Analysis of life expectancy using linear and nonlinear models

Life expectancy has consistently increased over the last 150 years due to improvements in nutrition, medicine, and public health. Several studies found that in many developed countries, life expectancy continued to rise following a nearly linear trend, which was contrary to a common belief that the rate of improvement in life expectancy would decelerate and was fit with an S-shaped curve. Using samples of countries that exhibited a wide range of economic development levels, we explored the change in life expectancy over time by employing both nonlinear and linear models. We then observed if there were any significant differences in estimates between linear models, assuming an auto-correlated error structure. When data did not have a sigmoidal shape, nonlinear growth models sometimes failed to provide meaningful parameter estimates. The existence of an inflection point and asymptotes in the growth models made them inflexible with life expectancy data. In linear models, there was no significant difference in the life expectancy growth rate and future estimates between ordinary least squares (OLS) and generalized least squares (GLS). However, the generalized least squares model was more robust because the data involved time-series variables and residuals were positively correlated. ^

The effect of genotype-by-environment interaction on longitudinal triglyceride profiles in the Bogalusa Heart Study

Triglyceride levels are a component of plasma lipids that are thought to be an important risk factor for coronary heart disease and are influenced by genetic and environmental factors, such as single nucleotide polymorphisms (SNPs), alcohol intake, and smoking. This study used longitudinal data from the Bogalusa Heart Study, a biracial community-based survey of cardiovascular disease risk factors. A sample of 1191 individuals, 4 to 38 years of age, was measured multiple times from 1973 to 2000. The study sample consisted of 730 white and 461 African American participants. Individual growth models were developed in order to assess gene-environment interactions affecting plasma triglycerides over time. After testing for inclusion of significant covariates and interactions, final models, each accounting for the effects of a different SNP, were assessed for fit and normality. After adjustment for all other covariates and interactions, LIPC -514C/T was found to interact with age3, age2, and age and a non-significant interaction of CETP -971G/A genotype with smoking status was found (p = 0.0812). Ever-smokers had higher triglyceride levels than never smokers, but persons heterozygous at this locus, about half of both races, had higher triglyceride levels after smoking cessation compared to current smokers. Since tobacco products increase free fatty acids circulating in the bloodstream, smoking cessation programs have the potential to ultimately reduce triglyceride levels for many persons. However, due to the effect of smoking cessation on the triglyceride levels of CETP -971G/A heterozygotes, the need for smoking prevention programs is also demonstrated. Both smoking cessation and prevention programs would have a great public health impact on minimizing triglyceride levels and ultimately reducing heart disease. ^

Computer simulation of glioma growth and morphology.

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion.

Delta like ligand 4 is a critical regulator of bone marrow cell differentiation into pericytes/vascular smooth muscle cells and is essential for the vasculogenesis that supports the growth of Ewing’s sarcoma

We have previously shown that vasculogenesis, the process by which bone marrow-derived cells are recruited to the tumor and organized to form a blood vessel network de novo, is essential for the growth of Ewing’s sarcoma. We further demonstrated that these bone marrow cells differentiate into pericytes/vascular smooth muscle cells(vSMC) and contribute to the formation of the functional vascular network. The molecular mechanisms that control bone marrow cell differentiation into pericytes/vSMC in Ewing’s sarcoma are poorly understood. Here, we demonstrate that the Notch ligand Delta like ligand 4 (DLL4) plays a critical role in this process. DLL4 is essential for the formation of mature blood vessels during development and in several tumor models. Inhibition of DLL4 causes increased vascular sprouting, decreased pericyte coverage, and decreased vessel functionality. We demonstrate for the first time that DLL4 is expressed by bone marrow-derived pericytes/vascular smooth muscle cells in two Ewing’s sarcoma xenograft models and by perivascular cells in 12 out of 14 patient samples. Using dominant negative mastermind to inhibit Notch, we demonstrate that Notch signaling is essential for bone marrow cell participation in vasculogenesis. Further, inhibition of DLL4 using either shRNA or the monoclonal DLL4 neutralizing antibody YW152F led to dramatic changes in blood vessel morphology and function. Vessels in tumors where DLL4 was inhibited were smaller, lacked lumens, had significantly reduced numbers of bone marrow-derived pericyte/vascular smooth muscle cells, and were less functional. Importantly, growth of TC71 and A4573 tumors was significantly inhibited by treatment with YW152F. Additionally, we provide in vitro evidence that DLL4-Notch signaling is involved in bone marrow-derived pericyte/vascular smooth muscle cell formation outside of the Ewing’s sarcoma environment. Pericyte/vascular smooth muscle cell marker expression by whole bone marrow cells cultured with mouse embryonic stromal cells was reduced when DLL4 was inhibited by YW152F. For the first time, our findings demonstrate a role for DLL4 in bone marrow-derived pericyte/vascular smooth muscle differentiation as well as a critical role for DLL4 in Ewing’s sarcoma tumor growth.

