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Statistical data presentation: a primer for rheumatology researchers

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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.

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References

  1. Ali Z, Bhaskar SB (2016) Basic statistical tools in research and data analysis. Indian J Anaesth 60:662–669. https://doi.org/10.4103/0019-5049.190623

    Article  PubMed  PubMed Central  Google Scholar 

  2. Haefeli M, Elfering A (2006) Pain assessment. Eur Spine J 15(Suppl 1):S17–S24. https://doi.org/10.1007/s00586-005-1044-x

    Article  PubMed  Google Scholar 

  3. Thiese MS (2014) Observational and interventional study design types; an overview. Biochem Med 24:199–210. https://doi.org/10.11613/BM.2014.022

    Article  Google Scholar 

  4. Benlidayi IC (2019) Implement statistics at each step of your research. Rheumatol Int 39:1303–1304. https://doi.org/10.1007/s00296-019-04327-3

    Article  Google Scholar 

  5. Misra DP, Agarwal V (2020) Integrity of clinical research conduct, reporting, publishing, and post-publication promotion in rheumatology. Clin Rheumatol 39:1049–1060. https://doi.org/10.1007/s10067-020-04965-0

    Article  PubMed  Google Scholar 

  6. McCue C (2007) 5 - Data. In: McCue C (ed) Data Mining and Predictive Analysis. Butterworth-Heinemann, Burlington, pp 67–92

    Chapter  Google Scholar 

  7. Habibzadeh F, Habibzadeh P (2015) How much precision in reporting statistics is enough? Croat Med J 56:490–492. https://doi.org/10.3325/cmj.2015.56.490

    Article  PubMed  PubMed Central  Google Scholar 

  8. Altman DG, Bland JM (1995) Statistics notes: the normal distribution. BMJ 310:298–298. https://doi.org/10.1136/bmj.310.6975.298

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Manikandan S (2011) Measures of central tendency: median and mode. J Pharmacol Pharmacother 2:214–215. https://doi.org/10.4103/0976-500X.83300

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ghasemi A, Zahediasl S (2012) Normality tests for statistical analysis: a guide for non-statisticians. Int J Endocrinol Metab 10:486–489. https://doi.org/10.5812/ijem.3505

    Article  PubMed  PubMed Central  Google Scholar 

  11. Habibzadeh F (2017) Statistical data editing in scientific articles. J Korean Med Sci 32:1072–1076

    Article  Google Scholar 

  12. Manikandan S (2010) Data transformation. J Pharmacol Pharmacother 1:126–127. https://doi.org/10.4103/0976-500X.72373

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Habibzadeh F (2013) Common statistical mistakes in manuscripts submitted to biomedical journals. Eur Sci Editing 39:92–94

    Google Scholar 

  14. Wan X, Wang W, Liu J, Tong T (2014) Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol 14:135. https://doi.org/10.1186/1471-2288-14-135

    Article  PubMed  PubMed Central  Google Scholar 

  15. Banerjee A, Chaudhury S (2010) Statistics without tears: populations and samples. Ind Psychiatry J 19:60–65. https://doi.org/10.4103/0972-6748.77642

    Article  PubMed  PubMed Central  Google Scholar 

  16. Zhao L, Tian L, Cai T, Claggett B, Wei LJ (2013) Effectively selecting a target population for a future comparative study. J Am Stat Assoc 108:527–539. https://doi.org/10.1080/01621459.2013.770705

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Altman DG, Bland JM (2005) Standard deviations and standard errors. BMJ 331:903–903. https://doi.org/10.1136/bmj.331.7521.903

    Article  PubMed  PubMed Central  Google Scholar 

  18. Altman DG (2005) Why we need confidence intervals. World J Surg 29:554–556. https://doi.org/10.1007/s00268-005-7911-0

    Article  PubMed  Google Scholar 

  19. du Prel J-B, Hommel G, Röhrig B, Blettner M (2009) Confidence interval or p-value?: part 4 of a series on evaluation of scientific publications. Deutsches Arzteblatt Int 106:335–339. https://doi.org/10.3238/arztebl.2009.0335

    Article  Google Scholar 

  20. de Graaf MA, Jager KJ, Zoccali C, Dekker FW (2011) Matching, an appealing method to avoid confounding? Nephron Clin Pract 118:c315–c318. https://doi.org/10.1159/000323136

    Article  PubMed  Google Scholar 

  21. Pearce N (2016) Analysis of matched case-control studies. BMJ 352:i969. https://doi.org/10.1136/bmj.i969

    Article  PubMed  PubMed Central  Google Scholar 

  22. Pernet C (2015) Null hypothesis significance testing: a short tutorial. F1000Research 4:621–621. https://doi.org/10.12688/f1000research.6963.3

