Recurrent Falls and Frailty in Osteoarthritis: Data from the Osteoarthritis Initiative

OBJECTIVES

The objective of this 8-year follow-up study was to investigate the relationship between magnesium intake and frailty, as well as recurrent falls, in individuals diagnosed with Osteoarthritis (OA) or those at a heightened risk for developing the condition.

METHODS

This study utilized data from the Osteoarthritis Initiative (OAI) database and conducted a prospective cohort study with a 8-year follow-up period. Total magnesium intake from both food sources and supplements was assessed using a food frequency questionnaire (FFQ), while frailty and recurrent falls were evaluated through established criteria and self-report, respectively. To account for potential confounding factors, various covariates were considered, and statistical analyses, including generalized additive mixed models (GAMMs), were employed to examine the associations.

RESULTS

Among the 4,667 participants with OA, those with lower total magnesium intake were characterized by younger age, a higher proportion of African American individuals, higher body mass index (BMI), and lower dietary fiber intake (P<0.001). Notably, this group exhibited higher odds of experiencing recurrent falls and frailty (P = 0.034 and 0.006, respectively). Controlling for various factors, the GAMMs consistently revealed negative correlations between magnesium intake and the likelihood of frailty and recurrent falls, with each 1 mg/1000 kcal increase in magnesium intake associated with a 0.5% reduced frailty risk (p < 0.001) and a 0.2% decreased risk of recurrent falls (p = 0.001). Subgroup analyses suggested that increased total magnesium intake from both food sources and supplements may exert a more pronounced preventive effect on recurrent falls and frailty in men, older adults, individuals with normal BMI, and those with higher dietary fiber intake.

CONCLUSIONS

Elevated total magnesium intake from both food sources and supplements was found to be associated with a decreased risk of recurrent falls and frailty in individuals diagnosed with OA or those at risk of developing the condition. These findings imply that increased total magnesium intake might be beneficial in managing the risk of these outcomes, particularly within specific subgroups, including men, older adults, those with a normal BMI, and those with higher dietary fiber intake.

Modelling multilevel nonlinear treatment-by-covariate interactions in cluster randomized controlled trials using a generalized additive mixed model

A cluster randomized controlled trial (C-RCT) is common in educational intervention studies. Multilevel modelling (MLM) is a dominant analytic method to evaluate treatment effects in a C-RCT. In most MLM applications intended to detect an interaction effect, a single interaction effect (called a conflated effect) is considered instead of level-specific interaction effects in a multilevel design (called unconflated multilevel interaction effects), and the linear interaction effect is modelled. In this paper we present a generalized additive mixed model (GAMM) that allows an unconflated multilevel interaction to be estimated without assuming a prespecified form of the interaction. R code is provided to estimate the model parameters using maximum likelihood estimation and to visualize the nonlinear treatment-by-covariate interaction. The usefulness of the model is illustrated using instructional intervention data from a C-RCT. Results of simulation studies showed that the GAMM outperformed an alternative approach to recover an unconflated logistic multilevel interaction. In addition, the parameter recovery of the GAMM was relatively satisfactory in multilevel designs found in educational intervention studies, except when the number of clusters, cluster sizes, and intraclass correlations were small. When modelling a linear multilevel treatment-by-covariate interaction in the presence of a nonlinear effect, biased estimates (such as overestimated standard errors and overestimated random effect variances) and incorrect predictions of the unconflated multilevel interaction were found.

The baseline and repeated measurements of DBP to assess in-hospital mortality risk among critically ill patients with acute myocardial infarction: A retrospective cohort study