Computer simulation of glioma growth and morphology.

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion.

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

Antibodies targeting hepatoma-derived growth factor as a novel strategy in treating lung cancer.

Hepatoma-derived growth factor (HDGF) is overexpressed in lung cancer and the overexpression correlates with aggressive biological behaviors and poor clinical outcomes. We developed anti-HDGF monoclonal antibodies and tested their antitumor activity in lung cancer xenograft models. We also determined biological effects in tumors treated with the antibody alone or in combination with bevacizumab/avastin (an anti-vascular endothelial growth factor antibody) and/or gemcitabine (a chemotherapeutic agent). We found the anti-HDGF was effective to inhibit tumor growth in non-small cell lung cancer xenograft models. In the A549 model, compared with control IgG, tumor growth was substantially inhibited in animals treated with anti-HDGF antibodies, particularly HDGF-C1 (P = 0.002) and HDGF-H3 (P = 0.005). When HDGF-H3 was combined with either bevacizumab or gemcitabine, we observed enhanced tumor growth inhibition, particularly when the three agents were used together. HDGF-H3-treated tumors exhibited significant reduction of microvessel density with a pattern distinctive from the microvessel reduction pattern observed in bevacizumab-treated tumors. HDGF-H3-treated but not bevacizumab-treated tumors also showed a significant increase of apoptosis. Interestingly, many of the apoptotic cells in HDGF-H3-treated tumors are stroma cells, suggesting that the mechanism of the antitumor activity is, at least in part, through disrupting formation of tumor-stroma structures. Our results show that HDGF is a novel therapeutic target for lung cancer and can be effectively targeted by an antibody-based approach.

LY2109761, a novel transforming growth factor beta receptor type I and type II dual inhibitor, as a therapeutic approach to suppressing pancreatic cancer metastasis.

Most pancreatic cancer patients present with inoperable disease or develop metastases after surgery. Conventional therapies are usually ineffective in treating metastatic disease. It is evident that novel therapies remain to be developed. Transforming growth factor beta (TGF-beta) plays a key role in cancer metastasis, signaling through the TGF-beta type I/II receptors (TbetaRI/II). We hypothesized that targeting TbetaRI/II kinase activity with the novel inhibitor LY2109761 would suppress pancreatic cancer metastatic processes. The effect of LY2109761 has been evaluated on soft agar growth, migration, invasion using a fibroblast coculture model, and detachment-induced apoptosis (anoikis) by Annexin V flow cytometric analysis. The efficacy of LY2109761 on tumor growth, survival, and reduction of spontaneous metastasis have been evaluated in an orthotopic murine model of metastatic pancreatic cancer expressing both luciferase and green fluorescence proteins (L3.6pl/GLT). To determine whether pancreatic cancer cells or the cells in the liver microenvironment were involved in LY2109761-mediated reduction of liver metastasis, we used a model of experimental liver metastasis. LY2109761 significantly inhibited the L3.6pl/GLT soft agar growth, suppressed both basal and TGF-beta1-induced cell migration and invasion, and induced anoikis. In vivo, LY2109761, in combination with gemcitabine, significantly reduced the tumor burden, prolonged survival, and reduced spontaneous abdominal metastases. Results from the experimental liver metastasis models indicate an important role for targeting TbetaRI/II kinase activity on tumor and liver microenvironment cells in suppressing liver metastasis. Targeting TbetaRI/II kinase activity on pancreatic cancer cells or the cells of the liver microenvironment represents a novel therapeutic approach to prevent pancreatic cancer metastasis.

