Dataset Information

A Simple-to-Use R Package for Mimicking Study Data by Simulations.

ABSTRACT:

Background

Data protection policies might prohibit the transfer of existing study data to interested research groups. To overcome legal restrictions, simulated data can be transferred that mimic the structure but are different from the existing study data.

Objectives

The aim of this work is to introduce the simple-to-use R package Mock Data Generation (modgo) that may be used for simulating data from existing study data for continuous, ordinal categorical, and dichotomous variables.

Methods

The core is to combine rank inverse normal transformation with the calculation of a correlation matrix for all variables. Data can then be simulated from a multivariate normal and transferred back to the original scale of the variables. Unique features of modgo are that it allows to change the correlation between variables, to perform perturbation analysis, to handle multicenter data, and to change inclusion/exclusion criteria by selecting specific values of one or a set of variables. Simulation studies on real data demonstrate the validity and flexibility of modgo.

Results

modgo mimicked the structure of the original study data. Results of modgo were similar with those from two other existing packages in standard simulation scenarios. modgo's flexibility was demonstrated on several expansions.

Conclusion

The R package modgo is useful when existing study data may not be shared. Its perturbation expansion permits to simulate truly anonymized subjects. The expansion to multicenter studies can be used for validating prediction models. Additional expansions can support the unraveling of associations even in large study data and can be useful in power calculations.

SUBMITTER: Koliopanos G

PROVIDER: S-EPMC10462429 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Simple-to-Use R Package for Mimicking Study Data by Simulations.

Koliopanos Giorgos G Ojeda Francisco F Ziegler Andreas A

Methods of information in medicine 20230307 3-04

<h4>Background</h4>Data protection policies might prohibit the transfer of existing study data to interested research groups. To overcome legal restrictions, simulated data can be transferred that mimic the structure but are different from the existing study data.<h4>Objectives</h4>The aim of this work is to introduce the simple-to-use R package Mock Data Generation (modgo) that may be used for simulating data from existing study data for continuous, ordinal categorical, and dichotomous variable ...[more]

PMID: 36882158

Similar Datasets

Project description:Computational tools provide a unique opportunity to study and design optimal materials by enhancing our ability to comprehend the connections between their atomistic structure and functional properties. However, designing materials with tailored functionalities is complicated due to the necessity to integrate various computational-chemistry software (not necessarily compatible with one another), the heterogeneous nature of the generated data, and the need to explore vast chemical and parameter spaces. The latter is especially important to avoid bias in scattered data points-based models and derive statistical trends only accessible by systematic datasets. Here, we introduce a robust high-throughput multi-scale computational infrastructure coined MISPR (Materials Informatics for Structure-Property Relationships) that seamlessly integrates classical molecular dynamics (MD) simulations with density functional theory (DFT). By enabling high-performance data analytics and coupling between different methods and scales, MISPR addresses critical challenges arising from the needs of automated workflow management and data provenance recording. The major features of MISPR include automated DFT and MD simulations, error handling, derivation of molecular and ensemble properties, and creation of output databases that organize results from individual calculations to enable reproducibility and transparency. In this work, we describe fully automated DFT workflows implemented in MISPR to compute various properties such as nuclear magnetic resonance chemical shift, binding energy, bond dissociation energy, and redox potential with support for multiple methods such as electron transfer and proton-coupled electron transfer reactions. The infrastructure also enables the characterization of large-scale ensemble properties by providing MD workflows that calculate a wide range of structural and dynamical properties in liquid solutions. MISPR employs the methodologies of materials informatics to facilitate understanding and prediction of phenomenological structure-property relationships, which are crucial to designing novel optimal materials for numerous scientific applications and engineering technologies.

Project description:Health Surveillance Assistants (HSAs) have been providing integrated community case management (iCCM) for sick children in Malawi since 2008. HSAs report monthly iCCM program data but, at the time of this study, little of it was being used for service improvement. Additionally, HSAs and facility health workers did not have the tools to compile and visualize the data they collected to make evidence-based program decisions. From 2012 to 2013, we worked with Ministry of Health staff and partners to develop and pilot a program in Dowa and Kasungu districts to improve data quality and use at the health worker level. We developed and distributed wall chart templates to display and visualize data, provided training to 426 HSAs and supervisors on data analysis using the templates, and engaged health workers in program improvement plans as part of a data quality and use (DQU) package. We assessed the package through baseline and endline surveys of the HSAs and facility and district staff in the study areas, focusing specifically on availability of reporting forms, completeness of the forms, and consistency of the data between different levels of the health system as measured through results verification ratio (RVR). We found evidence of significant improvements in reporting consistency for suspected pneumonia illness (from overreporting cases at baseline [RVR=0.82] to no reporting inconsistency at endline [RVR=1.0]; P=.02). Other non-significant improvements were measured for fever illness and gender of the patient. Use of the data-display wall charts was high; almost all HSAs and three-fourths of the health facilities had completed all months since January 2013. Some participants reported the wall charts helped them use data for program improvement, such as to inform community health education activities and to better track stock-outs. Since this study, the DQU package has been scaled up in Malawi and expanded to 2 other countries. Unfortunately, without the sustained support and supervision provided in this project, use of the tools in the Malawi scale-up is lower than during the pilot period. Nevertheless, this pilot project shows community and facility health workers can use data to improve programs at the local level given the opportunity to access and visualize the data along with supervision support.

Dataset Information

A Simple-to-Use R Package for Mimicking Study Data by Simulations.

Background

Objectives

Methods

Results

Conclusion

Publications

A Simple-to-Use R Package for Mimicking Study Data by Simulations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets