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Data in the MMWR

Methods (2 of 12)

Data in the MMWR (§5.1)
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2025-12-09

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Data in the MMWR (§5.1) On this site (opens in a new tab)
Describe methods, including software

Principles: A report should give enough information about data-analytic methods—including purpose, name, and software implementation—that a knowledgeable reader can confidently trace workflow from data sources through analytic methods to formal results. Assumptions behind statistical methods should be provided in sufficient detail to justify, and if possible to replicate, the analysis. Consequences of assumptions should be acknowledged. Specify software used for analysis, including the main program(s) and important specialized packages, libraries, or modules.

Observations: In some cases, a report describes its analytic purpose without directly naming the method. Analytic software (e.g., SAS, R, Python, Stata, SUDAAN) is consistently reported since early 2019. In some cases, data management software (e.g., REDCap) and analytic environments (e.g., Jupyter Notebooks and RStudio) are also reported.

Recommendations: For each substantive method, a report should state an unambiguous name or its general purpose; if space permits, report both. Report software that is directly relevant to analytic methods: main analytic software and important procedures, packages, or libraries (e.g., GLIMMIX in SAS, randomForest in R, sklearn in Python). Reports need not name computing environments (e.g., Jupyter or RStudio) unless these substantively affect quality or analysis.

Next topic: Describe quantities, including outcomes (§5.2)

Data in the MMWR