Referências

1.
Grami A. Discrete Probability. Em: Elsevier; 2023:285–305. doi:10.1016/b978-0-12-820656-0.00016-2
2.
Viti A, Terzi A, Bertolaccini L. A practical overview on probability distributions. Journal of Thoracic Disease. 2015;7(3). https://jtd.amegroups.org/article/view/4086.
3.
Benford F. The Law of Anomalous Numbers. Proceedings of the American Philosophical Society. 1938;78(4):551–572. http://www.jstor.org/stable/984802. Acessado novembro 24, 2024.
4.
Tversky A, Kahneman D. Belief in the law of small numbers. Psychological Bulletin. 1971;76(2):105–110. doi:10.1037/h0031322
5.
Bishop DVM, Thompson J, Parker AJ. Can we shift belief in the Law of Small Numbers? Royal Society Open Science. 2022;9(3). doi:10.1098/rsos.211028
6.
Guy RK. The Strong Law of Small Numbers. The American Mathematical Monthly. 1988;95(8):697. doi:10.2307/2322249
7.
Guy RK. The Second Strong Law of Small Numbers. Mathematics Magazine. 1990;63(1):3–20. doi:10.1080/0025570x.1990.11977475
8.
Kwak SG, Kim JH. Central limit theorem: the cornerstone of modern statistics. Korean Journal of Anesthesiology. 2017;70(2):144. doi:10.4097/kjae.2017.70.2.144
9.
Galton F. Regression Towards Mediocrity in Hereditary Stature. The Journal of the Anthropological Institute of Great Britain and Ireland. 1886;15:246. doi:10.2307/2841583
10.
Barnett AG. Regression to the mean: what it is and how to deal with it. International Journal of Epidemiology. 2004;34(1):215–220. doi:10.1093/ije/dyh299
11.
Senn S. Francis Galton and Regression to the Mean. Significance. 2011;8(3):124–126. doi:10.1111/j.1740-9713.2011.00509.x
12.
Recchia D. regtomean: Regression Toward the Mean.; 2022. https://CRAN.R-project.org/package=regtomean.
13.
Altman DG, Bland JM. Statistics Notes: Units of analysis. BMJ. 1997;314(7098):1874–1874. doi:10.1136/bmj.314.7098.1874
14.
Matthews JN, Altman DG, Campbell MJ, Royston P. Analysis of serial measurements in medical research. BMJ. 1990;300(6719):230–235. doi:10.1136/bmj.300.6719.230
15.
Banerjee A, Chaudhury S. Statistics without tears: Populations and samples. Industrial Psychiatry Journal. 2010;19(1):60. doi:10.4103/0972-6748.77642
16.
Martínez-Mesa J, González-Chica DA, Duquia RP, Bonamigo RR, Bastos JL. Sampling: how to select participants in my research study? Anais Brasileiros de Dermatologia. 2016;91(3):326–330. doi:10.1590/abd1806-4841.20165254
17.
Bland JM, Altman DG. Statistics Notes: Bootstrap resampling methods. BMJ. 2015;350(jun02 13):h2622–h2622. doi:10.1136/bmj.h2622
18.
Polin BA, Benisaac E. A longitudinal analysis of the hot hand and gamblers fallacy biases. Judgment and Decision Making. 2023;18. doi:10.1017/jdm.2023.23
19.
Meng XL. Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election. The Annals of Applied Statistics. 2018;12(2). doi:10.1214/18-aoas1161sf
20.
Abelson RP. A variance explanation paradox: When a little is a lot. Psychological Bulletin. 1985;97(1):129–133. doi:10.1037/0033-2909.97.1.129
21.
Berkson J. Limitations of the Application of Fourfold Table Analysis to Hospital Data. Biometrics Bulletin. 1946;2(3):47. doi:10.2307/3002000
22.
Ellsberg D. Risk, Ambiguity, and the Savage Axioms. The Quarterly Journal of Economics. 1961;75(4):643. doi:10.2307/1884324
23.
Freedman DA, Freedman DA. A Note on Screening Regression Equations. The American Statistician. 1983;37(2):152–155. doi:10.1080/00031305.1983.10482729
24.
Freedman LS, Pee D. Return to a Note on Screening Regression Equations. The American Statistician. 1989;43(4):279. doi:10.2307/2685389
25.
Hand DJ. On Comparing Two Treatments. The American Statistician. 1992;46(3):190–192. doi:10.1080/00031305.1992.10475881
26.
LINDLEY DV. A STATISTICAL PARADOX. Biometrika. 1957;44(1-2):187–192. doi:10.1093/biomet/44.1-2.187
27.
Lord FM. A paradox in the interpretation of group comparisons. Psychological Bulletin. 1967;68(5):304–305. doi:10.1037/h0025105
28.
Lord FM. Statistical adjustments when comparing preexisting groups. Psychological Bulletin. 1969;72(5):336–337. doi:10.1037/h0028108
29.
Simpson EH. The Interpretation of Interaction in Contingency Tables. Journal of the Royal Statistical Society: Series B (Methodological). 1951;13(2):238–241. doi:10.1111/j.2517-6161.1951.tb00088.x
30.
Blyth CR. On Simpson’s Paradox and the Sure-Thing Principle. Journal of the American Statistical Association. 1972;67(338):364–366. doi:10.1080/01621459.1972.10482387
31.
Pearl J. Comment: Understanding Simpsons Paradox. The American Statistician. 2014;68(1):8–13. doi:10.1080/00031305.2014.876829
32.
