Wallisch C, Bach P, Hafermann L, Klein N, Sauerbrei W, Steyerberg, E. W., Heinze G, Rauch G, on behalf of topic group 2 of the STRATOS initiative (2022): Review of guidance papers on regression modeling in statistical series of medical journals. PloS one, 17(1), e0262918. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0262918

Section: Series and articles recommended to read

Depending on the aim of the planned study, as well as the focus and knowledge level of the reader, different series and articles might be recommended. The series in Circulation comprised three papers about multiple linear and logistic regression [24–26], which provide basics and describe many essential aspects of univariable and multivariable regression modeling.

• Slinker BK, Glantz SA. Multiple linear regression—Accounting for multiple simultaneous determinants of a continuous dependent variable. Circulation. 2008; 117(13):1732–7. https://doi.org/10.1161/CIRCULATIONAHA.106.654376
• LaValley MP. Logistic regression. Circulation. 2008; 117(18):2395–9. https://doi.org/10.1161/CIRCULATIONAHA.106.682658
• Crawford SL. Correlation and regression. Circulation. 2006; 114(19):2083–8. https://doi.org/10.1161/CIRCULATIONAHA.105.586495

For more advanced researchers, we recommend the article of Nuñez et al. in Revista Española de Cardiologia [22], which gives a quick overview of aspects and existing methods including functional forms and variable selection.

• Nuñez E, Steyerberg EW, Nuñez J. Regression modeling strategies. Rev Esp Cardiol. 2011; 64 (6):501–7. https://doi.org/10.1016/j.rec.2011.01.017

The Nature Methods series published short articles focusing on few, specific aspects of regression modeling [34–42]. This series might be of interest if one likes to spend more time on learning about regression modeling.

• Altman N, Krzywinski M. Simple linear regression. Nat Methods. 2015; 12(11):999–1000. https://doi.org/10.1038/nmeth.3627 
• Altman N, Krzywinski M. Association, correlation and causation. Nat Methods. 2015; 12(10):899–900. https://doi.org/10.1038/nmeth.3587
• Altman N, Krzywinski M. Analyzing outliers: influential or nuisance? Nat Methods. 2016; 13(4):281–2. https://doi.org/10.1038/nmeth.3812
• Altman N, Krzywinski M. Regression diagnostics. Nature Methods. 2016; 13(5):385–6. https://doi.org/10.1038/nmeth.3854
• Krzywinski M, Altman N. Multiple linear regression. Nat Methods. 2015; 12(12):1103–4. https://doi.org/10.1038/nmeth.3665
• Krzywinski M, Altman N. Classification and regression trees. Nature Methods. 2017; 14(8):755–6. https://www.taylorfrancis.com/books/mono/10.1201/9781315139470/classification-regression-trees-leo-breiman
• Lever J, Krzywinski M, Altman N. Regularization. Nature Methods. 2016; 13(10):803–4. https://doi.org/10.1038/nmeth.4014
• Lever J, Krzywinski M, Altman N. Model selection and overfitting. Nature Methods. 2016; 13(9):703–4. https://doi.org/10.1038/nmeth.3968
• Lever J, Krzywinski M, Altman N. Logistic regression. Nature Methods. 2016; 13(7):541–2. https://doi.org/10.1038/nmeth.3904

If someone is especially interested in prediction models, we recommend a concise publication in the European Heart Journal [31], which provides details on model development and validation for predictive purposes.

• Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J. 2014; 35(29):1925. https://doi.org/10.1093/eurheartj/ehu207

For the same topic we can also recommend the paper by Grant et al. [21].

• Grant SW, Collins GS, Nashef SAM. Statistical Primer: developing and validating a risk prediction model. Eur J Cardio-Thorac. 2018; 54(2):203–8. https://doi.org/10.1093/ejcts/ezy180

We consider all series and articles recommended in this paragraph as suitable reading for medical researchers but this does not imply that we agree to all explanations, statements and aspects discussed.