Examining the relative influence of familial, genetic, and environmental covariate information in flexible risk models.
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ABSTRACT:
SUBMITTER: Bravo HC
PROVIDER: S-EPMC2677979 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In covariate (sub)models of population pharmacokinetic models, most covariates are normalized to the median value; however, for body weight, normalization to 70 kg or 1 kg is often applied. In this article, we illustrate the impact of normalization weight on the precision of population clearance (CLpop) parameter estimates. The influence of normalization weight (70, 1 kg or median weight) on the precision of the CLpop estimate, expressed as relative standard error (RSE), was illustrated using data from a pharmacokinetic study in neonates with a median weight of 2.7 kg. In addition, a simulation study was performed to show the impact of normalization to 70 kg in pharmacokinetic studies with paediatric or obese patients. The RSE of the CLpop parameter estimate in the neonatal dataset was lowest with normalization to median weight (8.1%), compared with normalization to 1 kg (10.5%) or 70 kg (48.8%). Typical clearance (CL) predictions were independent of the normalization weight used. Simulations showed that the increase in RSE of the CLpop estimate with 70 kg normalization was highest in studies with a narrow weight range and a geometric mean weight away from 70 kg. When, instead of normalizing with median weight, a weight outside the observed range is used, the RSE of the CLpop estimate will be inflated, and should therefore not be used for model selection. Instead, established mathematical principles can be used to calculate the RSE of the typical CL (CLTV) at a relevant weight to evaluate the precision of CL predictions.
| S-EPMC6325985 | biostudies-literature
Project description:ObjectiveTo examine familial aggregation of gout and to estimate the heritability and environmental contributions to gout susceptibility in the general population.MethodsUsing data from the National Health Insurance (NHI) Research Database in Taiwan, we conducted a nationwide cross-sectional study of data collected from 22 643 748 beneficiaries of the NHI in 2004; among them 1 045 059 individuals had physician-diagnosed gout. We estimated relative risks (RR) of gout in individuals with affected first-degree and second-degree relatives and relative contributions of genes (heritability), common environment shared by family members and non-shared environment to gout susceptibility.ResultsRRs for gout were significantly higher in individuals with affected first-degree relatives (men, 1.91 (95% CI 1.90 to 1.93); women, 1.97 (95% CI 1.94 to 1.99)) and also in those with affected second-degree relatives (men, 1.27 (95% CI 1.23 to 1.31); women, 1.40 (95% CI 1.35 to 1.46)). RRs (95% CIs) for individuals with an affected twin, sibling, offspring, parent, grandchild, nephew/niece, uncle/aunt and grandparent were 8.02 (6.95 to 9.26), 2.59 (2.54 to 2.63), 1.96 (1.95 to 1.97), 1.93 (1.91 to 1.94), 1.48 (1.43 to 1.53), 1.40 (1.32 to 1.47), 1.31 (1.24 to 1.39), and 1.26 (1.21 to 1.30), respectively. The relative contributions of heritability, common and non-shared environmental factors to phenotypic variance of gout were 35.1, 28.1 and 36.8% in men and 17.0, 18.5 and 64.5% in women, respectively.ConclusionsThis population-based study confirms that gout aggregates within families. The risk of gout is higher in people with a family history. Genetic and environmental factors contribute to gout aetiology, and the relative contributions are sexually dimorphic.
| S-EPMC4316854 | biostudies-literature
Project description:Meta-analysis is widely popular for synthesizing information on common parameters of interest across multiple studies because of its logistical convenience and statistical efficiency. We develop a generalized meta-analysis approach to combining information on multivariate regression parameters across multiple studies that have varying levels of covariate information. Using algebraic relationships among regression parameters in different dimensions, we specify a set of moment equations for estimating parameters of a maximal model through information available from sets of parameter estimates for a series of reduced models from the different studies. The specification of the equations requires a reference dataset for estimating the joint distribution of the covariates. We propose to solve these equations using the generalized method of moments approach, with the optimal weighting of the equations taking into account uncertainty associated with estimates of the parameters of the reduced models. We describe extensions of the iterated reweighted least-squares algorithm for fitting generalized linear regression models using the proposed framework. Based on the same moment equations, we also develop a diagnostic test for detecting violations of underlying model assumptions, such as those arising from heterogeneity in the underlying study populations. The proposed methods are illustrated with extensive simulation studies and a real-data example involving the development of a breast cancer risk prediction model using disparate risk factor information from multiple studies.