Stein C. INADMISSIBILITY OF THE USUAL ESTIMATOR FOR THE MEAN OF A MULTIVARIATE NORMAL DISTRIBUTION. Em: University of California Press; 1956:197–206. doi:10.1525/9780520313880-018
33.
De S, Sen A. The generalised Gamow-Stern problem. The Mathematical Gazette. 1996;80(488):345–348. doi:10.2307/3619568
34.
Feld SL. Why Your Friends Have More Friends Than You Do. American Journal of Sociology. 1991;96(6):1464–1477. doi:10.1086/229693
35.
Shields M. Information Literacy, Statistical Literacy, Data Literacy. IASSIST Quarterly. 2005;28(2):6. doi:10.29173/iq790
36.
Gal I. Adults’ Statistical Literacy: Meanings, Components, Responsibilities. International Statistical Review. 2002;70(1):1–25. doi:10.1111/j.1751-5823.2002.tb00336.x
37.
Sharma S. Definitions and models of statistical literacy: a literature review. Open Review of Educational Research. 2017;4(1):118–133. doi:10.1080/23265507.2017.1354313
38.
Hidayati NA, Waluya SB, Rochmad, Wardono. Statistics literacy: what, why and how? Journal of Physics: Conference Series. 2020;1613(1):012080. doi:10.1088/1742-6596/1613/1/012080
39.
GOULD R. DATA LITERACY IS STATISTICAL LITERACY. STATISTICS EDUCATION RESEARCH JOURNAL. 2017;16(1):22–25. doi:10.52041/serj.v16i1.209
40.
CALLINGHAM R, WATSON JM. THE DEVELOPMENT OF STATISTICAL LITERACY AT SCHOOL. STATISTICS EDUCATION RESEARCH JOURNAL. 2017;16(1):181–201. doi:10.52041/serj.v16i1.223
41.
Koga S. Characteristics of statistical literacy skills from the perspective of critical thinking. Teaching Statistics. 2022;44(2):59–67. doi:10.1111/test.12302
42.
Amatuzzi MLL, Barreto M do CC, Litvoc J, Leme LEG. Linguagem metodológica: parte 1. Acta Ortopédica Brasileira. 2006;14(1):53–56. doi:10.1590/s1413-78522006000100012
43.
Amatuzzi MLL, Barreto M do CC, Litvoc J, Leme LEG. Linguagem metodológica: parte 2. Acta Ortopédica Brasileira. 2006;14(2):108–112. doi:10.1590/s1413-78522006000200012
44.
Munafò MR, Nosek BA, Bishop DVM, et al. A manifesto for reproducible science. Nature Human Behaviour. 2017;1(1). doi:10.1038/s41562-016-0021
45.
Resnik DB, Shamoo AE. Reproducibility and Research Integrity. Accountability in Research. 2016;24(2):116–123. doi:10.1080/08989621.2016.1257387
46.
Hofner B, Schmid M, Edler L. Reproducible research in statistics: A review and guidelines for the Biometrical Journal. Biometrical Journal. 2015;58(2):416–427. doi:10.1002/bimj.201500156
47.
Mair P. Thou Shalt Be Reproducible! A Technology Perspective. Frontiers in Psychology. 2016;7. doi:10.3389/fpsyg.2016.01079
48.
Hinsen K. A data and code model for reproducible research and executable papers. Procedia Computer Science. 2011;4:579–588. doi:10.1016/j.procs.2011.04.061
49.
Wolff RF, Moons KGM, Riley RD, et al. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Annals of Internal Medicine. 2019;170(1):51–58. doi:10.7326/m18-1376
50.
Sterne JAC, Savović J, Page MJ, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. agosto 2019:l4898. doi:10.1136/bmj.l4898
51.
Shea BJ, Reeves BC, Wells G, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. setembro 2017:j4008. doi:10.1136/bmj.j4008
52.
Sterne JA, Hernán MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. outubro 2016:i4919. doi:10.1136/bmj.i4919
53.
Whiting P, Savović J, Higgins JPT, et al. ROBIS: A new tool to assess risk of bias in systematic reviews was developed. Journal of Clinical Epidemiology. 2016;69:225–234. doi:10.1016/j.jclinepi.2015.06.005
54.
Whiting PF, Rutjes AWS, Westwood ME, et al. QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies. Annals of Internal Medicine. 2011;155(8):529–536. doi:10.7326/0003-4819-155-8-201110180-00009
55.
John LK, Loewenstein G, Prelec D. Measuring the Prevalence of Questionable Research Practices With Incentives for Truth Telling. Psychological Science. 2012;23(5):524–532. doi:10.1177/0956797611430953
56.
Bausell RB. Too Much Medicine: Not Enough Health. Em: New York: Oxford University Press; 2021:56–C3.P203. doi:10.1093/oso/9780197536537.003.0004
57.
Neoh MJY, Carollo A, Lee A, Esposito G. Fifty years of research on questionable research practises in science: quantitative analysis of co-citation patterns. Royal Society Open Science. 2023;10(10). doi:10.1098/rsos.230677
58.
Kleinert S. COPE’s retraction guidelines. The Lancet. 2009;374(9705):1876–1877. doi:10.1016/s0140-6736(09)62074-2
59.
Kerr NL. HARKing: Hypothesizing After the Results are Known. Personality and Social Psychology Review. 1998;2(3):196–217. doi:10.1207/s15327957pspr0203_4
60.
