Suchergebnisse

We aimed to develop a framework for generating synthetic identifier datasets to support development and evaluation of data linkage methods. We evaluated whether replicating associations between attributes and identifiers improved the utility of the synthetic data for assessing linkage error.
We determined the steps required to generate synthetic identifiers that replicate the properties of real-world data collection. We generated synthetic versions of a large United Kingdom cohort study (the Avon Longitudinal Study of Parents and Children), according to the quality and completeness of identifiers recorded over several waves of the cohort. We evaluated the utility of the synthetic identifier data in terms of assessing linkage quality (false matches and missed matches).
Comparing data from two collection points in ALSPAC, we found within-person disagreement in identifiers (differences in recording due to both natural change and non-valid entries) in 18% of surnames and 12% of forenames. Rates of disagreement varied by maternal age and ethnic group. Synthetic data provided accurate estimates of linkage quality metrics compared with the original data (within 0.13-0.55% for missed matches and 0.00-0.04% for false matches). Incorporating associations between identifier errors and maternal age/ethnicity improved synthetic data utility.
Replicating dependencies between attribute values (e.g. ethnicity), values of identifiers (e.g. name), identifier disagreements (e.g. missing values, errors or changes over time), and their patterns and distribution structure enables generation of realistic synthetic data that can be used for robust evaluation of linkage methods.
Our framework provides a novel and generalisable mechanism for developing and benchmarking record linkage algorithms.

IntroductionCareful development and evaluation of data linkage methods is limited by researcher access to personal identifiers. One solution is to generate synthetic identifiers, which do not pose equivalent privacy concerns, but can form a 'gold-standard' linkage algorithm training dataset. Such data could help inform choices about appropriate linkage strategies in different settings.
ObjectivesWe aimed to develop and demonstrate a framework for generating synthetic identifier datasets to support development and evaluation of data linkage methods. We evaluated whether replicating associations between attributes and identifiers improved the utility of the synthetic data for assessing linkage error.
MethodsWe determined the steps required to generate synthetic identifiers that replicate the properties of real-world data collection. We then generated synthetic versions of a large UK cohort study (the Avon Longitudinal Study of Parents and Children; ALSPAC), according to the quality and completeness of identifiers recorded over several waves of the cohort. We evaluated the utility of the synthetic identifier data in terms of assessing linkage quality (false matches and missed matches).
ResultsComparing data from two collection points in ALSPAC, we found within-person disagreement in identifiers (differences in recording due to both natural change and non-valid entries) in 18% of surnames and 12% of forenames. Rates of disagreement varied by maternal age and ethnic group. Synthetic data provided accurate estimates of linkage quality metrics compared with the original data (within 0.13-0.55% for missed matches and 0.00-0.04% for false matches). Incorporating associations between identifier errors and maternal age/ethnicity improved synthetic data utility.
ConclusionsWe show that replicating dependencies between attribute values (e.g. ethnicity), values of identifiers (e.g. name), identifier disagreements (e.g. missing values, errors or changes over time), and their patterns and distribution structure enables generation of realistic synthetic data that can be used for robust evaluation of linkage methods.

The views expressed in this report are those of the author and do not reflect the official policy or position of the Department of Defense or the United States Government. ; This paper discusses a technique for modulating and demodulating M-ary FSK using an FFT based modem typical of Coded Orthogonal Frequency Division Modulation (COFDM) systems. COFDM is one of the more promising spectrally efficient, high data rate modulation techniques for line-of-sight communications between mobile platforms. This paper will show that legacy FSK radios like the AN/MRC-142 (binary FSKmodulation at 144, 288, 576 kbps) used by the U.S. Marine Corps could be easily implemented in a COFDM modem orginally designed for higher data rates and better spectral efficiency than the legacy radio. In addition, the performance of such an implementation is analyzed in detail and shown to result in negligible performance degradation (0.05 dB or less). Digital processing speed requirements are analyzed and shown to be similar to digital implementation of conventional FSK receivers. MATLAB code is included that simulates the modem. ; This report was sponsored by NCCOSC RDTE Division. ; Approved for public release; distribution is unlimited.

Market research focus groups generate three types of data: first, representatives of commissioning companies or organizations watch the group from behind a one-way mirror; second, they receive a video of the group discussion; third, they are given a report of the focus group. This article analyses how the required data are interactionally produced to be visible for the people behind the one-way screen, for the video and for the report. It describes the phenomenon of repeat receipts as a central device for producing visible data. Repeat receipts are sequences where the moderator repeats participants'contributions, typically with intonational cues that mark completion. Repeat receipts have several functions. They can (a) highlight central market-research relevant terms from participants'responses; (b) strip off rhetorical relations by repeating utterances in a decontextualized manner; (c) summarize contributions in repeating contributions of different authors as if of one voice; (d) cover conflict in repeating potentially contradictory contributions as discrete statements; (e) socialize responding by providing templates for the required contributions. Repeat receipts help shape the focus group interaction to generate visible data for the overhearing audience, the video and the report. The article ends with a comparison of repeats in market research focus groups, standardized surveys and news interviews.