| S-EPMC6690173 | biostudies-literature
Project description:BACKGROUND:Within cancer care, dynamic evaluations of the loss in expectation of life provides useful information to patients as well as physicians. The loss of lifetime function yields the conditional loss in expectation of life given survival up to a specific time point. Due to the inevitable censoring in time-to-event data, loss of lifetime estimation requires extrapolation of both the patient and general population survival function. In this context, the accuracy of different extrapolation approaches has not previously been evaluated. METHODS:The loss of lifetime function was computed by decomposing the all-cause survival function using the relative and general population survival function. To allow extrapolation, the relative survival function was fitted using existing parametric relative survival models. In addition, we introduced a novel mixture cure model suitable for extrapolation. The accuracy of the estimated loss of lifetime function using various extrapolation approaches was assessed in a simulation study and by data from the Danish Cancer Registry where complete follow-up was available. In addition, we illustrated the proposed methodology by analyzing recent data from the Danish Lymphoma Registry. RESULTS:No uniformly superior extrapolation method was found, but flexible parametric mixture cure models and flexible parametric relative survival models seemed to be suitable in various scenarios. CONCLUSION:Using extrapolation to estimate the loss of lifetime function requires careful consideration of the relative survival function outside the available follow-up period. We propose extensive sensitivity analyses when estimating the loss of lifetime function.
| S-EPMC6350283 | biostudies-literature
Project description:ADHD is the most common neurobehavioral disorder in school-aged children. In addition to genetic factors, environmental influences or gene x environmental interactions also play an important role in ADHD. One example of a well studied environmental risk factor for ADHD is exposure to polychlorinated biphenyls (PCBs). In this study, we investigated whether the well-established genetic model of ADHD based on the Spontaneously Hypertensive Rat (SHR) and a well established PCB-based model of ADHD exhibited similar molecular changes in brain circuits involved in ADHD. The brains from 28 male rats (8 SHR, 8 Sprague-Dawley (SD) controls, 8 Wistar-Kyoto (WKY) controls, and 4 PCB-exposed SD rats) were harvested at postnatal day 55-65 and RNA was isolated from six brain regions of interest. The RNA was analyzed for differences in expression of a set of 308 probe sets interrogating 218 unique genes considered highly relevant to ADHD or epigenetic gene regulation using the Rat RAE 230 2.0 GeneChip (Affymetrix). Selected observations were confirmed by real time quantitative RT-PCR. The results show that the expression levels of genes Gnal, COMT, Adrbk1, Ntrk2, Hk1, Syt11 and Csnk1a1 were altered in both the SHR rats and the PCB-exposed SD rats. Arrb2, Stx12, Aqp6, Syt1, Ddc and Pgk1 expression levels were changed only in the PCB-exposed SD rats. Genes with altered expression only in the SHRs included Oprm1, Calcyon, Calmodulin, Lhx1 and Hes6.The epigenetic genes Crebbp, Mecp2 and Hdac5 are significantly altered in both models. The data provide strong evidence that genes and environment can affect different set of genes in two different models of ADHD and yet result in the similar disease-like symptoms. The brains from 28 male rats (8 SHR, 8 Sprague-Dawley (SD) controls, 8 Wistar-Kyoto (WKY) controls, and 4 PCB-exposed SD rats) were harvested at postnatal day 55-65 and RNA was isolated from six brain regions of interest. The RNA was analyzed for differences in expression of a set of 308 probe sets interrogating 218 unique genes considered highly relevant to ADHD or epigenetic gene regulation using the Rat RAE 230 2.0 GeneChip (Affymetrix). Selected observations were confirmed by real time quantitative RT-PCR.
2008-11-20 | E-GEOD-12457 | biostudies-arrayexpress
Project description:ADHD is the most common neurobehavioral disorder in school-aged children. In addition to genetic factors, environmental influences or gene x environmental interactions also play an important role in ADHD. One example of a well studied environmental risk factor for ADHD is exposure to polychlorinated biphenyls (PCBs). In this study, we investigated whether the well-established genetic model of ADHD based on the Spontaneously Hypertensive Rat (SHR) and a well established PCB-based model of ADHD exhibited similar molecular changes in brain circuits involved in ADHD. The brains from 28 male rats (8 SHR, 8 Sprague-Dawley (SD) controls, 8 Wistar-Kyoto (WKY) controls, and 4 PCB-exposed SD rats) were harvested at postnatal day 55-65 and RNA was isolated from six brain regions of interest. The RNA was analyzed for differences in expression of a set of 308 probe sets interrogating 218 unique genes considered highly relevant to ADHD or epigenetic gene regulation using the Rat RAE 230 2.0 GeneChip (Affymetrix). Selected observations were confirmed by real time quantitative RT-PCR. The results show that the expression levels of genes Gnal, COMT, Adrbk1, Ntrk2, Hk1, Syt11 and Csnk1a1 were altered in both the SHR rats and the PCB-exposed SD rats. Arrb2, Stx12, Aqp6, Syt1, Ddc and Pgk1 expression levels were changed only in the PCB-exposed SD rats. Genes with altered expression only in the SHRs included Oprm1, Calcyon, Calmodulin, Lhx1 and Hes6.The epigenetic genes Crebbp, Mecp2 and Hdac5 are significantly altered in both models. The data provide strong evidence that genes and environment can affect different set of genes in two different models of ADHD and yet result in the similar disease-like symptoms.
