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Data on medical technology: A new set of variables.

ABSTRACT: This article provides and describes a database containing three different variables for medical technology. To capture the various dimensions of medical technology, the set of variables covers not only drugs and devices but also general advances in medical knowledge. Information was extracted and processed from the Drug Canada Product Database, and the National Institute of Health. Variables are extracted from 1997 to 2017 and they represent global proxies. The provided data is relevant for healthcare research in various fields of study.

SUBMITTER: Rodriguez Llorian E

PROVIDER: S-EPMC7182663 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Data on medical technology: A new set of variables.

Rodriguez Llorian Elisabet E Mann Janelle J

Data in brief 20200411

This article provides and describes a database containing three different variables for medical technology. To capture the various dimensions of medical technology, the set of variables covers not only drugs and devices but also general advances in medical knowledge. Information was extracted and processed from the Drug Canada Product Database, and the National Institute of Health. Variables are extracted from 1997 to 2017 and they represent global proxies. The provided data is relevant for heal ...[more]

PMID: 32346569

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Project description:BACKGROUND: Independence between observations is a standard prerequisite of traditional statistical tests of association. This condition is, however, violated when autocorrelation is present within the data. In the case of variables that are regularly sampled in space (i.e. lattice data or images), such as those provided by remote-sensing or geographical databases, this problem is particularly acute. Because analytic derivation of the null probability distribution of the test statistic (e.g. Pearson's r) is not always possible when autocorrelation is present, we propose instead the use of a Monte Carlo simulation with surrogate data. METHODOLOGY/PRINCIPAL FINDINGS: The null hypothesis that two observed mapped variables are the result of independent pattern generating processes is tested here by generating sets of random image data while preserving the autocorrelation function of the original images. Surrogates are generated by matching the dual-tree complex wavelet spectra (and hence the autocorrelation functions) of white noise images with the spectra of the original images. The generated images can then be used to build the probability distribution function of any statistic of association under the null hypothesis. We demonstrate the validity of a statistical test of association based on these surrogates with both actual and synthetic data and compare it with a corrected parametric test and three existing methods that generate surrogates (randomization, random rotations and shifts, and iterative amplitude adjusted Fourier transform). Type I error control was excellent, even with strong and long-range autocorrelation, which is not the case for alternative methods. CONCLUSIONS/SIGNIFICANCE: The wavelet-based surrogates are particularly appropriate in cases where autocorrelation appears at all scales or is direction-dependent (anisotropy). We explore the potential of the method for association tests involving a lattice of binary data and discuss its potential for validation of species distribution models. An implementation of the method in Java for the generation of wavelet-based surrogates is available online as supporting material.

Dataset Information

Data on medical technology: A new set of variables.

Publications

Data on medical technology: A new set of variables.

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