Statistical Reliability of the Modified Areal Weighted by Control Zones Method to Spatially Downscale Individual Social Data

In this paper, we present a unified modeling framework to combine aggregated data from randomized controlled trials (RCTs) with individual participant data (IPD) from observational studies. Rather than simply pooling the available evidence into an overall treatment effect, adjusted for potential confounding, the intention of this work is to explore treatment effects in specific patient populations reflected by the IPD. In this way, by collecting IPD, we can potentially gain new insights from RCTs' results, which cannot be seen using only a meta-analysis of RCTs. We present a new Bayesian hierarchical meta-regression model, which combines submodels, representing different types of data into a coherent analysis. Predictors of baseline risk are estimated from the individual data. Simultaneously, a bivariate random effects distribution of baseline risk and treatment effects is estimated from the combined individual and aggregate data. Therefore, given a subgroup of interest, the estimated treatment effect can be calculated through its correlation with baseline risk. We highlight different types of model parameters: those that are the focus of inference (e.g., treatment effect in a subgroup of patients) and those that are used to adjust for biases introduced by data collection processes (e.g., internal or external validity). The model is applied to a case study where RCTs' results, investigating efficacy in the treatment of diabetic foot problems, are extrapolated to groups of patients treated in medical routine and who were enrolled in a prospective cohort study.

Individual pharmacokinetic par parameters quantify the pharmacokinetics of an individual, while population pharmacokinetic parameters quantify population mean kinetics, interindividual variability, and residual intraindividual variability plus measurement error. Individual pharmacokinetics are estimated by fitting individual data to a pharmacokinetic model. Population pharmacokinetic parameters are estimated either by fitting all individual's data together as though there was no individual kinetic differences (the naive pooled data approach), or by fitting each individual's data separately, and then combining the individual parameter estimates (the two-stage approach). A third approach, NONMEM, takes a middle course between these, and avoids shortcomings of each of them. A data set consisting of 124 steady-state phenytoin concentration-dosage pairs from 49 patients, obtained in the routine course of their therapy, was analyzed by each method. The resulting population parameter estimates differ considerably (population mean Km, for example, is estimated as 1.57, 5.36, and 4.44 micrograms/ml by the naive pooled data, two-stage, and NONMEN approaches, respectively). Simulations of the data were analyzed to investigate these differences. The simulations indicate that the pooled data approach fails to estimate variabilities and produces imprecise estimates of mean kinetics. The two-stage approach produces good estimates of mean kinetics, but biased and imprecise estimates of interindividual variability. NONMEN produces accurate and precise estimates of all parameters, and also reasonable confidence intervals for them. This performance is exactly what is expected from theoretical considerations and provides empirical support for the use of NONMEM when estimating population pharmacokinetics from routine type patient data.

Subgroup meta-analysis can be used for comparing treatment effects between subgroups using information from multiple trials. If the effect of treatment is differential depending on subgroup, the results could enable personalization of the treatment. We propose using linear mixed models for estimating treatment effect modification in aggregate data meta-analysis. The linear mixed models capture existing subgroup meta-analysis methods while allowing for additional features such as flexibility in modeling heterogeneity, handling studies with missing subgroups and more. Reviews and simulation studies of the best suited models for estimating possible differential effect of treatment depending on subgroups have been studied mostly within individual participant data meta-analysis. While individual participant data meta-analysis in general is recommended over aggregate data meta-analysis, conducting an aggregate data subgroup meta-analysis could be valuable for exploring treatment effect modifiers before committing to an individual participant data subgroup meta-analysis. Additionally, using solely individual participant data for subgroup meta-analysis requires collecting sufficient individual participant data which may not always be possible. In this article, we compared existing methods with linear mixed models for aggregate data subgroup meta-analysis under a broad selection of scenarios using simulation and two case studies. Both the case studies and simulation studies presented here demonstrate the advantages of the linear mixed model approach in aggregate data subgroup meta-analysis.