Changes in diastolic blood pressure (DBP) are common in patients with acute myocardial infarction (AMI). The relationship between the dynamic change of DBP and in-hospital mortality among patients with AMI remains unclear. This study aimed to explore the importance of DBP during disease development among patients with AMI. We performed a retrospective cohort study involving patients from the Medical Information Mart for Intensive Care III database, which included > 40,000 patients admitted to the intensive care unit (ICU). Overall, 3209 adult AMI admissions were identified. We extracted the clinical and laboratory information in the patients with AMI. Cox proportional hazards models were used to evaluate the prognostic values of baseline DBP. We used the generalized additive mixed model (GAMM) to compare trends in DBP over time among survivors and non-survivors, after adjusting for potential confounders. During the ICU stay, 189 patients died (mortality rate, 6.36%). The age of each non-survivor together with the variations in DBP over time from admission to the time of death is of great importance to the scientific community. Cox multivariable regression analysis displayed that after adjusting for confounding factors, ascended baseline DBP was an important hazard factor for hospital deaths (hazard ratio, 1.02; 95% confidence interval, 1.01-1.03; P = .003). Based on GAMM, DBP in the death group was markedly lower than that of the surviving group. Moreover, the difference between the two groups showed an increasing trend within 3 days after ICU admission. After adjusting for various variables, the results were stable. DBP significantly contributed to in-hospital mortality among patients with AMI. There was a nonlinear correlation between baseline DBP and in-hospital mortality among patients with AMI, and the DBP of the non-survivors decreased within the first 3 days after ICU admission. However, the causality cannot be deduced from our data.

Explaining regional differences in mortality during the first wave of Covid-19 in Italy

Italy was hit harshly by the Covid-19 pandemic, registering more than 35,000 Covid-19 deaths between February and July 2020. During this first wave of the epidemic, the virus spread unequally across the country, with northern regions witnessing more cases and deaths. We investigate demographic and socio-economic factors contributing to the diverse regional impact of the virus during the first wave. Using generalized additive mixed models, we find that Covid-19 mortality at regional level is negatively associated with the degree of intergenerational co-residence, number of intensive care unit beds per capita, and delay in the outbreak of the epidemic. Conversely, we do not find strong associations for several variables highlighted in recent literature, such as population density or the share of the population who are older or have at least one chronic disease. Our results underscore the importance of context-specific analysis for the study of a pandemic.

Parasitism and alternative reproductive tactics in Northern chamois

Alternative reproductive tactics (ARTs), discrete phenotypic variations evolved to maximize fitness, may entail different cost-benefit trade-offs. In large mammals, differences in costs associated with ARTs-including energy expenditure and parasite infection-are typically greatest during the breeding season. Nonetheless, physiological and behavioral differences between ARTs can manifest throughout the year, possibly involving costs that may contribute to maintain ARTs within populations. Using the number of nematode larvae per gram of feces (LPG) as a proxy, we explored the temporal changes in lung parasite infection in territorial and nonterritorial male chamois Rupicapra in the Gran Paradiso National Park (Italy), between 2011 and 2012. We aimed to identify which tactic-specific physiological and behavioral features (including age, hormonal levels, inter- and intrasexual interactions, and space use) or climatic factors (temperature and precipitation) best explained yearly variation in parasite infection within and between ARTs. Generalized additive mixed models showed that the fecal larval counts of lung nematodes underwent strong temporal changes in both male types. Differences between ARTs (with higher LPG values in territorial than nonterritorial males) were greatest during the rut and-to a lesser extent-in spring, respectively, at the peak and at the onset of territoriality. The difference in LPG between tactics was largely explained by the greater levels of hormone metabolites in territorial males during the rut. The other variables did not contribute significantly to explain the different shedding of larvae within and between ARTs. Our analysis suggests that different values of LPG between territorial and nonterritorial males are largely a result of tactic-specific differences in the secretion of hormone metabolites, but only during the rut. To clarify whether rut-related parasitism contributes to the maintenance of ARTs, tactic-specific life history trade-offs, for example, between reproduction and parasite-related mortality, must be investigated.