    Article  PubMed  Google Scholar 

  23. Banerjee A, Chitnis UB, Jadhav SL, Bhawalkar JS, Chaudhury S (2009) Hypothesis testing, type I and type II errors. Ind Psychiatry J 18:127–131. https://doi.org/10.4103/0972-6748.62274

    Article  PubMed  PubMed Central  Google Scholar 

  24. Misra DP, Wakhlu A, Agarwal V, Sharma A, Negi VS (2017) Appropriate statistical analysis and research reporting. J Korean Med Sci 32:1379–1380. https://doi.org/10.3346/jkms.2017.32.8.1379

    Article  PubMed  PubMed Central  Google Scholar 

  25. Ioannidis JPA (2019) What have we (not) learnt from millions of scientific papers with p values? Am Stat 73:20–25. https://doi.org/10.1080/00031305.2018.1447512

    Article  Google Scholar 

  26. Ahmed S, Dhooria A (2020) Pitfalls in statistical analysis—a reviewers’ perspective. Indian J Rheumatol 15:39–45. https://doi.org/10.4103/injr.injr_32_20

    Article  Google Scholar 

  27. Tsiamalou P, Brotis A (2020) Biostatistics as a tool for medical research: what are we doing wrong? Mediterr J Rheumatol 30:196–200. https://doi.org/10.31138/mjr.30.4.196

    Article  PubMed  Google Scholar 

  28. ICMJE recommendations http://www.icmje.org/icmje-recommendations.pdf [Updated December 2019; Accessed on 09 January 2020]

  29. Calculation of required sample size. In: Kirkwood BR, Sterne JAC (eds) Essential Medical Statistics Blackwell Science Ltd.; 2003:413–428

  30. Charan J, Biswas T (2013) How to calculate sample size for different study designs in medical research? Indian J Psychol Med 35:121–126. https://doi.org/10.4103/0253-7176.116232

    Article  PubMed  PubMed Central  Google Scholar 

  31. Moher D, Dulberg CS, Wells GA (1994) Statistical power, sample size, and their reporting in randomized controlled trials. JAMA 272:122–124

    Article  CAS  Google Scholar 

  32. Dumas-Mallet E, Button KS, Boraud T, Gonon F, Munafò MR (2017) Low statistical power in biomedical science: a review of three human research domains. R Soc Open Sci 4:160254–160254. https://doi.org/10.1098/rsos.160254

    Article  PubMed  PubMed Central  Google Scholar 

  33. Nayak BK (2010) Understanding the relevance of sample size calculation. Indian J Ophthalmol 58:469–470. https://doi.org/10.4103/0301-4738.71673

    Article  PubMed  PubMed Central  Google Scholar 

  34. Schmidt B, Gemeinholzer B, Treloar A (2016) Open data in global environmental research: the Belmont forum’s open data survey. PLoS ONE 11:e0146695–e0146695. https://doi.org/10.1371/journal.pone.0146695

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Sullivan GM, Feinn R (2012) Using effect size-or why the p value is not enough. J Grad Med Educ 4:279–282. https://doi.org/10.4300/JGME-D-12-00156.1

    Article  PubMed  PubMed Central  Google Scholar 

  36. Ialongo C (2016) Understanding the effect size and its measures. Biochem Med (Zagreb) 26:150–163. https://doi.org/10.11613/bm.2016.015

    Article  Google Scholar 

  37. Bewick V, Cheek L, Ball J (2004) Statistics review 11: assessing risk. Crit Care 8:287–291. https://doi.org/10.1186/cc2908

    Article  PubMed  PubMed Central  Google Scholar 

  38. Kitchen CMR (2009) Nonparametric vs parametric tests of location in biomedical research. Am J Ophthalmol 147:571–572. https://doi.org/10.1016/j.ajo.2008.06.031

    Article  PubMed  PubMed Central  Google Scholar 

  39. Benlidayi IC (2019) Statistical accuracy in rheumatology research. Mediterr J Rheumatol 30:207–215. https://doi.org/10.31138/mjr.30.4.207

    Article  PubMed  PubMed Central  Google Scholar 

  40. Bland JM, Altman DG (2000) Statistics notes. The odds ratio. BMJ 320:1468–1468. https://doi.org/10.1136/bmj.320.7247.1468

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Armstrong RA (2014) When to use the Bonferroni correction. Ophthalmic Physiol Opt 34:502–508. https://doi.org/10.1111/opo.12131

    Article  PubMed  Google Scholar 

  42. Panagiotou OA, Ioannidis JPA, for the Genome-Wide Significance P (2011) What should the genome-wide significance threshold be? Empirical replication of borderline genetic associations. Int J Epidemiol 41:273–286. https://doi.org/10.1093/ije/dyr178

