OpenEpi

Last updated
OpenEpi
Developer(s) AG Dean, KM Sullivan, MM Soe
Stable release
3.03 / September 22, 2014;9 years ago (2014-09-22)
Operating system Cross-platform
Platform HTML, JavaScript
Type Web application, Statistical analysis
License MIT License
Website www.openepi.com

OpenEpi is a free, web-based, open source, operating system-independent series of programs for use in epidemiology, biostatistics, public health, and medicine, providing a number of epidemiologic and statistical tools for summary data. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] OpenEpi was developed in JavaScript and HTML, and can be run in modern web browsers. The program can be run from the OpenEpi website or downloaded and run without a web connection. The source code and documentation is downloadable and freely available for use by other investigators. OpenEpi has been reviewed, both by media organizations and in research journals. [12] [13] [14] [15] [16]

Contents

The OpenEpi developers have had extensive experience in the development and testing of Epi Info, a program developed by the Centers for Disease Control and Prevention (CDC) and widely used around the world for data entry and analysis. OpenEpi was developed to perform analyses found in the DOS version of Epi Info modules StatCalc and EpiTable, to improve upon the types of analyses provided by these modules, and to provide a number of tools and calculations not currently available in Epi Info. It is the first step toward an entirely web-based set of epidemiologic software tools. OpenEpi can be thought of as an important companion to Epi Info and to other programs such as SAS, PSPP, SPSS, Stata, SYSTAT, Minitab, Epidata, and R (see the R programming language). Another functionally similar Windows-based program is Winpepi. See also list of statistical packages and comparison of statistical packages. Both OpenEpi and Epi Info were developed with the goal of providing tools for low and moderate resource areas of the world. The initial development of OpenEpi was supported by a grant from the Bill and Melinda Gates Foundation to Emory University. [17]

Types

The types of calculations currently performed by OpenEpi include:

For epidemiologists and other health researchers, OpenEpi performs a number of calculations based on tables not found in most epidemiologic and statistical packages. For example, for a single 2x2 table, in addition to the results presented in other programs, OpenEpi provides estimates for:

For stratified 2x2 tables with count data, OpenEpi provides:

Similar to Epi Info, in a stratified analysis, both crude and adjusted estimates are provided so that the assessment of confounding can be made. With rate data, OpenEpi provides adjusted rate ratio's and rate differences, and tests for interaction. Finally, with count data, OpenEpi also performs a test for trend, for both crude data and stratified data.

In addition to being used to analyze data by health researchers, OpenEpi has been used as a training tool for teaching epidemiology to students at: Emory University, University of Massachusetts, University of Michigan, University of Minnesota, Morehouse College, Columbia University, University of Wisconsin, San Jose State University, University of Medicine and Dentistry of New Jersey, University of Washington, and elsewhere. This includes campus-based and distance learning courses. Because OpenEpi is easy to use, requires no programming experience, and can be run on the internet, students can use the program and focus on the interpretation of results. Users can run the program in English, French, Spanish, Portuguese or Italian.

Comments and suggestions for improvements are welcomed and the developers respond to user queries. The developers encourage others to develop modules that could be added to OpenEpi and provide a developer's tool at the website. Planned future development include improvements to existing modules, development of new modules, translation into other languages, and add the ability to cut and paste data and/or read data files.

See also

Related Research Articles

Biostatistics is a branch of statistics that applies statistical methods to a wide range of topics in biology. It encompasses the design of biological experiments, the collection and analysis of data from those experiments and the interpretation of the results.

<span class="mw-page-title-main">Epidemiology</span> Study of health and disease within a population

Epidemiology is the study and analysis of the distribution, patterns and determinants of health and disease conditions in a defined population.

The science of epidemiology has matured significantly from the times of Hippocrates, Semmelweis and John Snow. The techniques for gathering and analyzing epidemiological data vary depending on the type of disease being monitored but each study will have overarching similarities.

An odds ratio (OR) is a statistic that quantifies the strength of the association between two events, A and B. The odds ratio is defined as the ratio of the odds of A in the presence of B and the odds of A in the absence of B, or equivalently, the ratio of the odds of B in the presence of A and the odds of B in the absence of A. Two events are independent if and only if the OR equals 1, i.e., the odds of one event are the same in either the presence or absence of the other event. If the OR is greater than 1, then A and B are associated (correlated) in the sense that, compared to the absence of B, the presence of B raises the odds of A, and symmetrically the presence of A raises the odds of B. Conversely, if the OR is less than 1, then A and B are negatively correlated, and the presence of one event reduces the odds of the other event.

A case–control study is a type of observational study in which two existing groups differing in outcome are identified and compared on the basis of some supposed causal attribute. Case–control studies are often used to identify factors that may contribute to a medical condition by comparing subjects who have the condition with patients who do not have the condition but are otherwise similar. They require fewer resources but provide less evidence for causal inference than a randomized controlled trial. A case–control study is often used to produce an odds ratio, which is an inferior measure of strength of association compared to relative risk, but new statistical methods make it possible to use a case-control study to estimate relative risk, risk differences, and other quantities.

