Browse
Submit Data
Databases
API
Help

Dataset Information

5 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

The hybrid synthetic microdata platform: a method for statistical disclosure control.

ABSTRACT: Owners of biobanks are in an unfortunate position: on the one hand, they need to protect the privacy of their participants, whereas on the other, their usefulness relies on the disclosure of the data they hold. Existing methods for Statistical Disclosure Control attempt to find a balance between utility and confidentiality, but come at a cost for the analysts of the data. We outline an alternative perspective to the balance between confidentiality and utility. By combining the generation of synthetic data with the automated execution of data analyses, biobank owners can guarantee the privacy of their participants, yet allow the analysts to work in an unrestricted manner.

SUBMITTER: Kuiper J

PROVIDER: S-EPMC4559158 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

The hybrid synthetic microdata platform: a method for statistical disclosure control.

Kuiper Joël J van den Heuvel Edwin R ER Swertz Morris A MA

Biopreservation and biobanking 20150601 3

Owners of biobanks are in an unfortunate position: on the one hand, they need to protect the privacy of their participants, whereas on the other, their usefulness relies on the disclosure of the data they hold. Existing methods for Statistical Disclosure Control attempt to find a balance between utility and confidentiality, but come at a cost for the analysts of the data. We outline an alternative perspective to the balance between confidentiality and utility. By combining the generation of synt ...[more]

PMID: 26035007

Similar Datasets

Synthetic/ECM-inspired hybrid platform for hollow microcarriers with ROS-triggered nanoporation hallmarks.

Project description:Reactive oxygen species (ROS) are key pathological signals expressed in inflammatory diseases such as cancer, ischemic conditions and atherosclerosis. An ideal drug delivery system should not only be responsive to these signals but also should not elicit an unfavourable host response. This study presents an innovative platform for drug delivery where a natural/synthetic composite system composed of collagen type I and a synthesized polythioether, ensures a dual stimuli-responsive behaviour. Collagen type I is an extracellular matrix constituent protein, responsive to matrix metalloproteinases (MMP) cleavage per se. Polythioethers are stable synthetic polymers characterized by the presence of sulphur, which undergoes a ROS-responsive swelling switch. A polythioether was synthesised, functionalized and tested for cytotoxicity. Optimal conditions to fabricate a composite natural/synthetic hollow sphere construct were optimised by a template-based method. Collagen-polythioether hollow spheres were fabricated, revealing uniform size and ROS-triggered nanoporation features. Cellular metabolic activity of H9C2 cardiomyoblasts remained unaffected upon exposure to the spheres. Our natural/synthetic hollow microspheres exhibit the potential for use as a pathological stimuli-responsive reservoir system for applications in inflammatory diseases.

| S-EPMC5640652 | biostudies-literature

A robust statistical method for case-control association testing with copy number variation.

Project description:Copy number variation (CNV) is pervasive in the human genome and can play a causal role in genetic diseases. The functional impact of CNV cannot be fully captured through linkage disequilibrium with SNPs. These observations motivate the development of statistical methods for performing direct CNV association studies. We show through simulation that current tests for CNV association are prone to false-positive associations in the presence of differential errors between cases and controls, especially if quantitative CNV measurements are noisy. We present a statistical framework for performing case-control CNV association studies that applies likelihood ratio testing of quantitative CNV measurements in cases and controls. We show that our methods are robust to differential errors and noisy data and can achieve maximal theoretical power. We illustrate the power of these methods for testing for association with binary and quantitative traits, and have made this software available as the R package CNVtools.

| S-EPMC2784596 | biostudies-literature

GUIA: a digital platform to facilitate result disclosure in genetic counseling.

