Multivariate semiparametric regression models for longitudinal data

Multiple-outcome longitudinal data are abundant in clinical investigations. For example, infections with different pathogenic organisms are often tested concurrently, and assessments are usually taken repeatedly over time. It is therefore natural to consider a multivariate modeling approach to accommodate the underlying interrelationship among the multiple longitudinally measured outcomes. This dissertation proposes a multivariate semiparametric modeling framework for such data. Relevant estimation and inference procedures as well as model selection tools are discussed within this modeling framework. The first part of this research focuses on the analytical issues concerning binary data. The second part extends the binary model to a more general situation for data from the exponential family of distributions. The proposed model accounts for the correlations across the outcomes as well as the temporal dependency among the repeated measures of each outcome within an individual. An important feature of the proposed model is the addition of a bivariate smooth function for the depiction of concurrent nonlinear and possibly interacting influences of two independent variables on each outcome. For model implementation, a general approach for parameter estimation is developed by using the maximum penalized likelihood method. For statistical inference, a likelihood-based resampling procedure is proposed to compare the bivariate nonlinear effect surfaces across the outcomes. The final part of the dissertation presents a variable selection tool to facilitate model development in practical data analysis. Using the adaptive least absolute shrinkage and selection operator (LASSO) penalty, the variable selection tool simultaneously identifies important fixed effects and random effects, determines the correlation structure of the outcomes, and selects the interaction effects in the bivariate smooth functions. Model selection and estimation are performed through a two-stage procedure based on an expectation-maximization (EM) algorithm. Simulation studies are conducted to evaluate the performance of the proposed methods. The utility of the methods is demonstrated through several clinical applications

A generalized semiparametric mixed model for analysis of multivariate health care utilization data

Health care utilization is an outcome of interest in health services research. Two frequently studied forms of utilization are counts of emergency department (ED) visits and hospital admissions. These counts collectively convey a sense of disease exacerbation and cost escalation. Different types of event counts from the same patient form a vector of correlated outcomes. Traditional analysis typically model such outcomes one at a time, ignoring the natural correlations between different events, and thus failing to provide a full picture of patient care utilization. In this research, we propose a multivariate semiparametric modeling framework for the analysis of multiple health care events following the exponential family of distributions in a longitudinal setting. Bivariate nonparametric functions are incorporated to assess the concurrent nonlinear influences of independent variables as well as their interaction effects on the outcomes. The smooth functions are estimated using the thin plate regression splines. A maximum penalized likelihood method is used for parameter estimation. The performance of the proposed method was evaluated through simulation studies. To illustrate the method, we analyzed data from a clinical trial in which ED visits and hospital admissions were considered as bivariate outcomes

A sexually transmitted infection screening algorithm based on semiparametric regression models

Sexually transmitted infections (STIs) with Chlamydia trachomatis, Neisseria gonorrhoeae, and Trichomonas vaginalis are among the most common infectious diseases in the United States, disproportionately affecting young women. Because a significant portion of the infections present no symptoms, infection control relies primarily on disease screening. However, universal STI screening in a large population can be expensive. In this paper, we propose a semiparametric model-based screening algorithm. The model quantifies organism-specific infection risks in individual subjects and accounts for the within-subject interdependence of the infection outcomes of different organisms and the serial correlations among the repeated assessments of the same organism. Bivariate thin-plate regression spline surfaces are incorporated to depict the concurrent influences of age and sexual partners on infection acquisition. Model parameters are estimated by using a penalized likelihood method. For inference, we develop a likelihood-based resampling procedure to compare the bivariate effect surfaces across outcomes. Simulation studies are conducted to evaluate the model fitting performance. A screening algorithm is developed using data collected from an epidemiological study of young women at increased risk of STIs. We present evidence that the three organisms have distinct age and partner effect patterns; for C. trachomatis, the partner effect is more pronounced in younger adolescents. Predictive performance of the proposed screening algorithm is assessed through a receiver operating characteristic analysis. We show that the model-based screening algorithm has excellent accuracy in identifying individuals at increased risk, and thus can be used to assist STI screening in clinical practice

Ideological and Political Construction Based on the “Scenario-Action” Teaching Mode in the Major of French Language: A Case Study of “French Reading” Course

This paper refers to the teaching design and practice in the field of Ideological and political construction of curriculum in French reading course, the authors will firstly analyse the background of our exploration of the new teaching and learning mode, and then, represent the main cores of this mode, in order to promote the construction of the ideological and political in curriculum of not only the French reading course, but all courses of the French language