The use of second-generation antipsychotics and the changes in physical growth in children and adolescents with perinatally acquired HIV.

Second-generation antipsychotics (SGAs) are increasingly prescribed to treat psychiatric symptoms in pediatric patients infected with HIV. We examined the relationship between prescribed SGAs and physical growth in a cohort of youth with perinatally acquired HIV-1 infection. Pediatric AIDS Clinical Trials Group (PACTG), Protocol 219C (P219C), a multicenter, longitudinal observational study of children and adolescents perinatally exposed to HIV, was conducted from September 2000 until May 2007. The analysis included P219C participants who were perinatally HIV-infected, 3-18 years old, prescribed first SGA for at least 1 month, and had available baseline data prior to starting first SGA. Each participant prescribed an SGA was matched (based on gender, age, Tanner stage, baseline body mass index [BMI] z score) with 1-3 controls without antipsychotic prescriptions. The main outcomes were short-term (approximately 6 months) and long-term (approximately 2 years) changes in BMI z scores from baseline. There were 236 participants in the short-term and 198 in the long-term analysis. In linear regression models, youth with SGA prescriptions had increased BMI z scores relative to youth without antipsychotic prescriptions, for all SGAs (short-term increase = 0.192, p = 0.003; long-term increase = 0.350, p < 0.001), and for risperidone alone (short-term = 0.239, p = 0.002; long-term = 0.360, p = 0.001). Participants receiving both protease inhibitors (PIs) and SGAs showed especially large increases. These findings suggest that growth should be carefully monitored in youth with perinatally acquired HIV who are prescribed SGAs. Future research should investigate the interaction between PIs and SGAs in children and adolescents with perinatally acquired HIV infection.

Impact of medications prescribed for treatment of attention-deficit hyperactivity disorder on physical growth in children and adolescents with HIV.

OBJECTIVE: To examine the relationships between physical growth and medications prescribed for symptoms of attention-deficit hyperactivity disorder in children with HIV. METHODS: Analysis of data from children with perinatally acquired HIV (N = 2251; age 3-19 years), with and without prescriptions for stimulant and nonstimulant medications used to treat attention-deficit hyperactivity disorder, in a long-term observational study. Height and weight measurements were transformed to z scores and compared across medication groups. Changes in z scores during a 2-year interval were compared using multiple linear regression models adjusting for selected covariates. RESULTS: Participants with (n = 215) and without (n = 2036) prescriptions were shorter than expected based on US age and gender norms (p < .001). Children without prescriptions weighed less at baseline than children in the general population (p < .001) but gained height and weight at a faster rate (p < .001). Children prescribed stimulants were similar to population norms in baseline weight; their height and weight growth velocities were comparable with the general population and children without prescriptions (for weight, p = .511 and .100, respectively). Children prescribed nonstimulants had the lowest baseline height but were similar to population norms in baseline weight. Their height and weight growth velocities were comparable with the general population but significantly slower than children without prescriptions (p = .01 and .02, respectively). CONCLUSION: The use of stimulants to treat symptoms of attention-deficit hyperactivity disorder does not significantly exacerbate the potential for growth delay in children with HIV and may afford opportunities for interventions that promote physical growth. Prospective studies are needed to confirm these findings.

The detrimental role of angiotensin receptor agonistic autoantibodies in intrauterine growth restriction seen in preeclampsia.

Growth-restricted fetuses are at risk for a variety of lifelong medical conditions. Preeclampsia, a life-threatening hypertensive disorder of pregnancy, is associated with fetuses who suffer from intrauterine growth restriction (IUGR). Recently, emerging evidence indicates that preeclamptic women harbor AT(1) receptor agonistic autoantibodies (AT(1)-AAs) that contribute to the disease features. However, the exact role of AT(1)-AAs in IUGR and the underlying mechanisms have not been identified. We report that these autoantibodies are present in the cord blood of women with preeclampsia and retain the ability to activate AT(1) receptors. Using an autoantibody-induced animal model of preeclampsia, we show that AT(1)-AAs cross the mouse placenta, enter fetal circulation, and lead to small fetuses with organ growth retardation. AT(1)-AAs also induce apoptosis in the placentas of pregnant mice, human villous explants, and human trophoblast cells. Finally, autoantibody-induced IUGR and placental apoptosis are diminished by either losartan or an autoantibody-neutralizing peptide. Thus, these studies identify AT(1)-AA as a novel causative factor of preeclampsia-associated IUGR and offer two possible underlying mechanisms: a direct detrimental effect on fetal development by crossing the placenta and entering fetal circulation, and indirectly through AT(1)-AA-induced placental damage. Our findings highlight AT(1)-AAs as important therapeutic targets.