Generalized additive mixed model on the analysis of total transport losses of market-weight pigs1

Transportation losses of market-weight pigs are an animal welfare concern, and result in direct economic impact for producers and abattoirs. Such losses are related to multiple factors including pig genetics, human handling, management, and weather conditions. Understanding the factors associated with total transport losses (TTL) is important to the swine industry because it can aid decision-making, and help in the development of transportation strategies to minimize the risk of losses. Hence, the objective of this study was to investigate factors associated with TTL on market-weight pigs in typically field conditions for Midwestern United States using a generalized additive mixed model (GAMM). The final quasi-binomial GAMM included the fixed (main and interactions) effects of abattoir of destination, type of driver, average market weight, distance traveled, wind speed, precipitation, and temperature-humidity index (THI), as well as the random effects of truck companies and the combination of site of origin and period of the year. Results indicate significant associations between TTL and the main effect of all explanatory variables (P < 0.05), except for wind speed and precipitation. Interactions of average market weight × abattoir, and wind speed × precipitation were also significant. A complex nonlinear relationship between TTL and model covariates were observed for distance traveled, THI, and interaction terms. This study showed that TTL of market-weight pigs are caused by a complex system involving multiple interacting factors, which can be potentially managed to mitigate the risk of losses. In addition, the GAMM showed to be a simple and flexible approach to model TTL because it can capture nonlinear relationships, handle non-normal data, and can potentially accommodate data structure.

Analyzing the Time Course of Pupillometric Data

This article provides a tutorial for analyzing pupillometric data. Pupil dilation has become increasingly popular in psychological and psycholinguistic research as a measure to trace language processing. However, there is no general consensus about procedures to analyze the data, with most studies analyzing extracted features from the pupil dilation data instead of analyzing the pupil dilation trajectories directly. Recent studies have started to apply nonlinear regression and other methods to analyze the pupil dilation trajectories directly, utilizing all available information in the continuously measured signal. This article applies a nonlinear regression analysis, generalized additive mixed modeling, and illustrates how to analyze the full-time course of the pupil dilation signal. The regression analysis is particularly suited for analyzing pupil dilation in the fields of psychological and psycholinguistic research because generalized additive mixed models can include complex nonlinear interactions for investigating the effects of properties of stimuli (e.g., formant frequency) or participants (e.g., working memory score) on the pupil dilation signal. To account for the variation due to participants and items, nonlinear random effects can be included. However, one of the challenges for analyzing time series data is dealing with the autocorrelation in the residuals, which is rather extreme for the pupillary signal. On the basis of simulations, we explain potential causes of this extreme autocorrelation, and on the basis of the experimental data, we show how to reduce their adverse effects, allowing a much more coherent interpretation of pupillary data than possible with feature-based techniques.

Association between Hemoglobin and Hemoglobin A1c: A Data-Driven Analysis of Health Checkup Data in Japan

Background: Interpretation of hemoglobin A_1c (HbA_1c) levels may be confounded by spurious results in anemic persons, but its degree is not well-established. Methods: We used an employer-based health insurance database, containing health checkup data and medical claims data; both were linked via a unique identifier of each beneficiary. This study included persons aged 18–75 years who participated in health checkups, with a confirmed or suspected diagnosis of diabetes. The relationship between hemoglobin (Hb) and HbA_1c is shown in a spline curve using a machine learning technique accounting for patient factors and within-person correlations. Spline curves were also shown in several sub-populations. Results: Overall, a decreased Hb value was associated with a lower HbA_1c value, but the extent differed among populations. In the whole cohort of the type-2 diabetes group (55,420 persons), the curve was generally a plateau in the persons with a Hb value <120–130 g/L. Among the 18,478 persons with HbA_1c around 48 mmol/mol, we observed a liner trend. Among the current glucose-lowering medication users (6253 persons), we found a right upward curve. Conclusions: The relationship between Hb and HbA_1c may not be straightforward, varying among populations of different clinical interest. Our results indicate that a simple formulation between the Hb and HbA_1c values is unlikely.

Quantifying the Impact of Floods on Bacillary Dysentery in Dalian City, China, From 2004 to 2010

OBJECTIVE

Studies quantifying relationships between floods and diarrheal diseases have mainly been conducted in low-latitude regions. It's therefore increasingly important to examine these relationships in midlatitude regions, where they may have significant public health implications. This study aimed to examine the association between floods and bacillary dysentery in the city of Dalian, China.