The Effect of Body Mass Index on Blood Pressure Varies by Race among Children

poster abstractThe effect of adiposity on blood pressure (BP) intensifies as children become obese, and black children tend to have greater body mass index (BMI) and higher BP than age-matched white children. But few studies have compared the magnitude of the effect of BMI on BP in obese black and white children. We used a novel analytic technique to examine the influence of age and BMI on BP in children seen at a hospital-based obesity clinic. The study sample included 821 overweight and obese children (age and sex adjusted BMI% ranged from 87% to 100%; 306 males, 515 females, 362 blacks, and 459 whites). The mean age of the study subjects was 11.72 ± 3.48 years, the mean BMI was 36.22 ± 8.51 kg/m2, and the mean systolic and diastolic BP were 109.36 ± 16.10 and 69.99 ± 10.48 mmHg, respectively. In comparison, blacks and whites were similar in age (11.89 vs 11.58; p=0.197); while black patients had higher BMI (37.32 vs 35.34 kg/m2; p=0.0010), and higher systolic BP% than whites (58.71 vs 50.72 mmHg; p=0.00062). Semiparametric regression models showed that while age and BMI were significantly associated with systolic BP% in both race groups, black children had significantly higher BP% values as compared with white children of the same age and BMI (Fig 1 (a) and (b)). Although BP% values have taken into account the effect of age, there continued to be a significant effect of age on BP% in black children. In conclusion, among children referred for treatment of obesity, black children are at a significantly greater risk for having elevated BP as compared with their white peers of similar age and severity of obesity. Further research is needed to better understand this population-specific intensification of the adiposity effect on BP in obese black children

The effect of body mass index on blood pressure varies by race among obese children

Objective: Previous studies have shown that the effect of adiposity on blood pressure (BP) intensifies as children become increasingly obese. Black children tend to have greater body mass index (BMI) and higher BP than age-matched white children. It is unclear whether the BP effects of BMI are race-specific among black and white children, and data on obese Hispanic children are sparse. We compared the BP effect of BMI in obese white, black, and Hispanic children. Methods: We examined the medical records of children enrolled in a pediatric obesity clinic. Height, weight, BP, and fasting insulin were assessed as part of routine clinical care. The concurrent effects of age and BMI on BP percentile values were examined using semiparametric regression, which allows the accommodation of nonlinear effects. Results: The study included 873 children (338 male; 354 black, 447 white, 72 Hispanic; 11.7±3.5 years, BMI 36.2±8.5 kg/m2). While BMI Z-scores were similar among the groups, systolic BP (SBP) was higher in black children and Hispanic children (white: 107 mm Hg; black: 112 mm Hg; Hispanic: 112 mm Hg; p=0.0001). Age, sex, and height-adjusted SBP percentiles were significantly different among the three groups (white: 50; black: 59; Hispanic: 59; p=0.0006). In children of the same age, BP was higher at any given BMI in black children and Hispanic children. Conclusions: Among children referred for treatment of obesity, black children and Hispanic children are at a greater risk for having elevated BP when compared to white children of similar age and BMI

Depressive symptoms are associated with fasting insulin resistance in obese youth

BACKGROUND: In adults, depressive symptoms are positively associated with insulin resistance. OBJECTIVE: To determine whether an association exists between depressive symptoms and markers of insulin resistance in youth. METHODS: This study used a retrospective review of data from an obesity clinic. We evaluated the association between depressive symptoms (Children's Depression Inventory, CDI) and fasting insulin and homeostatic model assessment-insulin resistance (HOMA-IR) in obese youth (n = 207, age 10-18 years). Individuals with lower vs. higher CDI T-scores (<65 vs. ≥65) were compared; this cut-point is accepted as indicating the possibility of clinical depression. Multiple linear regression was used to evaluate relationships between CDI T-scores and insulin resistance. RESULTS: Fasting insulin and HOMA-IR values were 40% higher in patients with higher CDI T-scores (P = 0.04). After accounting for gender, race, age and body mass index, CDI T-score remained associated with HOMA-IR, although the strength of the association was small (b = 0.007, P = 0.049). CONCLUSIONS: Relationships between depressive symptoms and insulin resistance should be considered when evaluating obese youth

Proportion of Osteoporotic Women Remaining at Risk for Fracture Despite Adherence to Oral Bisphosphonates