BackgroundNetwork meta-analysis methods, which are an extension of the standard pair-wise synthesis framework, allow for the simultaneous comparison of multiple interventions and consideration of the entire body of evidence in a single statistical model. There are well-established advantages to using individual patient data to perform network meta-analysis and methods for network meta-analysis of individual patient data have already been developed for dichotomous and time-to-event data. This paper describes appropriate methods for the network meta-analysis of individual patient data on continuous outcomes.MethodsThis paper introduces and describes network meta-analysis of individual patient data models for continuous outcomes using the analysis of covariance framework. Comparisons are made between this approach and change score and final score only approaches, which are frequently used and have been proposed in the methodological literature. A motivating example on the effectiveness of acupuncture for chronic pain is used to demonstrate the methods. Individual patient data on 28 randomised controlled trials were synthesised. Consistency of endpoints across the evidence base was obtained through standardisation and mapping exercises.ResultsIndividual patient data availability avoided the use of non-baseline-adjusted models, allowing instead for analysis of covariance models to be applied and thus improving the precision of treatment effect estimates while adjusting for baseline imbalance.ConclusionsThe network meta-analysis of individual patient data using the analysis of covariance approach is advocated to be the most appropriate modelling approach for network meta-analysis of continuous outcomes, particularly in the presence of baseline imbalance. Further methods developments are required to address the challenge of analysing aggregate level data in the presence of baseline imbalance.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0224-1) contains supplementary material, which is available to authorized users.

The world of education will never be separated from student data and graduation data. Student data is usually obtained at the time of registration of new students to an institution. Various individual data and parental data will be stored in the student's individual data archive. It is unfortunate if the data is not used and only as an archive. In this study, students will utilize individual data in the form of parental income data and data on student visits to the library. Which individual data will be associated with the student's cumulative achievement index to determine the validity of the student's graduation predicate. One method that can be used is the K-Nearest Neighbor method. The K-Nearest Neighbor method is a method for classifying objects based on the training data that is closest to the object. This study aims to analyze the validity of the student graduation predicate based on individual data such as parental income data and student library visit data using the K-Nearest Neighbor method. The case study was conducted by taking data from the Adisutjipto Aerospace Technology Institute.

This article attempts to analyze and find (1) the futuristic paradigm of Islamic religious education curriculum design (2) implementation of Islamic religious education curriculum design in character strengthening (3) the implications of Islamic religious education curriculum design on strengthening the character of students. This research implements a qualitative approach with a case study research type with a multi-case design model. The research was carried out at SD Plus Muhammadiyah Brawijaya, Mojokerto City and MI Ismailiyah New Paradigm, Mojokerto City. Data collection techniques apply in-depth interviews, observation of roles and documentation. Data analysis used multi-case analysis which was carried out in two stages, namely analysis of individual case data (individual case) and cross-case data analysis (cross case analysis). Checking data using credibility and confirmability. The results showed that: (1) Islamic religious education curriculum design based on the dynamics of life (adaptability centered design) was formulated as a response to the phenomenon of the transformation of the times and public interest (2) implementation of Islamic religious education curriculum design through textual and contextual learning models with a humanist approach. -religious and humanist-sufistic provide positive excesses in strengthening the character of students (3) the implications of Islamic religious education curriculum design on character strengthening are the formation of self-pity (behavioral attitude) with the intention of creating individuals who seek to get closer to Allah swt (taqarrub ilallah).

Intraspecific variability plays a key role in modeling population dynamics and ecological interactions. It is also a fundamental issue for agriculture, livestock production, and aquaculture. Dynamic Energy Budget (DEB) theory is a metabolic theory that can provide insights for these applications, as it can accurately predict the response of an organism to changes in environmental conditions. However, DEB model parameters are typically estimated for the species using mean trait values, neglecting individual differences. With advancing data collection technologies, there is a growing need to leverage individual data to calibrate DEB models. In this paper, we propose a multi-tier methodology for estimating individual-specific DEB parameters based on techniques and principles of the standard DEB estimation procedure. Individual data is often not sufficiently complete, therefore we compliment it available data on organisms of the same species. To avoid estimation bias, we employ a hierarchical tier structure. Data is categorized into tiers, depending on how general it is. In each tier, only a subset of the parameters is estimated depending on available data. The estimation proceeds in a top-down approach, starting with the most general tier and culminating in the individual tier. Values obtained in previously estimated tiers are used as pseudo-data to anchor the parameter estimation. In this way, we obtain biologically sensible individual parameter estimates in an efficient manner. As a case study, we apply our methodology to a dataset that contains weight and feed intake measurements for 52 individuals of the Mertolenga cattle breed from two performance trials. We conclude that variability in the surface-specific maximum assimilation rate and the digestion efficiency is needed to accurately model the individual data. Furthermore, the analysis of tier parameters and individual parameter distributions uncovers differences in the diets fed to the animals.