METHODS

A generalized additive mixed model was applied to examine the association between floods and bacillary dysentery. The relative risk (RR) of flood impact on bacillary dysentery was estimated.

RESULTS

A total of 18,976 cases of bacillary dysentery were reported in Dalian during the study period. Two weeks' lagged effect was detected from the impact of floods on bacillary dysentery. The RR of flood impact on bacillary dysentery was 1.17 (95% CI: 1.03-1.33).

CONCLUSIONS

Floods have significantly increased the risk of bacillary dysentery in Dalian. More studies should focus on the association between floods and infectious diseases in different regions. Our findings have significant implications for managing the negative health impact of floods in the midlatitude region of China. (Disaster Med Public Health Preparedness. 2017;11:190-195).

A Two-Stage Method to Estimate the Contribution of Road Traffic to PM₂.₅ Concentrations in Beijing, China

BACKGROUND

Fine particulate matters with aerodynamic diameters smaller than 2.5 micrometers (PM2.5) have been a critical environmental problem in China due to the rapid road vehicle growth in recent years. To date, most methods available to estimate traffic contributions to ambient PM2.5 concentration are often hampered by the need for collecting data on traffic volume, vehicle type and emission profile.

OBJECTIVE

To develop a simplified and indirect method to estimate the contribution of traffic to PM2.5 concentration in Beijing, China.

METHODS

Hourly PM2.5 concentration data, daily meteorological data and geographic information were collected at 35 air quality monitoring (AQM) stations in Beijing between 2013 and 2014. Based on the PM2.5 concentrations of different AQM station types, a two-stage method comprising a dispersion model and generalized additive mixed model (GAMM) was developed to estimate separately the traffic and non-traffic contributions to daily PM2.5 concentration. The geographical trend of PM2.5 concentrations was investigated using generalized linear mixed model. The temporal trend of PM2.5 and non-linear relationship between PM2.5 and meteorological conditions were assessed using GAMM.

RESULTS

The medians of daily PM2.5 concentrations during 2013-2014 at 35 AQM stations in Beijing ranged from 40 to 92 μg/m³. There was a significant increasing trend of PM2.5 concentration from north to south. The contributions of road traffic to daily PM2.5 concentrations ranged from 17.2% to 37.3% with an average 30%. The greatest contribution was found at AQM stations near busy roads. On average, the contribution of road traffic at urban stations was 14% higher than that at rural stations.

CONCLUSIONS

Traffic emissions account for a substantial share of daily total PM2.5 concentrations in Beijing. Our two-stage method is a useful and convenient tool in ecological and epidemiological studies to estimate the traffic contribution to PM2.5 concentrations when there is limited information on vehicle number and types and emission profile.

Synchrony in serum antibody response to conserved proteins of Streptococcus pneumoniae in young children

Conserved Streptococcus pneumoniae (Spn) proteins are currently under investigation as vaccine candidates. We recently identified a subset of children prone to frequent acute otitis media (AOM) that we refer to as stringently-defined otitis prone (sOP). We investigated the synchrony of serum antibody responses against 5 Spn protein vaccine antigens, PhtD, LytB, PcpA, PhtE, and PlyD1 resulting from nasopharyngeal colonization and AOM in sOP children (49 observations) and non-otitis prone (NOP) children (771 observations). Changes in serum IgG and IgM were quantitated with ELISA. IgG antibody concentrations against PhtD, PcpA, and PlyD1 rose in synchrony in sOP and NOP children; that is, the proteins appeared equally and highly immunogenic in children at age 6 to 15 months and then leveled off in their rise at 15 to 25 months. In contrast, rises in concentrations to PhtE and LytB were significantly slower and had not peaked in children even at 25 months of age, consistent with lower immunogenicity. Serum IgM responses against PhtD and PlyD1 were in synchrony in children at age 6-25 months old. PcpA did not induce a significant increase of serum IgM response in children, suggesting that primary responses to PcpA occurred prior to children attaining age 6 months old. PhtD, PcpA, and Ply elicit a synchronous natural acquisition of serum antibody in young children suggesting that a trivalent Spn protein vaccine combining PhtD, PcpA, and PlyD1 would be less likely to display antigen competition when administered as a combination vaccine in young children.