Background Adherence to oral bisphosphonates is often low, but even adherent patients may remain at elevated fracture risk. The goal of this study was to estimate the proportion of bisphosphonate-adherent women remaining at high risk of fracture. Methods A retrospective cohort of women aged 50 years and older, adherent to oral bisphosphonates for at least two years was identified, and data were extracted from a multi-system health information exchange. Adherence was defined as having a dispensed medication possession ratio ≥ 0.8. The primary outcome was clinical occurrence of: low trauma fracture (months 7–36), persistent T-score ≤ − 2.5 (months 13–36), decrease in bone mineral density (BMD) at any skeletal site ≥ 5%, or the composite of any one of these outcomes. Results Of 7435 adherent women, 3110 had either pre- or post-adherent DXA data. In the full cohort, 7% had an incident osteoporotic fracture. In 601 women having both pre- and post-adherent DXA to evaluate BMD change, 6% had fractures, 22% had a post-treatment T-score ≤ − 2.5, and 16% had BMD decrease by ≥ 5%. The composite outcomes occurred in 35%. Incident fracture was predicted by age, previous fracture, and a variety of co-morbidities, but not by race, glucocorticoid treatment or type of bisphosphonate. Conclusion Despite bisphosphonate adherence, 7% had incident osteoporotic fractures and 35% had either fracture, decreases in BMD, or persistent osteoporotic BMD, representing a substantial proportion of treated patients in clinical practices remaining at risk for future fractures. Further studies are required to determine the best achievable goals for osteoporosis therapy, and which patients would benefit from alternate therapies

Intensity of lipid lowering with statin therapy in patients with cerebrovascular disease versus coronary artery disease: Insights from the PALM Registry

Background Current treatment guidelines strongly recommend statin therapy for secondary prevention. However, it remains unclear whether patients\u27 perceptions of cardiovascular risk, beliefs on cholesterol, or the intensity of prescribed statin therapy differs for patients with coronary artery disease (CAD) versus cerebrovascular disease (CeVD) versus both CAD and CeVD (CAD&CeVD). Methods and Results The PALM (Patient and Provider Assessment of Lipid Management) registry collected data on statin use, intensity, and core laboratory low-density lipoprotein cholesterol levels for 3232 secondary prevention patients treated at 133 US clinics. Among individuals with CeVD only (n=403), CAD only (n=2202), and CeVD&CAD (n=627), no significant differences were observed in patient-perceived cardiovascular disease risk, beliefs on cholesterol lowering, or perceived effectiveness and safety of statin therapy. However, patients with CeVD only were less likely to receive any statin therapy (76.2% versus 86.2%; adjusted odds ratio 0.64, 95% CI 0.45-0.91), or guideline-recommended statin intensity (34.6% versus 50.4%; adjusted odds ratio 0.60, 95% CI 0.45-0.81) than those with CAD only. Individuals with CeVD only were also less likely to achieve low-density lipoprotein cholesterol \u3c100 mg/dL (59.2% versus 69.7%; adjusted odds ratio 0.79, 95% CI 0.64-0.99) than individuals with CAD alone. There were no significant differences in the use of any statin therapy or guideline-recommended statin intensity between individuals with CAD&CeVD and those with CAD only. Conclusions Despite lack of significant differences in patient-perceived cardiovascular risk or statin beliefs, patients with CeVD were significantly less likely to receive higher intensity statin or achieve low-density lipoprotein cholesterol \u3c100 mg/dL than those with CAD only

Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer

Background We aimed to develop a radiomic model based on pre-treatment computed tomography (CT) to predict the pathological complete response (pCR) in patients with rectal cancer after neoadjuvant treatment and tried to integrate our model with magnetic resonance imaging (MRI)-based radiomic signature. Methods This was a secondary analysis of the FOWARC randomized controlled trial. Radiomic features were extracted from pre-treatment portal venous-phase contrast-enhanced CT images of 177 patients with rectal cancer. Patients were randomly allocated to the primary and validation cohort. The least absolute shrinkage and selection operator regression was applied to select predictive features to build a radiomic signature for pCR prediction (rad-score). This CT-based rad-score was integrated with clinicopathological variables using gradient boosting machine (GBM) or MRI-based rad-score to construct comprehensive models for pCR prediction. The performance of CT-based model was evaluated and compared by receiver operator characteristic (ROC) curve analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare CT-based rad-score, MRI-based rad-score and the combined rad-score. Results We developed a CT-based rad-score for pCR prediction and a gradient boosting machine (GBM) model was built after clinicopathological variables were incorporated, with improved AUCs of 0.997 [95% CI 0.990–1.000] and 0.822 [95% CI 0.649–0.995] in the primary and validation cohort, respectively. Moreover, we constructed a combined model of CT- and MRI-based radiomic signatures that achieve better AIC (75.49 vs. 81.34 vs.82.39) than CT-based rad-score (P = 0.005) and MRI-based rad-score (P = 0.003) alone did. Conclusions The CT-based radiomic models we constructed may provide a useful and reliable tool to predict pCR after neoadjuvant treatment, identify patients that are appropriate for a 'watch and wait' approach, and thus avoid overtreatment. Moreover, the CT-based radiomic signature may add predictive value to the MRI-based models for clinical decision making