In evidence-based medicine, likelihood ratios are used for assessing the value of performing a diagnostic test. They use the sensitivity and specificity of the test to determine whether a test result usefully changes the probability that a condition exists. The first description of the use of likelihood ratios for decision rules was made at a symposium on information theory in 1954. In medicine, likelihood ratios were introduced between 1975 and 1980.

<span class="mw-page-title-main">Relative risk</span> Measure of association used in epidemiology

The relative risk (RR) or risk ratio is the ratio of the probability of an outcome in an exposed group to the probability of an outcome in an unexposed group. Together with risk difference and odds ratio, relative risk measures the association between the exposure and the outcome.

<span class="mw-page-title-main">Epi Info</span> Statistical software from the CDC

Epi Info is statistical software for epidemiology developed by Centers for Disease Control and Prevention (CDC) in Atlanta, Georgia (US).

<span class="mw-page-title-main">Forest plot</span> Graphical display of scientific results

A forest plot, also known as a blobbogram, is a graphical display of estimated results from a number of scientific studies addressing the same question, along with the overall results. It was developed for use in medical research as a means of graphically representing a meta-analysis of the results of randomized controlled trials. In the last twenty years, similar meta-analytical techniques have been applied in observational studies and forest plots are often used in presenting the results of such studies also.

<span class="mw-page-title-main">EpiData</span>

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Epi Map is a module that displays geographic maps with data from Epi Info. Epi Map is built around the Esri MapObjects software. Epi Map displays shapefiles containing the geographic boundaries layered with data results from the Analysis module.

<span class="mw-page-title-main">CIETmap</span>

CIETmap will be open source software under development by the CIET group to help build the community voice into planning. Currently no downloads are made available. CIETmap integrates epidemiological analysis tools with raster and vector mapping capabilities geared towards decision-making for planners. It includes a Windows interface with the statistical programming language, R.

<span class="mw-page-title-main">Forensic epidemiology</span>

The discipline of forensic epidemiology (FE) is a hybrid of principles and practices common to both forensic medicine and epidemiology. FE is directed at filling the gap between clinical judgment and epidemiologic data for determinations of causality in civil lawsuits and criminal prosecution and defense.

Donna Spiegelman is a biostatistician and epidemiologist who works at the interface between the two fields as a methodologist, applying statistical solutions to address potential biases in epidemiologic studies.

<span class="mw-page-title-main">Jamovi</span> Graphical user interface for R programming language

Jamovi is a free and open-source computer program for data analysis and performing statistical tests. The core developers of Jamovi are Jonathon Love, Damian Dropmann, and Ravi Selker, who are developers for the JASP project. Jamovi is a fork of JASP

References

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  2. Sullivan K.; Abramson J. (July 2008). "Update on free epidemiologic software". The Epidemiology Monitor. 29 (7): 3–4.
  3. Sullivan, K. (Spring–Summer 2008). "Faculty Spotlight". The Epi Vanguard.
  4. Sullivan, KM (December 2007). "OpenEpi now available in English, French, Italian, and Spanish". The Epidemiology Monitor. 28 (14).
  5. Sullivan KM, Dean A, Soe MM (May 2007). "OpenEpi Version 2". The Epidemiology Monitor. 28 (5): 1, 9–10.
  6. Sullivan, KM (April 2007). "Overview Of Free Analytic Software for Epidemiologists". The Epidemiology Monitor. 28 (4).
  7. Sullivan, KM (Spring 2007). "OpenEpi - A web-based calculator". The Epi Vanguard.
  8. Sullivan K, Dean A, Soe MM (June 2006). "OpenEpi: an update". The Epidemiology Monitor. 27 (6): 4.
  9. Sullivan KM, Dean AG, Mir R (April 2004). "OpenEpi: A new collaborative effort in epidemiologic computing". The Epidemiology Monitor. 25 (4): 3, 7, 9.
  10. Sullivan KM, Dean AG, Mir R (11–14 June 2003). OpenEpi: a new collaborative effort in epidemiologic computing. 36th Annual Meeting of the Society for Epidemiologic Research. Atlanta, GA, United States.
  11. Dean AG, Sullivan KM, Mir R (1–4 October 2003). The Open Epi Initiative: Open Source Web Browser Software for Public Health. Annual Scientific Meeting of the International Epidemiological Association. Toledo, Spain: European Epidemiology Federation.
  12. "OpenEpi online". September 24, 2007.
  13. Great free tools for teaching statistics | Education IT | ZDNet.com
  14. Antoch, J. (2008). "Environment for statistical computing". Computer Science Review. 2 (2): 113–122. doi:10.1016/j.cosrev.2008.05.002.
  15. Abramson JH, Abramson ZH (2008). Research Methods in Community Medicine: Surveys, Epidemiologic Research, Programme Evaluation, Clinical Trials (6th ed.). John Wiley & Sons.
  16. Singh, S. (2009). "Review of epidata entry and analysis freewares". Indian Journal of Community Medicine. 34 (34): 76–7. doi: 10.4103/0970-0218.45384 . PMC   2763648 . PMID   19876466.
  17. "Archived copy" (PDF). Archived from the original (PDF) on 2009-09-20. Retrieved 2009-09-22.{{cite web}}: CS1 maint: archived copy as title (link)