A maximum likelihood procedure for the analysis of group and individual data in aphasia research

Disease mapping with individual level information; a case study of acute myocardial infarction mortality.

Bayesian networks are powerful in data mining and analyzing causal relationships of an uncertain-reasoning problem. The implementation of Bayesian networks in industry and healthcare diagnosis can facilitate the process of locating causations in complex issues. This study conducted two case studies by BayesiaLab in consumer service and healthcare domain. Case Study One used unsupervised learning and supervised learning on the individual data set of county road traffic volume in Indiana State and concluded that road type has the most significant impact on daily vehicle miles traveled. In Case Study Two, only supervised learning was used to observe the aggregated data of adverse mental health effect on civilians, deployed veterans and nondeployed veterans of different genders. Both types of veterans showed higher probability to have adverse mental health compared to civilians. In conclusion, Bayesian networks provided valid results to support prior research. Further research is needed to investigate the differences between using individual data and aggregated data, and to apply Bayesian networks in meta-analysis.

The Acoustic Corer™ (AC) 3-D imaging technology comprises high- and low-frequency chirps and a parametric source with operating frequencies from 1.5kHz-15kHz through a stationary landed platform. Using multi-aspect acoustic imaging, it produces a 14m diameter volumetric image to a penetration depth of 60m below seabed, depending on the geological conditions. Recent multi-core AC surveys within the Baltic Sea and Gulf of Mexico proved that merging individual AC data sets into a unified volume greatly enhanced the data resolution and understanding of the sub-seabed stratigraphy and geohazards and obstructions present within. Specifically, this enabled a more cohesive representation and characterization of boulders, hard layers, and conductor pipes. The ability to merge data sets and maintain data quality suggests that optimal positioning, soil velocity and static corrections were obtained. These case studies, as applied to the renewables and oil and gas sectors, build on the value that the AC delivers for de-risking offshore infrastructure installations and remediation efforts.

According to SAS No. 56, Analytical Procedures, the use of disaggregate, individual location data can improve the effectiveness of analytical procedures used in multilocation audits. Using a case-study approach, we investigate whether improvements in the accuracy and precision of account balance expectations can be obtained by using disaggregate, individual location data in a large, multilocation company. Specifically, we examine two issues: (1) whether the summation of individual location expectations generates more accurate and precise expectations of company-wide account balances than expectations based on company-wide data only and (2) whether the accuracy and precision of analytical procedures is enhanced by including peer location observations of the account balance in individual location expectation models. We find that for the multilocation company examined in this case study the summation of individual location account balance expectations is not more accurate or precise than an expectation derived from aggregate models unless the individual location models include peer location observations of the account balance. When the individual location models include the same account observations from other peer locations within the company, the company-wide account balance expectations developed from disaggregate models are more accurate and precise (less variable) than expectations developed using aggregate, company-wide data only. The results from this case study indicate that when auditors are generating expectations of company-wide balances, disaggregate models incorporating peer location account observations provide account balance expectations that are both more accurate and more precise than company-wide, aggregate models. Given the limitations of a case-study approach, future research should be directed at establishing the generalizability of these findings.

Research background: Authors mentioned in the Research describe a financial analysis as first step of a financial planning process. It is an effective tool in the evaluation of a company’s financial efficiency on the basis of data from financial reports, where the individual data are sorted, aggregated and measured. The analysis usually consists liquidity, activity, profitability indicators and debt ratios. The result of the analysis should evaluate financial health and subsequently predict future of the focused company. Purpose of the article: The scope of this article is a practical application of financial analysis of the company. The main sources of information include financial statements balance sheet and income statement over the period of the last 3 years. Other sources are information obtained from employees of the company and information gained by studying a literature. Methods: The analysis process begins by identifying the main objectives and targets, continues by the methodical procedure, analysis of indicators and an interpretation of the analysis results. The main objective of the analysis is to evaluate the assets, the sources and the overall economic situation of the company. The key indicators that are the status and development of current and non-current assets, the status and development of equity and liabilities and a profit development. Findings & Value added: The financial analysis of a focused company was applied for the last 3-year time period. The worst status of the indicators occurred in the second year and after the subsequent intervention of the company management the result improved to sustainable status.