Groot AD de. The meaning of significance for different types of research [translated and annotated by Eric-Jan Wagenmakers, Denny Borsboom, Josine Verhagen, Rogier Kievit, Marjan Bakker, Angelique Cramer, Dora Matzke, Don Mellenbergh, and Han L. J. van der Maas]. Acta Psychologica. 2014;148:188–194. doi:10.1016/j.actpsy.2014.02.001
61.
Andrade C. HARKing, Cherry-Picking, P-Hacking, Fishing Expeditions, and Data Dredging and Mining as Questionable Research Practices. The Journal of Clinical Psychiatry. 2021;82(1). doi:10.4088/jcp.20f13804
62.
Stefan AM, Schönbrodt FD. Big little lies: a compendium and simulation ofp-hacking strategies. Royal Society Open Science. 2023;10(2). doi:10.1098/rsos.220346
63.
Chuard PJC, Vrtílek M, Head ML, Jennions MD. Evidence that nonsignificant results are sometimes preferred: Reverse P-hacking or selective reporting? PLOS Biology. 2019;17(1):e3000127. doi:10.1371/journal.pbio.3000127
64.
Sasaki K, Yamada Y. SPARKing: Sample-size planning after the results are known. Frontiers in Human Neuroscience. 2023;17. doi:10.3389/fnhum.2023.912338
65.
Armitage P, McPherson CK, Rowe BC. Repeated Significance Tests on Accumulating Data. Journal of the Royal Statistical Society Series A (General). 1969;132(2):235. doi:10.2307/2343787
66.
Horton R. The rhetoric of research. BMJ. 1995;310(6985):985–987. doi:10.1136/bmj.310.6985.985
67.
Chiu K, Grundy Q, Bero L. Spin in published biomedical literature: A methodological systematic review. Boutron I, org. PLOS Biology. 2017;15(9):e2002173. doi:10.1371/journal.pbio.2002173
68.
Picano E. Who is the author: genuine, honorary, ghost, gold, and fake authors? Exploration of Cardiology. 2024;2(3):88–96. doi:10.37349/ec.2024.00024
69.
Nosek BA, Ebersole CR, DeHaven AC, Mellor DT. The preregistration revolution. Proceedings of the National Academy of Sciences. 2018;115(11):2600–2606. doi:10.1073/pnas.1708274114
70.
P. Simmons J, D. Nelson L, Simonsohn U. Pre-registration: Why and How. Journal of Consumer Psychology. 2021;31(1):151–162. doi:10.1002/jcpy.1208
71.
Hartgerink C, Aust F. retractcheck: Retraction Scanner.; 2025. https://github.com/chartgerink/retractcheck.
72.
Ihaka R, Gentleman R. R: A Language for Data Analysis and Graphics. Journal of Computational and Graphical Statistics. 1996;5(3):299. doi:10.2307/1390807
73.
Nwanganga F, Chapple M. Introduction to R and RStudio. Em: Nwanganga F, Chapple M, orgs. Practical Machine Learning in R. John Wiley & Sons, Ltd; 2020:25–52. doi:10.1002/9781119591542.ch2
74.
R Core Team. The Comprehensive R Archive Network. 2021. https://cran.r-project.org.
75.
Allaire J, Xie Y, Dervieux C, et al. rmarkdown: Dynamic Documents for R.; 2023. https://CRAN.R-project.org/package=rmarkdown.
76.
Holmes DT, Mobini M, McCudden CR. Reproducible manuscript preparation with RMarkdown application to JMSACL and other Elsevier Journals. Journal of Mass Spectrometry and Advances in the Clinical Lab. 2021;22:8–16. doi:10.1016/j.jmsacl.2021.09.002
77.
Love J, Selker R, Marsman M, et al. JASP: Graphical Statistical Software for Common Statistical Designs. Journal of Statistical Software. 2019;88(2). doi:10.18637/jss.v088.i02
78.
ŞAHİN M, AYBEK E. Jamovi: An Easy to Use Statistical Software for the Social Scientists. International Journal of Assessment Tools in Education. 2020;6(4):670–692. doi:10.21449/ijate.661803
79.
Selker R, Love J, Dropmann D. jmv: The jamovi Analyses.; 2023. https://CRAN.R-project.org/package=jmv.
80.
Love J. jmvconnect: Connect to the jamovi Statistical Spreadsheet.; 2022. https://CRAN.R-project.org/package=jmvconnect.
81.
Racine JS. RStudio: A Platform-Independent IDE for R and Sweave. Journal of Applied Econometrics. 2011;27(1):167–172. doi:10.1002/jae.1278
82.
Aden-Buie G, Schloerke B, Allaire J, Rossell Hayes A. learnr: Interactive Tutorials for R.; 2023. https://CRAN.R-project.org/package=learnr.
83.
Schwab, Simon, Held, Leonhard. Statistical programming: Small mistakes, big impacts. Wiley-Blackwell Publishing, Inc. 2021. doi:10.5167/UZH-205154
84.
Eglen SJ, Marwick B, Halchenko YO, et al. Toward standard practices for sharing computer code and programs in neuroscience. Nature Neuroscience. 2017;20(6):770–773. doi:10.1038/nn.4550
85.
Xie Y. formatR: Format R Code Automatically.; 2022. https://CRAN.R-project.org/package=formatR.
86.
Müller K, Walthert L. styler: Non-Invasive Pretty Printing of R Code.; 2023. https://CRAN.R-project.org/package=styler.
87.
Hester J, Angly F, Hyde R, et al. lintr: A Linter for R Code.; 2023. https://CRAN.R-project.org/package=lintr.
88.
All R CRAN packages [Full List]. 2025. https://r-packages.io/packages. Acessado fevereiro 11, 2025.
89.
R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2023. https://www.R-project.org/.
90.
Wickham H, Danenberg P, Csárdi G, Eugster M. roxygen2: In-Line Documentation for R.; 2024. doi:10.32614/CRAN.package.roxygen2
91.
Trisovic A, Lau MK, Pasquier T, Crosas M. A large-scale study on research code quality and execution. Scientific Data. 2022;9(1). doi:10.1038/s41597-022-01143-6
92.
Gohel D, Ross N. officedown: Enhanced R Markdown Format for Word and PowerPoint.; 2023. https://CRAN.R-project.org/package=officedown.
93.
Xie Y. bookdown: Authoring Books and Technical Documents with R Markdown. Chapman; Hall/CRC; 2023. https://bookdown.org/yihui/bookdown/.
94.
Ioannidis JPA. How to Make More Published Research True. PLoS Medicine. 2014;11(10):e1001747. doi:10.1371/journal.pmed.1001747
95.
Krieger N, Perzynski A, Dalton J. projects: A Project Infrastructure for Researchers.; 2021. https://CRAN.R-project.org/package=projects.
96.
Schultze A, Tazare J. The role of programming code sharing in improving the transparency of medical research. BMJ. outubro 2023:p2402. doi:10.1136/bmj.p2402
97.
R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2023. https://www.R-project.org/.
98.
Zhao Y, Xiao N, Anderson K, Zhang Y. Electronic common technical document submission with analysis using R. Clinical Trials. 2022;20(1):89–92. doi:10.1177/17407745221123244
99.
Francisco Rodríguez-Sánchez, Connor P. Jackson, Shaurita D. Hutchins. grateful: Facilitate citation of R packages.; 2023. https://github.com/Pakillo/grateful.
100.
Eglen SJ, Marwick B, Halchenko YO, et al. Toward standard practices for sharing computer code and programs in neuroscience. Nature Neuroscience. 2017;20(6):770–773. doi:10.1038/nn.4550
101.
Trisovic A, Lau MK, Pasquier T, Crosas M. A large-scale study on research code quality and execution. Scientific Data. 2022;9(1). doi:10.1038/s41597-022-01143-6
102.
Metropolis N, Ulam S. The Monte Carlo Method. Journal of the American Statistical Association. 1949;44(247):335–341. doi:10.1080/01621459.1949.10483310
103.
HÄGGSTRÖM O. Problem Solving is Often a Matter of Cooking Up an Appropriate Markov Chain*. Scandinavian Journal of Statistics. 2007;34(4):768–780. doi:10.1111/j.1467-9469.2007.00561.x
104.
Goldfeld K, Wujciak-Jens J. simstudy: Illuminating research methods through data generation. Journal of Open Source Software. 2020;5:2763. doi:10.21105/joss.02763
105.
DeBruine L. faux: Simulation for Factorial Designs.; 2023. doi:10.5281/zenodo.2669586
106.
Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR, Olino TM. Tutorial: Power Analyses for Interaction Effects in Cross-Sectional Regressions. Advances in Methods and Practices in Psychological Science. 2023;6(3):25152459231187531. doi:10.1177/25152459231187531
107.
Monks T, Currie CSM, Onggo BS, Robinson S, Kunc M, Taylor SJE. Strengthening the reporting of empirical simulation studies: Introducing the STRESS guidelines. Journal of Simulation. 2018;13(1):55–67. doi:10.1080/17477778.2018.1442155
108.
Altman DG, Bland JM. Statistics notes Variables and parameters. BMJ. 1999;318(7199):1667–1667. doi:10.1136/bmj.318.7199.1667
109.
Vetter TR. Fundamentals of Research Data and Variables. Anesthesia & Analgesia. 2017;125(4):1375–1380. doi:10.1213/ane.0000000000002370
110.
Ali Z, Bhaskar Sb. Basic statistical tools in research and data analysis. Indian Journal of Anaesthesia. 2016;60(9):662. doi:10.4103/0019-5049.190623
111.
Dettori JR, Norvell DC. The Anatomy of Data. Global Spine Journal. 2018;8(3):311–313. doi:10.1177/2192568217746998
112.
Kaliyadan F, Kulkarni V. Types of variables, descriptive statistics, and sample size. Indian Dermatology Online Journal. 2019;10(1):82. doi:10.4103/idoj.idoj_468_18
113.
Barkan H. Statistics in clinical research: Important considerations. Annals of Cardiac Anaesthesia. 2015;18(1):74. doi:10.4103/0971-9784.148325
114.
Bland JM, Altman DG. Statistics Notes: Transforming data. BMJ. 1996;312(7033):770–770. doi:10.1136/bmj.312.7033.770
115.
Fedorov V, Mannino F, Zhang R. Consequences of dichotomization. Pharmaceutical Statistics. 2009;8(1):50–61. doi:10.1002/pst.331
116.
Osborne J. Improving your data transformations: Applying the Box-Cox transformation. University of Massachusetts Amherst. 2010. doi:10.7275/QBPC-GK17
117.
Box GEP, Cox DR. An Analysis of Transformations. Journal of the Royal Statistical Society: Series B (Methodological). 1964;26(2):211–243. doi:10.1111/j.2517-6161.1964.tb00553.x
118.
Venables WN, Ripley BD. Modern Applied Statistics with S. Springer; 2002. https://www.stats.ox.ac.uk/pub/MASS4/.
119.
MacCallum RC, Zhang S, Preacher KJ, Rucker DD. On the practice of dichotomization of quantitative variables. Psychological Methods. 2002;7(1):19–40. doi:10.1037/1082-989x.7.1.19
120.
Altman DG, Royston P. The cost of dichotomising continuous variables. BMJ. 2006;332(7549):1080.1. doi:10.1136/bmj.332.7549.1080
121.
Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Statistics in Medicine. 2005;25(1):127–141. doi:10.1002/sim.2331
122.
Collins GS, Ogundimu EO, Cook JA, Manach YL, Altman DG. Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic model. Statistics in Medicine. 2016;35(23):4124–4135. doi:10.1002/sim.6986
123.
Nelson SLP, Ramakrishnan V, Nietert PJ, Kamen DL, Ramos PS, Wolf BJ. An evaluation of common methods for dichotomization of continuous variables to discriminate disease status. Communications in Statistics – Theory and Methods. 2017;46(21):10823–10834. doi:10.1080/03610926.2016.1248783
124.
Bennette C, Vickers A. Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents. BMC Medical Research Methodology. 2012;12(1). doi:10.1186/1471-2288-12-21
125.
Barnier J, Briatte F, Larmarange J. questionr: Functions to Make Surveys Processing Easier.; 2023. https://CRAN.R-project.org/package=questionr.
126.
Aguinis H, Pierce CA, Culpepper SA. Scale Coarseness as a Methodological Artifact. Organizational Research Methods. 2008;12(4):623–652. doi:10.1177/1094428108318065
127.
Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3(1):32–35. doi:10.1002/1097-0142(1950)3:1<32::aid-cncr2820030106>3.0.co;2-3
128.
Strobl C, Boulesteix AL, Augustin T. Unbiased split selection for classification trees based on the Gini Index. Computational Statistics & Data Analysis. 2007;52(1):483–501. doi:10.1016/j.csda.2006.12.030
129.
Pearson K. X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science. 1900;50(302):157–175. doi:10.1080/14786440009463897
130.
Greiner M, Pfeiffer D, Smith RD. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Preventive Veterinary Medicine. 2000;45(1-2):23–41. doi:10.1016/s0167-5877(00)00115-x
131.
Fleiss JL. Measuring nominal scale agreement among many raters. Psychological Bulletin. 1971;76(5):378–382. doi:10.1037/h0031619
132.
Olson K. What Are Data? Qualitative Health Research. 2021;31(9):1567–1569. doi:10.1177/10497323211015960
133.
Smeden M van. A Very Short List of Common Pitfalls in Research Design, Data Analysis, and Reporting. PRiMER. 2022;6. doi:10.22454/PRiMER.2022.511416
134.
Baillie M, Cessie S le, Schmidt CO, Lusa L, Huebner M. Ten simple rules for initial data analysis. PLOS Computational Biology. 2022;18(2):e1009819. doi:10.1371/journal.pcbi.1009819
135.
Buttliere B. Adopting standard variable labels solves many of the problems with sharing and reusing data. Methodological Innovations. 2021;14(2):205979912110266. doi:10.1177/20597991211026616
136.
Pebesma E, Mailund T, Hiebert J. Measurement Units in R. The R Journal. 2016;8. doi:10.32614/RJ-2016-061
137.
Firke S. janitor: Simple Tools for Examining and Cleaning Dirty Data.; 2023. https://CRAN.R-project.org/package=janitor.
138.
Harrell Jr FE. Hmisc: Harrell Miscellaneous.; 2023. https://CRAN.R-project.org/package=Hmisc.
139.
Bryer J, Speerschneider K. likert: Analysis and Visualization Likert Items.; 2016. https://CRAN.R-project.org/package=likert.
140.
Larmarange J. ggstats: Extension to ’ggplot2’ for Plotting Stats.; 2025. doi:10.32614/CRAN.package.ggstats
141.
Ferris TLJ. A new definition of measurement. Measurement. 2004;36(1):101–109. doi:10.1016/j.measurement.2004.03.001
142.
R Core Team. R: A Language and Environment for Statistical Computing.; 2023. https://www.R-project.org/.
143.
Healy MJR, Goldstein H. Regression to the mean. Annals of Human Biology. 1978;5(3):277–280. doi:10.1080/03014467800002891
144.
Altman DG, Bland JM. Measurement in Medicine: The Analysis of Method Comparison Studies. The Statistician. 1983;32(3):307. doi:10.2307/2987937
145.
Menditto A, Patriarca M, Magnusson B. Understanding the meaning of accuracy, trueness and precision. Accreditation and Quality Assurance. 2006;12(1):45–47. doi:10.1007/s00769-006-0191-z
146.
Streiner DL, Norman GR. Precision and Accuracy: Two Terms That Are Neither. Journal of Clinical Epidemiology. 2006;59(4):327–330. doi:10.1016/j.jclinepi.2005.09.005
147.
Tierney N, Cook D. Expanding Tidy Data Principles to Facilitate Missing Data Exploration, Visualization and Assessment of Imputations. Journal of Statistical Software. 2023;105(7). doi:10.18637/jss.v105.i07
148.
Hammill D. DataEditR: An Interactive Editor for Viewing, Entering, Filtering & Editing Data.; 2022. https://CRAN.R-project.org/package=DataEditR.
149.
Broman KW, Woo KH. Data Organization in Spreadsheets. The American Statistician. 2018;72(1):2–10. doi:10.1080/00031305.2017.1375989
150.
Juluru K, Eng J. Use of Spreadsheets for Research Data Collection and Preparation: Academic Radiology. 2015;22(12):1592–1599. doi:10.1016/j.acra.2015.08.024
151.
Dowle M, Srinivasan A. data.table: Extension of ‘data.frame‘.; 2023. https://CRAN.R-project.org/package=data.table.
152.
Altman DG, Bland JM. Missing data. BMJ. 2007;334(7590):424–424. doi:10.1136/bmj.38977.682025.2c
153.
Heymans MW, Twisk JWR. Handling missing data in clinical research. Journal of Clinical Epidemiology. setembro 2022. doi:10.1016/j.jclinepi.2022.08.016
154.
Carpenter JR, Smuk M. Missing data: A statistical framework for practice. Biometrical Journal. 2021;63(5):915–947. doi:10.1002/bimj.202000196
155.
Yanagida T. misty: Miscellaneous Functions.; 2023. https://CRAN.R-project.org/package=misty.
156.
Little RJA. A Test of Missing Completely at Random for Multivariate Data with Missing Values. Journal of the American Statistical Association. 1988;83(404):1198–1202. doi:10.1080/01621459.1988.10478722
157.
Tierney N, Cook D. Expanding Tidy Data Principles to Facilitate Missing Data Exploration, Visualization and Assessment of Imputations. Journal of Statistical Software. 2023;105(7):1–31. doi:10.18637/jss.v105.i07
158.
Akl EA, Shawwa K, Kahale LA, et al. Reporting missing participant data in randomised trials: systematic survey of the methodological literature and a proposed guide. BMJ Open. 2015;5(12):e008431. doi:10.1136/bmjopen-2015-008431
159.
Austin PC, Buuren S van. Logistic regression vs. predictive mean matching for imputing binary covariates. Statistical Methods in Medical Research. setembro 2023. doi:10.1177/09622802231198795
160.
Buuren S van, Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software. 2011;45:1–67. doi:10.18637/jss.v045.i03
161.
Rubin DB. Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations. Journal of Business & Economic Statistics. 1986;4(1):87. doi:10.2307/1391390
162.
Little RJA. Missing-Data Adjustments in Large Surveys. Journal of Business & Economic Statistics. 1988;6(3):287–296. doi:10.1080/07350015.1988.10509663
163.
Robitzsch A, Grund S. miceadds: Some Additional Multiple Imputation Functions, Especially for mice.; 2023. https://CRAN.R-project.org/package=miceadds.
164.
FitzJohn R. ids: Generate Random Identifiers.; 2017. https://CRAN.R-project.org/package=ids.
165.
Brown C. hash: Full Featured Implementation of Hash Tables/Associative Arrays/Dictionaries.; 2023. https://CRAN.R-project.org/package=hash.
166.
Hendricks P. anonymizer: Anonymize Data Containing Personally Identifiable Information.; 2023. https://github.com/paulhendricks/anonymizer.
167.
Lucas DE with contributions by A, Tuszynski J, Bengtsson H, et al. digest: Create Compact Hash Digests of R Objects.; 2023. https://CRAN.R-project.org/package=digest.
168.
Nowok B, Raab GM, Dibben C. synthpop: Bespoke Creation of Synthetic Data in R. Journal of Statistical Software. 2016;74. doi:10.18637/jss.v074.i11
169.
Chatfield C. Exploratory data analysis. European Journal of Operational Research. 1986;23(1):5–13. doi:10.1016/0377-2217(86)90209-2
170.
Ferketich S, Verran J. Technical Notes. Western Journal of Nursing Research. 1986;8(4):464–466. doi:10.1177/019394598600800409
171.
Landis SC, Amara SG, Asadullah K, et al. A call for transparent reporting to optimize the predictive value of preclinical research. Nature. 2012;490(7419):187–191. doi:10.1038/nature11556
172.
Huebner M, Vach W, Cessie S le. A systematic approach to initial data analysis is good research practice. The Journal of Thoracic and Cardiovascular Surgery. 2016;151(1):25–27. doi:10.1016/j.jtcvs.2015.09.085
173.
Krasser R. explore: Simplifies Exploratory Data Analysis.; 2023. https://CRAN.R-project.org/package=explore.
174.
Petersen AH, Ekstrøm CT. dataMaid: Your Assistant for Documenting Supervised Data Quality Screening in R. Journal of Statistical Software. 2019;90. doi:10.18637/jss.v090.i06
175.
Cui B. DataExplorer: Automate Data Exploration and Treatment.; 2020. https://CRAN.R-project.org/package=DataExplorer.
176.
Dayanand Ubrangala, R K, Prasad Kondapalli R, Putatunda S. SmartEDA: Summarize and Explore the Data.; 2022. https://CRAN.R-project.org/package=SmartEDA.
177.
Meyer F, Perrier V. esquisse: Explore and Visualize Your Data Interactively.; 2022. https://CRAN.R-project.org/package=esquisse.
178.
Zuur AF, Ieno EN, Elphick CS. A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution. 2009;1(1):3–14. doi:10.1111/j.2041-210x.2009.00001.x
179.
Mock T. gtExtras: Extending gt for Beautiful HTML Tables.; 2023. https://CRAN.R-project.org/package=gtExtras.
180.
Nijs V. radiant: Business Analytics using R and Shiny.; 2023. https://CRAN.R-project.org/package=radiant.
181.
Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer; 2016. https://ggplot2.tidyverse.org.
182.
Prunello M, Mari G. ggcleveland: Implementation of Plots from Cleveland’s Visualizing Data Book.; 2021. doi:10.32614/CRAN.package.ggcleveland
183.