Project description:PurposeUse of genomic sequencing is increasing at a pace that requires technological solutions to effectively meet the needs of a growing patient population. We developed GUÍA, a web-based application, to enhance the delivery of genomic results and related clinical information to patients and families.MethodsGUÍA development occurred in five overlapping phases: formative research, content development, stakeholder/community member input, user interface design, and web application development. Development was informed by formative qualitative research involving parents (N = 22) whose children underwent genomic testing. Participants enrolled in the NYCKidSeq pilot study (N = 18) completed structured feedback interviews post-result disclosure using GUÍA. Genetic specialists, researchers, patients, and community stakeholders provided their perspectives on GUÍA's design to ensure technical, cultural, and literacy appropriateness.ResultsNYCKidSeq participants responded positively to the use of GUÍA to deliver their children's results. All participants (N = 10) with previous experience with genetic testing felt GUÍA improved result disclosure, and 17 (94%) participants said the content was clear.ConclusionGUÍA communicates complex genomic information in an understandable and personalized manner. Initial piloting demonstrated GUÍA's utility for families enrolled in the NYCKidSeq pilot study. Findings from the NYCKidSeq clinical trial will provide insight into GUÍA's effectiveness in communicating results among diverse, multilingual populations.

| S-EPMC8105171 | biostudies-literature

Examination of the Synthetic Control Method for Evaluating Health Policies with Multiple Treated Units.

Project description:This paper examines the synthetic control method in contrast to commonly used difference-in-differences (DiD) estimation, in the context of a re-evaluation of a pay-for-performance (P4P) initiative, the Advancing Quality scheme. The synthetic control method aims to estimate treatment effects by constructing a weighted combination of control units, which represents what the treated group would have experienced in the absence of receiving the treatment. While DiD estimation assumes that the effects of unobserved confounders are constant over time, the synthetic control method allows for these effects to change over time, by re-weighting the control group so that it has similar pre-intervention characteristics to the treated group. We extend the synthetic control approach to a setting of evaluation of a health policy where there are multiple treated units. We re-analyse a recent study evaluating the effects of a hospital P4P scheme on risk-adjusted hospital mortality. In contrast to the original DiD analysis, the synthetic control method reports that, for the incentivised conditions, the P4P scheme did not significantly reduce mortality and that there is a statistically significant increase in mortality for non-incentivised conditions. This result was robust to alternative specifications of the synthetic control method. © 2015 The Authors. Health Economics published by John Wiley & Sons Ltd.

| S-EPMC5111584 | biostudies-literature

Business Cycles and Divorce: Evidence from Microdata.

Project description:We use individual-level data to show that divorce is pro-cyclical on average, a finding robust to the inclusion of a wide range of controls. Pro-cyclical divorce is concentrated among women who married young and/or do not have a college degree.

| S-EPMC3638965 | biostudies-literature

Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation.

Project description:BackgroundThere has been growing interest in data synthesis for enabling the sharing of data for secondary analysis; however, there is a need for a comprehensive privacy risk model for fully synthetic data: If the generative models have been overfit, then it is possible to identify individuals from synthetic data and learn something new about them.ObjectiveThe purpose of this study is to develop and apply a methodology for evaluating the identity disclosure risks of fully synthetic data.MethodsA full risk model is presented, which evaluates both identity disclosure and the ability of an adversary to learn something new if there is a match between a synthetic record and a real person. We term this "meaningful identity disclosure risk." The model is applied on samples from the Washington State Hospital discharge database (2007) and the Canadian COVID-19 cases database. Both of these datasets were synthesized using a sequential decision tree process commonly used to synthesize health and social science data.ResultsThe meaningful identity disclosure risk for both of these synthesized samples was below the commonly used 0.09 risk threshold (0.0198 and 0.0086, respectively), and 4 times and 5 times lower than the risk values for the original datasets, respectively.ConclusionsWe have presented a comprehensive identity disclosure risk model for fully synthetic data. The results for this synthesis method on 2 datasets demonstrate that synthesis can reduce meaningful identity disclosure risks considerably. The risk model can be applied in the future to evaluate the privacy of fully synthetic data.

| S-EPMC7704280 | biostudies-literature

Impact of Regression to the Mean on the Synthetic Control Method: Bias and Sensitivity Analysis.