2008-11-21 | GSE12457 | GEO
Project description:Transmissible infections such as those caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread according to who contacts whom. Therefore, many epidemic models incorporate contact patterns through contact matrices. Contact matrices can be generated from social contact survey data. However, the resulting matrices are often imbalanced, such that the total number of contacts reported by group A with group B do not match those reported by group B with group A. We examined the theoretical influence of imbalanced contact matrices on the estimated basic reproduction number (R0). We then explored how imbalanced matrices may bias model-based epidemic projections using an illustrative simulation model of SARS-CoV-2 with 2 age groups (<15 and ≥15 years). Models with imbalanced matrices underestimated the initial spread of SARS-CoV-2, had later time to peak incidence, and had smaller peak incidence. Imbalanced matrices also influenced cumulative infections observed per age group, as well as the estimated impact of an age-specific vaccination strategy. Stratified transmission models that do not consider contact balancing may generate biased projections of epidemic trajectory and the impact of targeted public health interventions. Therefore, modeling studies should implement and report methods used to balance contact matrices for stratified transmission models.
| S-EPMC10840077 | biostudies-literature
Project description:Environmental exposures and socioeconomic status (SES) are associated with asthma and chronic obstructive pulmonary disease (COPD) morbidity and mortality. Despite efforts to reduce the impact of environmental exposures through regulation and education, knowledge gaps remain. We sought to understand how adults with asthma and COPD perceive and seek information about environmental factors, and how these responses varied by disease or socioeconomic characteristics. Participants with self-reported asthma or COPD enrolled in a digital platform for respiratory disease self-management, consisting of sensors to track medication use and a companion smartphone app, completed an electronic survey exploring perceptions of environmental factors. Using mixed-method analyses, we evaluated differences in responses by disease (asthma vs. COPD), education (≤ vs. > some college), annual household income (< vs. ≥ $50,000), and mean annual residential air pollutant exposure (> vs. ≤80th percentile). Survey responses from 698 participants [500 asthma (72%) and 198 COPD (28%)] were analyzed. A high percentage of participants perceived that environmental factors could influence their symptoms, including: pollen (93% for asthma vs. 86% for COPD), mold (89 vs. 85%), second-hand smoke (89 vs. 83%), and air pollution (84% for both). Participants reported seeking environmental information daily from an average of three sources, preferring mobile apps and television (TV) programs. Significant differences were identified by disease.ConclusionParticipants with asthma and COPD perceive a relationship between their respiratory symptoms and their environment and regularly seek out environmental information. This information can help inform digital health development for respiratory education and self-management.
| S-EPMC9113516 | biostudies-literature
Project description:Developing molecular generative models for directly generating 3D conformation has recently become a hot research area. Here, an autoencoder based generative model was proposed for molecular conformation generation. A unique feature of our method is that the graph information embedded relative coordinate (GIE-RC), satisfying translation and rotation invariance, was proposed as a novel way for encoding molecular three-dimensional structure. Compared with commonly used Cartesian coordinate and internal coordinate, GIE-RC is less sensitive on errors when decoding latent variables to 3D coordinates. By using this method, a complex 3D generation task can be turned into a graph node feature generation problem. Examples were shown that the GIE-RC based autoencoder model can be used for both ligand and peptide conformation generation. Additionally, this model was used as an efficient conformation sampling method to augment conformation data needed in the construction of neural network-based force field.
| S-EPMC9823299 | biostudies-literature
Project description:BackgroundExtrapolation of survival data is a key task in health technology assessments (HTAs), which may be improved by incorporating general population mortality data via relative survival models. Dynamic survival models are a promising method for extrapolation that may be expanded to dynamic relative survival models (DRSMs), a novel development presented here. There are currently neither examples of dynamic models in HTA nor comparisons of DRSMs with other relative survival models when used for survival extrapolation.MethodsAn existing appraisal, for which there had been disagreement over the approach to survival extrapolation, was chosen and the health economic model recreated. The sensitivity of estimates of cost-effectiveness to different model choices (standard survival models, DSMs, and DRSMs) and specifications was examined. The appraisal informed a simulation study to evaluate DRSMs with relative survival models based on both standard and spline-based (flexible) models.ResultsDynamic models provided insight into the behavior of the trend in the hazard function and how it may vary during the extrapolated phase. DRSMs led to extrapolations with improved plausibility for which model choice may be based on clinical input. In the simulation study, the flexible and dynamic relative survival models performed similarly and provided highly variable extrapolations.LimitationsFurther experience with these models is required to identify settings when they are most useful, and they provide sufficiently accurate extrapolations.ConclusionsDynamic models provide a flexible and attractive method for extrapolating survival data and facilitate the use of clinical input for model choice. Flexible and dynamic relative survival models make few structural assumptions and can improve extrapolation plausibility, but further research is required into methods for reducing the variability in extrapolations.
| S-EPMC9459356 | biostudies-literature