THE MECHANISM OF TUMORIGENESIS IN THE IMMORTALIZED HUMAN PANCREATIC CELL LINES: CELL CULTURE MODELS OF HUMAN PANCREATIC CANCER

The mechanism of tumorigenesis in the immortalized human pancreatic cell lines: cell culture models of human pancreatic cancer Pancreatic ductal adenocarcinoma (PDAC) is the most lethal cancer in the world. The most common genetic lesions identified in PDAC include activation of K-ras (90%) and Her2 (70%), loss of p16 (95%) and p14 (40%), inactivation p53 (50-75%) and Smad4 (55%). However, the role of these signature gene alterations in PDAC is still not well understood, especially, how these genetic lesions individually or in combination contribute mechanistically to human pancreatic oncogenesis is still elusive. Moreover, a cell culture transformation model with sequential accumulation of signature genetic alterations in human pancreatic ductal cells that resembles the multiple-step human pancreatic carcinogenesis is still not established. In the present study, through the stepwise introduction of the signature genetic alterations in PDAC into the HPV16-E6E7 immortalized human pancreatic duct epithelial (HPDE) cell line and the hTERT immortalized human pancreatic ductal HPNE cell line, we developed the novel experimental cell culture transformation models with the most frequent gene alterations in PDAC and further dissected the molecular mechanism of transformation. We demonstrated that the combination of activation of K-ras and Her2, inactivation of p16/p14 and Smad4, or K-ras mutation plus p16 inactivation, was sufficient for the tumorigenic transformation of HPDE or HPNE cells respectively. We found that these transformed cells exhibited enhanced cell proliferation, anchorage-independent growth in soft agar, and grew tumors with PDAC histopathological features in orthotopic mouse model. Molecular analysis showed that the activation of K-ras and Her2 downstream effector pathways –MAPK, RalA, FAK, together with upregulation of cyclins and c-myc were involved in the malignant transformation. We discovered that MDM2, BMP7 and Bmi-1 were overexpressed in the tumorigenic HPDE cells, and that Smad4 played important roles in regulation of BMP7 and Bmi-1 gene expression and the tumorigenic transformation of HPDE cells. IPA signaling pathway analysis of microarray data revealed that abnormal signaling pathways are involved in transformation. This study is the first complete transformation model of human pancreatic ductal cells with the most common gene alterations in PDAC. Altogether, these novel transformation models more closely recapitulate the human pancreatic carcinogenesis from the cell origin, gene lesion, and activation of specific signaling pathway and histopathological features.

Insulin-associated changes in carnitine palmitoyltransferase activity are mediated through the insulin-like growth factor I pathway in the cultured neonatal rat cardiac myocyte