    Article  PubMed  Google Scholar 

  43. Kim H-Y (2017) Statistical notes for clinical researchers: chi squared test and Fisher’s exact test. Restor Dent Endod 42:152–155. https://doi.org/10.5395/rde.2017.42.2.152

    Article  PubMed  PubMed Central  Google Scholar 

  44. Bland JM, Altman DG (1994) Correlation, regression, and repeated data. BMJ 308:896. https://doi.org/10.1136/bmj.308.6933.896

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Mukaka MM (2012) Statistics corner: a guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24:69–71

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Schober P, Boer C, Schwarte LA (2018) Correlation coefficients: appropriate use and interpretation. Anesth Analg 126:1763–1768. https://doi.org/10.1213/ane.0000000000002864

    Article  PubMed  Google Scholar 

  47. Kwiecien R, Kopp-Schneider A, Blettner M (2011) Concordance analysis: part 16 of a series on evaluation of scientific publications. Deutsches Arzteblatt Int 108:515–521. https://doi.org/10.3238/arztebl.2011.0515

    Article  Google Scholar 

  48. Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1:307–310

    Article  CAS  Google Scholar 

  49. Bewick V, Cheek L, Ball J (2003) Statistics review 7: correlation and regression. Crit Care 7:451–459. https://doi.org/10.1186/cc2401

    Article  PubMed  PubMed Central  Google Scholar 

  50. Sperandei S (2014) Understanding logistic regression analysis. Biochem Med 24:12–18. https://doi.org/10.11613/BM.2014.003

    Article  Google Scholar 

  51. Bender R (2009) Introduction to the use of regression models in epidemiology. Methods Mol Biol 471:179–195. https://doi.org/10.1007/978-1-59745-416-2_9

    Article  PubMed  Google Scholar 

  52. Alexopoulos EC (2010) Introduction to multivariate regression analysis. Hippokratia 14:23–28

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Schneider A, Hommel G, Blettner M (2010) Linear regression analysis: part 14 of a series on evaluation of scientific publications. Deutsches Arzteblatt Int 107:776–782. https://doi.org/10.3238/arztebl.2010.0776

    Article  Google Scholar 

  54. Kim JH (2019) Multicollinearity and misleading statistical results. Korean J Anesthesiol 72:558–569. https://doi.org/10.4097/kja.19087

    Article  PubMed  PubMed Central  Google Scholar 

  55. Tu YK, Clerehugh V, Gilthorpe MS (2004) Collinearity in linear regression is a serious problem in oral health research. Eur J Oral Sci 112:389–397. https://doi.org/10.1111/j.1600-0722.2004.00160.x

    Article  PubMed  Google Scholar 

  56. Gasparyan AY, Ayvazyan L, Mukanova U, Yessirkepov M, Kitas GD (2019) The platelet-to-lymphocyte ratio as an inflammatory marker in rheumatic diseases. Ann Lab Med 39:345–357. https://doi.org/10.3343/alm.2019.39.4.345

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Ekelund S (2012) ROC curves—what are they and how are they used? Point of Care 11:16–21

    Article  Google Scholar 

  58. Cook CE (2008) Clinimetrics corner: the minimal clinically important change score (mcid): a necessary pretense. J Man Manip Ther 16:E82–E83. https://doi.org/10.1179/jmt.2008.16.4.82E

    Article  PubMed  PubMed Central  Google Scholar 

  59. Wells GA, Tugwell P, Kraag GR, Baker PR, Groh J, Redelmeier DA (1993) Minimum important difference between patients with rheumatoid arthritis: the patient’s perspective. J Rheumatol 20:557–560

    CAS  PubMed  Google Scholar 

  60. Ranganathan P, Pramesh CS, Buyse M (2015) Common pitfalls in statistical analysis: clinical versus statistical significance. Perspect Clin Res 6:169–170. https://doi.org/10.4103/2229-3485.159943

    Article  PubMed  PubMed Central  Google Scholar 

  61. Head ML, Holman L, Lanfear R, Kahn AT, Jennions MD (2015) The extent and consequences of p-hacking in science. PLoS Biol 13:e1002106. https://doi.org/10.1371/journal.pbio.1002106

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Bruns SB, Ioannidis JPA (2016) p-curve and p-hacking in observational research. PLoS ONE 11:e0149144–e0149144. https://doi.org/10.1371/journal.pone.0149144

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Hill AB (2015) The environment and disease: association or causation? 1965. J R Soc Med 108:32–37. https://doi.org/10.1177/0141076814562718

    Article  PubMed  PubMed Central  Google Scholar 

  64. Parascandola M, Weed DL (2001) Causation in epidemiology. J Epidemiol Community Health 55:905–912. https://doi.org/10.1136/jech.55.12.905

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Hong EP, Park JW (2012) Sample size and statistical power calculation in genetic association studies. Genomics Inform 10:117–122. https://doi.org/10.5808/GI.2012.10.2.117

    Article  PubMed  PubMed Central  Google Scholar 

  66. Lee WC (2003) Searching for disease-susceptibility loci by testing for Hardy-Weinberg disequilibrium in a gene bank of affected individuals. Am J Epidemiol 158:397–400. https://doi.org/10.1093/aje/kwg150

    Article  PubMed  Google Scholar 

  67. Little J, Higgins JP, Ioannidis JP et al (2009) STrengthening the REporting of Genetic Association Studies (STREGA)–an extension of the STROBE statement. Genet Epidemiol 33:581–598. https://doi.org/10.1002/gepi.20410

    Article  PubMed  Google Scholar 

  68. Misra DP, Agarwal V (2018) Systematic reviews: challenges for their justification, related comprehensive searches, and implications. J Korean Med Sci 33:9. https://doi.org/10.3346/jkms.2018.33.e92

    Article  Google Scholar 

  69. Kelley GA, Kelley KS (2019) Systematic reviews and meta-analysis in rheumatology: a gentle introduction for clinicians. Clin Rheumatol 38:2029–2038. https://doi.org/10.1007/s10067-019-04590-6

    Article  PubMed  Google Scholar 

  70. Abou-Raya A, Abou-Raya S, Khadrawe T (2018) Retracted: methotrexate in the treatment of symptomatic knee osteoarthritis: randomised placebo-controlled trial. Ann Rheum Dis 77:e46. https://doi.org/10.1136/annrheumdis-2013-204856

    Article  CAS  PubMed  Google Scholar 

  71. Steinfeld SD, Demols P, Salmon I, Kiss R, Appelboom T (2013) Notice of retraction of two articles (“Infliximab in patients with primary Sjögren’s syndrome: a pilot study” and “Infliximab in patients with primary Sjögren’s syndrome: one-year followup”). Arthritis Rheum 65:814–814. https://doi.org/10.1002/art.37874

    Article  PubMed  Google Scholar 

  72. Kivity S, Shoenfeld Y, Arango M-T et al (2017) Retracted: anti-ribosomal-phosphoprotein autoantibodies penetrate to neuronal cells via neuronal growth associated protein, affecting neuronal cells in vitro. Rheumatology 56:1827–1827. https://doi.org/10.1093/rheumatology/kex259

    Article  PubMed  Google Scholar 

  73. Abou-Raya A, Abou-Raya S, Helmii M (2018) The effect of vitamin d supplementation on inflammatory and hemostatic markers and disease activity in patients with systemic lupus erythematosus: a randomized placebo-controlled trial [retraction of: J Rheumatol. 2013 Mar;40(3):265–272]. J Rheumatol 45:1713. https://doi.org/10.3899/jrheum.111594.ret1

  74. Alfawaz DD, Siebert S, Derakhshan MH (2019) RETRACTED: 249 The relative efficacy of secukinumab in psoriatic arthritis and ankylosing spondylitis: a systematic review and meta-analysis. Rheumatology. https://doi.org/10.1093/rheumatology/kez107.065

    Article  Google Scholar 

  75. Moots R, Liu H (2011) Retraction. Rheumatology 50:2147–2147. https://doi.org/10.1093/rheumatology/ker376

    Article  PubMed  Google Scholar 

  76. Lukić IK, Marusić M (2001) Appointment of statistical editor and quality of statistics in a small medical journal. Croat Med J 42:500–503

    PubMed  Google Scholar 

  77. Misra DP, Ravindran V, Wakhlu A, Sharma A, Agarwal V, Negi VS (2017) Publishing in black and white: the relevance of listing of scientific journals. Rheumatol Int 37:1773–1778. https://doi.org/10.1007/s00296-017-3830-2

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, India

    Durga Prasanna Misra

  2. Department of Internal Medicine No. 2, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine

    Olena Zimba

  3. Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, West Midlands, UK

    Armen Yuri Gasparyan

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  1. Durga Prasanna Misra
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  3. Armen Yuri Gasparyan
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Contributions

The conception and design of the study—DPM and AYG; acquisition of data, analysis and interpretation of data—DPM, OZ, and AYG. Drafting the article—DPM; Revising it critically for important intellectual content—OZ and AYG. Final approval of the version to be submitted—DPM, OZ, and AYG. Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved—DPM, OZ, and AYG.

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Correspondence to Durga Prasanna Misra.

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Misra, D.P., Zimba, O. & Gasparyan, A.Y. Statistical data presentation: a primer for rheumatology researchers. Rheumatol Int 41, 43–55 (2021). https://doi.org/10.1007/s00296-020-04740-z

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