Spatiotemporal analysis of particulate air pollution and ischemic heart disease mortality in Beijing, China

BACKGROUND

Few studies have used spatially resolved ambient particulate matter with an aerodynamic diameter of <10 μm (PM10) to examine the impact of PM10 on ischemic heart disease (IHD) mortality in China. The aim of our study is to evaluate the short-term effects of PM10 concentrations on IHD mortality by means of spatiotemporal analysis approach.

METHODS

We collected daily data on air pollution, weather conditions and IHD mortality in Beijing, China during 2008 and 2009. Ordinary kriging (OK) was used to interpolate daily PM10 concentrations at the centroid of 287 township-level areas based on 27 monitoring sites covering the whole city. A generalized additive mixed model was used to estimate quantitatively the impact of spatially resolved PM10 on the IHD mortality. The co-effects of the seasons, gender and age were studied in a stratified analysis. Generalized additive model was used to evaluate the effects of averaged PM10 concentration as well.

RESULTS

The averaged spatially resolved PM10 concentration at 287 township-level areas was 120.3 ± 78.1 μg/m3. Ambient PM10 concentration was associated with IHD mortality in spatiotemporal analysis and the strongest effects were identified for the 2-day average. A 10 μg/m3 increase in PM10 was associated with an increase of 0.33% (95% confidence intervals: 0.13%, 0.52%) in daily IHD mortality. The effect estimates using spatially resolved PM10 were larger than that using averaged PM10. The seasonal stratification analysis showed that PM10 had the statistically stronger effects on IHD mortality in summer than that in the other seasons. Males and older people demonstrated the larger response to PM10 exposure.

CONCLUSIONS

Our results suggest that short-term exposure to particulate air pollution is associated with increased IHD mortality. Spatial variation should be considered for assessing the impacts of particulate air pollution on mortality.

Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors

BACKGROUND

Exposure to atmospheric particulate matter (PM) remains an important public health concern, although it remains difficult to quantify accurately across large geographic areas with sufficiently high spatial resolution. Recent epidemiologic analyses have demonstrated the importance of spatially- and temporally-resolved exposure estimates, which show larger PM-mediated health effects as compared to nearest monitor or county-specific ambient concentrations.

METHODS

We developed generalized additive mixed models that describe regional and small-scale spatial and temporal gradients (and corresponding uncertainties) in monthly mass concentrations of fine (PM2.5), inhalable (PM10), and coarse mode particle mass (PM(2.5-10)) for the conterminous United States (U.S.). These models expand our previously developed models for the Northeastern and Midwestern U.S. by virtue of their larger spatial domain, their inclusion of an additional 5 years of PM data to develop predictions through 2007, and their use of refined geographic covariates for population density and point-source PM emissions. Covariate selection and model validation were performed using 10-fold cross-validation (CV).

RESULTS

The PM2.5 models had high predictive accuracy (CV R2=0.77 for both 1988-1998 and 1999-2007). While model performance remained strong, the predictive ability of models for PM10 (CV R2=0.58 for both 1988-1998 and 1999-2007) and PM(2.5-10) (CV R2=0.46 and 0.52 for 1988-1998 and 1999-2007, respectively) was somewhat lower. Regional variation was found in the effects of geographic and meteorological covariates. Models generally performed well in both urban and rural areas and across seasons, though predictive performance varied somewhat by region (CV R2=0.81, 0.81, 0.83, 0.72, 0.69, 0.50, and 0.60 for the Northeast, Midwest, Southeast, Southcentral, Southwest, Northwest, and Central Plains regions, respectively, for PM2.5 from 1999-2007).

CONCLUSIONS

Our models provide estimates of monthly-average outdoor concentrations of PM2.5, PM10, and PM(2.5-10) with high spatial resolution and low bias. Thus, these models are suitable for estimating chronic exposures of populations living in the conterminous U.S. from 1988 to 2007.