Abstract
Searching for stock picking strategies can be modelled as a multiobjective optimization problem. The objectives are mostly the profit and risk. Because of the conflicting nature of these objectives, we have to find Pareto optimal solutions. Multiobjective genetic programming (MOGP) can be used to find tree based solutions, using evolutionary operators. The advantage is that this algorithm can combine any number of inputs and generate complex models. Recent research shows, that the popularity of different terms on the internet can be used to enhance the models. This paper deals with a SPEA2 MOGP implementation, which uses Google trends and Wikipedia popularity to find stock investment strategies.

IntroductionAdministrative health records (AHRs) are used to conduct population-based post-market drug safety and comparative effectiveness studies to inform healthcare decision making. However, the cost of data extraction, and the challenges associated with privacy and securing approvals can make it challenging for researchers to conduct methodological research in a timely manner using real data. Generating synthetic AHRs that reasonably represent the real-world data are beneficial for developing analytic methods and training analysts to rapidly implement study protocols. We generated synthetic AHRs using two methods and compared these synthetic AHRs to real-world AHRs. We described the challenges associated with using synthetic AHRs for real-world study.
MethodsThe real-world AHRs comprised prescription drug records for individuals with healthcare insurance coverage in the Population Research Data Repository (PRDR) from Manitoba, Canada for the 10-year period from 2008 to 2017. Synthetic data were generated using the Observational Medical Dataset Simulator II (OSIM2) and a modification (ModOSIM). Synthetic and real-world data were described using frequencies and percentages. Agreement of prescription drug use measures in PRDR, OSIM2 and ModOSIM was estimated with the concordance coefficient.
ResultsThe PRDR cohort included 169,586,633 drug records and 1,395 drug types for 1,604,734 individuals. Synthetic data for 1,000,000 individuals were generated using OSIM2 and ModOSIM. Sex and age group distributions were similar in the real-world and synthetic AHRs. However, there were significant differences in the number of drug records and number of unique drugs per person for OSIM2 and ModOSIM when compared with PRDR. For the average number of days of drug use, concordance with the PRDR was 16% (95% confidence interval [CI]: 12%-19%) for OSIM2 and 88% (95% CI: 87%-90%) for ModOSIM.
ConclusionsModOSIM data were more similar to PRDR than OSIM2 data on many measures. Synthetic AHRs consistent with those found in real-world settings can be generated using ModOSIM. Synthetic data will benefit rapid implementation of methodological studies and data analyst training.

AbstractModels for converting expert-coded data to estimates of latent concepts assume different data-generating processes (DGPs). In this paper, we simulate ecologically valid data according to different assumptions, and examine the degree to which common methods for aggregating expert-coded data (1) recover true values and (2) construct appropriate coverage intervals. We find that the mean and both hierarchical Aldrich–McKelvey (A–M) scaling and hierarchical item-response theory (IRT) models perform similarly when expert error is low; the hierarchical latent variable models (A-M and IRT) outperform the mean when expert error is high. Hierarchical A–M and IRT models generally perform similarly, although IRT models are often more likely to include true values within their coverage intervals. The median and non-hierarchical latent variable models perform poorly under most assumed DGPs.

Purpose: Few studies have applied count data analysis to tourist accommodation data. This study was undertaken to investigate the characteristics and to seek for the most fitting models for population total estimation in relation to tourist accommodation data.
Methods: Based on the data of 10,503 hotels, obtained from by a nationwide Japanese survey, the bootstrap resampling method was applied for re-randomisation of the data. Training and test sets were derived by randomly splitting each of the bootstrap samples. Six count models were fitted to the training set and validated with the test set. Bootstrap distributions for parameters of significance were used for model evaluation.
Results: The outcome variable (number of guests), was found to be heterogenous, over dispersed and long-tailed, with excessive zero counts. The hurdle negative binomial and zero-inflated negative binomial models outperformed the other models. The accuracy (se) of the estimation of total guests with training sets that ranged from 5% to 85%, was from 3.7 to 0.4 respectively. Results appear little overestimated.
Implications: Findings indicated that the integration of the bootstrap resampling method and count regression provide a statistical tool for generating reliable tourist accommodation statistics. The use of bootstrap would help to detect and correct the bias of the estimation.

Background: Automated data processing is creating and implementing technology that automatically processes data. This computer tool is recommended for doctors because it supports their everyday work, assists in medical diagnosis, and enhances patient care. The aim of this paper is to propose an informatic tool that can extract the values of some parameters of interest from blood test sheets in order to get medical alerts and to monitor the chronic disease. Methods: An application, written in Python, was developed Results: The computer tool extracted automatically the values of glucose, triglycerides, HDL-cholesterol, total cholesterol, and LDL-cholesterol from medical sheets (text-based file or graphic file, respectively), and saved them in a database, accessed and represented in a graphic form the most recent values of these parameters; alerts according to metabolic syndrome criteria and Framingham risk score were generated. Conclusions: This tool contributes to the management of the medical process, saving precious time and helping the doctor in detecting current or future health problems.