In this study, the aim was to develop and test an urban green space indicator for public health, as proposed by the World Health Organisation (WHO) Regional Office for Europe, in order to support health and environmental policies. We defined the indicator of green space accessibility as a proportion of an urban population living within a certain distance from a green space boundary. We developed a Geographic Information System (GIS)-based method and tested it in three case studies in Malmö, Sweden; Kaunas, Lithuania; and Utrecht, The Netherlands. Land use data in GIS from the Urban Atlas were combined with population data. Various population data formats, maximum distances to green spaces, minimum sizes of green spaces, and different definitions of green spaces were studied or discussed. Our results demonstrated that with increasing size of green space and decreased distance to green space, the indicator value decreased. As compared to Malmö and Utrecht, a relatively bigger proportion of the Kaunas population had access to large green spaces, at both shorter and longer distances. Our results also showed that applying the method of spatially aggregated population data was an acceptable alternative to using individual data. Based on reviewing the literature and the case studies, a 300 m maximum linear distance to the boundary of urban green spaces of a minimum size of 1 hectare are recommended as the default options for the indicator. The indicator can serve as a proxy measure for assessing public accessibility to urban green spaces, to provide comparable data across Europe and stimulate policy actions that recognise the importance of green spaces for sustainable public health.

Simple SummaryAs precision livestock technology continues to become viable for extensive rangeland systems. it is important to determine which technology has the potential to positively impact grazing management. Utilizing precision weighing systems on rangeland beef cattle provides new and novel insight into individual animal performance throughout grazing periods, which are directly linked to stocking rates (cattle/ha/time), to ensure adequate forage dry matter intake for cattle while avoiding negative environmental impacts. Refining stocking rate estimates using precision body weight measurements and precision system modeling is critical as this management decision is fundamental to rangeland management and livestock productivity across the United States.An essential component required for calculating stocking rates for livestock grazing extensive rangeland is dry matter intake (DMI). Animal unit months are used to simplify this calculation for rangeland systems to determine the rate of forage consumption and the cattle grazing duration. However, there is an opportunity to leverage precision technology deployed on rangeland systems to account for the individual animal variation of DMI and subsequent impacts on herd-level decisions regarding stocking rate. Therefore, the objectives of this study were, first, to build a precision system model (PSM) to predict total DMI (kg) and required pasture area (ha) using precision body weight (BW), and second, to evaluate differences in PSM-predicted stocking rates compared to the traditional herd-level method using initial or estimated mid-season BW. A deterministic model was constructed in both Vensim (version 10.1.2) and Program R (version 4.2.3) to incorporate individual precision BW data into a commonly used rangeland equation using %BW to estimate individual DMI, daily herd DMI, and area (ha) required to meet animal DMI requirements throughout specific grazing periods. Using the PSM, differences in outputs were evaluated using three scenarios: (1) initial BW (business as usual); (2) average mid-season BW; and (3) individual precision BW using data from two precision rangeland experiments conducted at the South Dakota State University Cottonwood Field Station. The data from the two experiments were used to develop PSM case studies. The trial data were collected using precision weight data (SmartScale™) collected from replacement heifers (Case study 1, n = 60) and steers (Case study 2, n = 254) grazing native rangeland. In Case study 1 (heifers), Scenario 1 versus Scenario 3 resulted in an additional 73.41 ha required. Results from Case study 2 indicated an average additional 4.4 ha required per pasture when comparing Scenario 3 versus Scenario 1. Sensitivity analyses resulted in a difference between maximum and minimum simulated values of 27,995 and 4265 kg forage consumed, and 122 and 8.9 pasture ha required for Case studies 1 and 2, respectively. Thus, results from the scenarios indicate an opportunity to identify both under- and over-stocking situations using precision DMI estimates, which helps to identify high-leverage precision tools that have practical applications for enhancing animal and plant productivity and environmental sustainability on extensive rangelands.