Gerring J. Mere Description. British Journal of Political Science. 2012;42(4):721–746. doi:10.1017/s0007123412000130
184.
Cummings P, Rivara FP. Reporting Statistical Information in Medical Journal Articles. Archives of Pediatrics & Adolescent Medicine. 2003;157(4):321. doi:10.1001/archpedi.157.4.321
185.
Cole TJ. Setting number of decimal places for reporting risk ratios: rule of four. BMJ. 2015;350(apr27 3):h1845–h1845. doi:10.1136/bmj.h1845
186.
Cole TJ. Too many digits: the presentation of numerical data. Archives of Disease in Childhood. 2015;100(7):608–609. doi:10.1136/archdischild-2014-307149
187.
Inskip H, Ntani G, Westbury L, et al. Getting started with tables. Archives of Public Health. 2017;75(1). doi:10.1186/s13690-017-0180-1
188.
Kwak SG, Kang H, Kim JH, et al. The principles of presenting statistical results: Table. Korean Journal of Anesthesiology. 2021;74(2):115–119. doi:10.4097/kja.20582
189.
Gohel D, Skintzos P. flextable: Functions for Tabular Reporting.; 2023. https://CRAN.R-project.org/package=flextable.
190.
Thériault R. rempsyc: Convenience functions for psychology. Journal of Open Source Software. 2023;8:5466. doi:10.21105/joss.05466
191.
Rich B. table1: Tables of Descriptive Statistics in HTML.; 2023. https://CRAN.R-project.org/package=table1.
192.
Sjoberg DD, Whiting K, Curry M, Lavery JA, Larmarange J. Reproducible Summary Tables with the gtsummary Package. The R Journal. 2021;13:570–580. doi:10.32614/RJ-2021-053
193.
Barnett A. Automated detection of over- and under-dispersion in baseline tables in randomised controlled trials. F1000Research. 2023;11:783. doi:10.12688/f1000research.123002.2
194.
Westreich D, Greenland S. The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients. American Journal of Epidemiology. 2013;177(4):292–298. doi:10.1093/aje/kws412
195.
Chen H, Lu Y, Slye N. Testing for baseline differences in clinical trials. International Journal of Clinical Trials. 2020;7(2):150. doi:10.18203/2349-3259.ijct20201720
196.
Pijls BG. The Table I Fallacy: P Values in Baseline Tables of Randomized Controlled Trials. Journal of Bone and Joint Surgery. 2022;104(16):e71. doi:10.2106/jbjs.21.01166
197.
Greenhalgh T. How to read a paper: Statistics for the non-statistician. I: Different types of data need different statistical tests. BMJ. 1997;315(7104):364–366. doi:10.1136/bmj.315.7104.364
198.
Hayes-Larson E, Kezios KL, Mooney SJ, Lovasi G. Who is in this study, anyway? Guidelines for a useful Table 1. Journal of Clinical Epidemiology. 2019;114:125–132. doi:10.1016/j.jclinepi.2019.06.011
199.
Bandoli G, Palmsten K, Chambers CD, Jelliffe-Pawlowski LL, Baer RJ, Thompson CA. Revisiting the Table 2 fallacy: A motivating example examining preeclampsia and preterm birth. Paediatric and Perinatal Epidemiology. 2018;32(4):390–397. doi:10.1111/ppe.12474
200.
Park JH, Lee DK, Kang H, et al. The principles of presenting statistical results using figures. Korean Journal of Anesthesiology. 2022;75(2):139–150. doi:10.4097/kja.21508
201.
Sievert C. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman; Hall/CRC; 2020. https://plotly-r.com.
202.
Wei T, Simko V. R package ’corrplot’: Visualization of a Correlation Matrix.; 2024. https://github.com/taiyun/corrplot.
203.
Cumming G, Fidler F, Vaux DL. Error bars in experimental biology. The Journal of Cell Biology. 2007;177(1):7–11. doi:10.1083/jcb.200611141
204.
Krzywinski M, Altman N. Error bars. Nature Methods. 2013;10(10):921–922. doi:10.1038/nmeth.2659
205.
Weissgerber TL, Winham SJ, Heinzen EP, et al. Reveal, Dont Conceal. Circulation. 2019;140(18):1506–1518. doi:10.1161/circulationaha.118.037777
206.
Xiao N. ggsci: Scientific Journal and Sci-Fi Themed Color Palettes for ggplot2.; 2023. https://CRAN.R-project.org/package=ggsci.
207.
R Core Team. R: A Language and Environment for Statistical Computing.; 2024. https://www.R-project.org/.
208.
Urbanek S, Johnson K. tiff: Read and Write TIFF Images.; 2022. https://CRAN.R-project.org/package=tiff.
209.
S M. Frequency distribution. Journal of Pharmacology and Pharmacotherapeutics. 2011;2(1):54–56. doi:10.4103/0976-500x.77120
210.
Sturges HA. The Choice of a Class Interval. Journal of the American Statistical Association. 1926;21(153):65–66. doi:10.1080/01621459.1926.10502161
211.
SCOTT DW. On optimal and data-based histograms. Biometrika. 1979;66(3):605–610. doi:10.1093/biomet/66.3.605
212.
Freedman D, Diaconis P. On the histogram as a density estimator:L 2 theory. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete. 1981;57(4):453–476. doi:10.1007/bf01025868
213.
Kay M. ggdist: Visualizations of Distributions and Uncertainty in the Grammar of Graphics. IEEE Transactions on Visualization and Computer Graphics. 2024;30(1):414–424. doi:10.1109/TVCG.2023.3327195
214.
