Statistical data presentation: a primer for rheumatology researchers
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Rheumatology International https://doi.org/10.1007/s00296-020-04740-z
INTERNATIONAL
REVIEW
Statistical data presentation: a primer for rheumatology researchers Durga Prasanna Misra1 · Olena Zimba2 · Armen Yuri Gasparyan3 Received: 10 October 2020 / Accepted: 24 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Statistical presentation of data is key to understanding patterns and drawing inferences about biomedical phenomena. In this article, we provide an overview of basic statistical considerations for data analysis. Assessment of whether tested parameters are distributed normally is important to decide whether to employ parametric or non-parametric data analyses. The nature of variables (continuous or discrete) also determines analysis strategies. Normally distributed data can be presented using means with standard deviations (SD), whereas non-parametric measures such as medians (with range or interquartile range) should be used for non-normal distributions. While the SD provides a measure of data dispersion, the standard error provides estimates of the 95% confidence interval i.e. the actual mean in the population. Univariable analyses should be directed to denote effect sizes, as well as test a priori hypothesis (i.e. null hypothesis significance testing). Univariable analyses should be followed up by suitable adjusted multivariable analyses such as linear or logistic regression. Linear correlation statistics can help assess whether two variables change hand in hand. Concordance rather than correlation should be used to compare outcome measures of disease states. Prior sample size calculation to ensure adequate study power is recommended for studies which have analogues in the literature with SDs. Statistical considerations for systematic reviews should include appropriate use of meta-analysis, assessment of heterogeneity, publication bias assessment when there are more than ten studies, and quality assessment of studies. Since statistical errors are responsible for a significant proportion of retractions, appropriate statistical analysis is mandatory during study planning and data analysis. Keywords Statistics as topic · Statistical data interpretation · Regression analysis
The importance of statistics Research studies attempt to describe and interpret natural phenomena. Numbers enable enumeration and scientific discourse. Biomedical phenomena are particularly challenging in this regard, because the quantitation or mathematical representation of biological processes sometimes require mathematical conversion of abstract data [1]. A case in point is
* Durga Prasanna Misra [email protected] 1
Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, India
2
Department of Internal Medicine No. 2, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine
3
Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University
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