Project description:To make informed policy recommendations from observational panel data, researchers must consider the effects of confounding and temporal variability in outcome variables. Difference-in-difference methods allow for estimation of treatment effects under the parallel trends assumption. To justify this assumption, methods for matching based on covariates, outcome levels, and outcome trends-such as the synthetic control approach-have been proposed. While these tools can reduce bias and variability in some settings, we show that certain applications can introduce regression to the mean (RTM) bias into estimates of the treatment effect. Through simulations, we show RTM bias can lead to inflated type I error rates and bias toward the null in typical policy evaluation settings. We develop a novel correction for RTM bias that allows for valid inference and show how this correction can be used in a sensitivity analysis. We apply our proposed sensitivity analysis to reanalyze data concerning the effects of California's Proposition 99, a large-scale tobacco control program, on statewide smoking rates.

| S-EPMC7541515 | biostudies-literature

Dynamics of bed bug infestations and control under disclosure policies.

Project description:Bed bugs have reemerged in the United States and worldwide over recent decades, presenting a major challenge to both public health practitioners and housing authorities. A number of municipalities have proposed or initiated policies to stem the bed bug epidemic, but little guidance is available to evaluate them. One contentious policy is disclosure, whereby landlords are obligated to notify potential tenants of current or prior bed bug infestations. Aimed to protect tenants from leasing an infested rental unit, disclosure also creates a kind of quarantine, partially and temporarily removing infested units from the market. Here, we develop a mathematical model for the spread of bed bugs in a generalized rental market, calibrate it to parameters of bed bug dispersion and housing turnover, and use it to evaluate the costs and benefits of disclosure policies to landlords. We find disclosure to be an effective control policy to curb infestation prevalence. Over the short term (within 5 years), disclosure policies result in modest increases in cost to landlords, while over the long term, reductions of infestation prevalence lead, on average, to savings. These results are insensitive to different assumptions regarding the prevalence of infestation, rate of introduction of bed bugs from other municipalities, and the strength of the quarantine effect created by disclosure. Beyond its application to bed bugs, our model offers a framework to evaluate policies to curtail the spread of household pests and is appropriate for systems in which spillover effects result in highly nonlinear cost-benefit relationships.

| S-EPMC6442569 | biostudies-literature

Scalable Hybrid Synthetic/Biocatalytic Route to Psilocybin.

Project description:Psilocybin, the principal indole alkaloid of Psilocybe mushrooms, is currently undergoing clinical trials as a medication against treatment-resistant depression and major depressive disorder. The psilocybin supply for pharmaceutical purposes is met by synthetic chemistry. We replaced the problematic phosphorylation step during synthesis with the mushroom kinase PsiK. This enzyme was biochemically characterized and used to produce one gram of psilocybin from psilocin within 20 minutes. We also describe a pilot-scale protocol for recombinant PsiK that yielded 150?mg enzyme in active and soluble form. Our work consolidates the simplicity of tryptamine chemistry with the specificity and selectivity of enzymatic catalysis and helps provide access to an important drug at potentially reasonable cost.

| S-EPMC7383583 | biostudies-literature

Modeling the impact of exposure reductions using multi-stressor epidemiology, exposure models, and synthetic microdata: an application to birthweight in two environmental justice communities.

Project description:BackgroundMany vulnerable populations experience elevated exposures to environmental and social stressors, with deleterious effects on health. Multi-stressor epidemiological models can be used to assess benefits of exposure reductions. However, requisite individual-level risk factor data are often unavailable at adequate spatial resolution.ObjectiveTo leverage public data and novel simulation methods to estimate birthweight changes following simulated environmental interventions in two environmental justice communities in Massachusetts, USA.MethodsWe gathered risk factor data from public sources (US Census, Behavioral Risk Factor Surveillance System, and Massachusetts Department of Health). We then created synthetic individual-level data sets using combinatorial optimization, and probabilistic and logistic modeling. Finally, we used coefficients from a multi-stressor epidemiological model to estimate birthweight and birthweight improvement associated with simulated environmental interventions.ResultsWe created geographically resolved synthetic microdata. Mothers with the lowest predicted birthweight were those identifying as Black or Hispanic, with parity > 1, utilization of government prenatal support, and lower educational attainment. Birthweight improvements following greenness and temperature improvements were similar for all high-risk groups and were larger than benefits from smoking cessation.SignificanceAbsent private health data, this methodology allows for assessment of cumulative risk and health inequities, and comparison of individual-level impacts of localized health interventions.

| S-EPMC8141037 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data