The mitochondrial carnitine palmitoyltransferase (CPT) system is composed of two proteins, CPT-I and CPT-II, involved in the transport of fatty acids into the mitochondrial matrix to undergo $\beta$-oxidation. CPT-I is located outside the inner membrane and CPT-II is located on the inner aspect of the inner membrane. The CPT proteins are distinct with different molecular weights and activities. The malonyl-CoA sensitivity of CPT-I has been proposed as a regulatory step in $\beta$-oxidation. Using the neonatal rat cardiac myocyte, assays were designed to discriminate between these activities in situ using digitonin and Triton X-100. With this methodology, we are able to determine the involvement of the IGF-I pathway in the insulin-mediated increase in CPT activities. Concentrations of digitonin up to 25 $\mu$M fail to release citrate synthase from the mitochondrial matrix or alter the malonyl-CoA sensitivity of CPT-I. If the mitochondrial matrix was exposed, malonyl-CoA insensitive CPT-II would reduce malonyl-CoA sensitivity. In contrast to digitonin, Triton X-100 (0.15%) releases citrate synthase from the matrix and exposes CPT-II. CPT-II activity is confirmed by the absence of malonyl-CoA sensitivity. To examine the effects of various agents on the expression and/or activity of CPT, it is necessary to use serum-free medium to eliminate mitogenic effects of serum proteins. Comparison of different media to optimize CPT activity and cell viability resulted in the decision to use Dulbecco's Modified Eagle medium supplemented with transferrin. In three established models of cardiac hypertrophy using the neonatal rat cardiac myocyte there is a significant increase in CPT-I and CPT-II activity in the treated cells. Analogous to the situation seen in the hypertrophy model, insulin also significantly increases the activity of the mitochondrial proteins CPT-I, CPT-II and cytochrome oxidase with a coinciding increase the expression of CPT-II and cytochrome oxidase mRNA. The removal of serum increases the I$\sb{50}$ (concentration of inhibitor that halves enzyme activity) of CPT-I for malonyl-CoA by four-fold. Incubation with insulin returns I$\sb{50}$ values to serum levels. Incubation with insulin significantly increases malonyl-CoA and ATP levels in the cells with a resulting reduction in palmitate oxidation. Once malonyl-CoA inhibition of CPT-I is removed by permeabilizing the cells, insulin significantly increases the oxidation of palmitoyl-CoA in a manner which parallels the increase in CPT-I activity. Interestingly, CPT-II activity increases significantly only at the tissue culture concentration (1.7 $\mu$M) of insulin suggesting that the IGF-I pathway may be involved. Supporting a role for the IGF-I pathway in the insulin-induced increase in CPT activity is the significant increase in the synthesis of both cellular and mitochondrial proteins as well as increased synthesis of CPT-II. Consistent with an IGF-mediated pathway for the effect of insulin, IGF-I (10 ng/ml) significantly increases the activities of both CPT-I and -II. An IGF-I analogue which inhibits the autophosphorylation of the IGF-I receptor blunts the insulin-mediated increase in CPT-I and -II activity by greater than 70% and virtually eliminates the IGF-I response by greater than 90%. This is the first study to demonstrate the involvement of the IGF-I pathway in the regulation of mitochondrial protein expression, e.g. CPT. ^

Left ventricular growth from age 8 to 18 and its anthropometric determinants: Longitudinal results from Project Heartbeat!

Left ventricular mass (LVM) is a strong predictor of cardiovascular disease (CVD) in adults. However, normal growth of LVM in healthy children is not well understood, and previous results on independent effects of body size and body fatness on LVM have been inconsistent. The purpose of this study was (1) to establish the normal growth curve of LVM from age 8 to age 18, and evaluate the determinants of change in LVM with age, and (2) to assess the independent effects of body size and body fatness on LVM.^ In Project HeartBeat!, 678 healthy children aged 8, 11 and 14 years at baseline were enrolled and examined at 4-monthly intervals for up to 4 years. A synthetic cohort with continuous observations from age 8 to 18 years was constructed. A total of 4608 LVM measurements was made from M-mode echocardiography. The multilevel linear model was used for analysis.^ Sex-specific trajectories of normal growth of LVM from age 8 to 18 was displayed. On average, LVM was 15 g higher in males than females. Average LVM increased linearly in males from 78 g at age 8 to 145 g at age 18. For females, the trajectory was curvilinear, nearly constant after age 14. No significant racial differences were found. After adjustment for the effects of body size and body fatness, average LVM decreased slightly from age 8 to 18, and sex differences in changes of LVM remained constant.^ The impact of body size on LVM was examined by adding to a basic LVM-sex-age model one of 9 body size indicators. The impact of body fatness was tested by further introducing into each of the 9 LVM models (with one or another of the body size indicators) one of 4 body fatness indicators, yielding 36 models with different body size and body fatness combinations. The results indicated that effects of body size on LVM can be distinguished between fat-free body mass and fat body mass, both being independent, positive predictors. The former is the stronger determinant. When a non-fat-free body size indicator is used as predictor, the estimated residual effect of body fatness on LVM becomes negative. ^

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^