Tang Y, Horikoshi M, Li W. ggfortify: Unified Interface to Visualize Statistical Result of Popular R Packages. Vol 8.; 2016. doi:10.32614/RJ-2016-060
215.
Rochon J, Gondan M, Kieser M. To test or not to test: Preliminary assessment of normality when comparing two independent samples. BMC Medical Research Methodology. 2012;12(1). doi:10.1186/1471-2288-12-81
216.
Schmider E, Ziegler M, Danay E, Beyer L, Bühner M. Is It Really Robust? Methodology. 2010;6(4):147–151. doi:10.1027/1614-2241/a000016
217.
Kanji G. 100 Statistical Tests. SAGE Publications Ltd; 2006. doi:10.4135/9781849208499
218.
Curran-Everett D. Explorations in statistics: standard deviations and standard errors. Advances in Physiology Education. 2008;32(3):203–208. doi:10.1152/advan.90123.2008
219.
Altman DG, Bland JM. Statistics Notes: Quartiles, quintiles, centiles, and other quantiles. BMJ. 1994;309(6960):996–996. doi:10.1136/bmj.309.6960.996
220.
S. M. Measures of central tendency: The mean. Journal of Pharmacology and Pharmacotherapeutics. 2011;2(2):140–142. doi:10.4103/0976-500x.81920
221.
S. M. Measures of central tendency: Median and mode. Journal of Pharmacology and Pharmacotherapeutics. 2011;2(3):214–215. doi:10.4103/0976-500x.83300
222.
Manikandan S. Measures of dispersion. Journal of Pharmacology and Pharmacotherapeutics. 2011;2(4):315–316. doi:10.4103/0976-500x.85931
223.
Sahai H, Misra S. Definitions of Sample Variance: Some Teaching Problems to be Overcome. The Statistician. 1992;41(1):55. doi:10.2307/2348636
224.
Leys C, Delacre M, Mora YL, Lakens D, Ley C. How to Classify, Detect, and Manage Univariate and Multivariate Outliers, With Emphasis on Pre-Registration. International Review of Social Psychology. 2019;32(1). doi:10.5334/irsp.289
225.
Rousseeuw PJ, Hubert M. Robust statistics for outlier detection. WIREs Data Mining and Knowledge Discovery. 2011;1(1):73–79. doi:10.1002/widm.2
226.
Daszykowski M, Kaczmarek K, Vander Heyden Y, Walczak B. Robust statistics in data analysis A review. Chemometrics and Intelligent Laboratory Systems. 2007;85(2):203–219. doi:10.1016/j.chemolab.2006.06.016
227.
Mair P, Wilcox R. Robust Statistical Methods in R Using the WRS2 Package. Behavior Research Methods. 2020;52:464--488. doi:10.3758/s13428-019-01246-w
228.
Leys C, Ley C, Klein O, Bernard P, Licata L. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology. 2013;49(4):764–766. doi:10.1016/j.jesp.2013.03.013
229.
Leys C, Klein O, Dominicy Y, Ley C. Detecting multivariate outliers: Use a robust variant of the Mahalanobis distance. Journal of Experimental Social Psychology. 2018;74:150–156. doi:10.1016/j.jesp.2017.09.011
230.
Tukey JW, McLaughlin DH. Less Vulnerable Confidence and Significance Procedures for Location Based on a Single Sample: Trimming/Winsorization 1. Sankhyā: The Indian Journal of Statistics, Series A (1961-2002). 1963;25(3):331–352. http://www.jstor.org/stable/25049278. Acessado abril 11, 2025.
231.
Komsta L. outliers: Tests for Outliers.; 2022. https://CRAN.R-project.org/package=outliers.
232.
Loh PL. A Theoretical Review of Modern Robust Statistics. Annual Review of Statistics and Its Application. 2025;12(1):477–496. doi:10.1146/annurev-statistics-112723-034446
233.
Mair P, Wilcox R, Indrajeet P. A Collection of Robust Statistical Methods.; 2025. https://CRAN.R-project.org/package=WRS2.
234.
Breznau N, Rinke EM, Wuttke A, et al. Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty. Proceedings of the National Academy of Sciences. 2022;(44):e2203150119. doi:10.1073/pnas.2203150119
235.
Dwivedi AK, Shukla R. Evidence-based statistical analysis and methods in biomedical research (SAMBR) checklists according to design features. CANCER REPORTS. 2019;3(4). doi:10.1002/cnr2.1211
236.
Dwivedi AK. How to Write Statistical Analysis Section in Medical Research. Journal of Investigative Medicine. 2022;70(8):1759–1770. doi:10.1136/jim-2022-002479
237.
Kim N, Fischer AH, Dyring-Andersen B, Rosner B, Okoye GA. Research Techniques Made Simple: Choosing Appropriate Statistical Methods for Clinical Research. Journal of Investigative Dermatology. 2017;137(10):e173–e178. doi:10.1016/j.jid.2017.08.007
238.
Marusteri M, Bacarea V. Comparing groups for statistical differences: how to choose the right statistical test? Biochemia Medica. 2010:15–32. doi:10.11613/bm.2010.004
239.
Mishra P, Pandey C, Singh U, Keshri A, Sabaretnam M. Selection of appropriate statistical methods for data analysis. Annals of Cardiac Anaesthesia. 2019;22(3):297. doi:10.4103/aca.aca_248_18
240.
Ray A, Najmi A, Sadasivam B. How to choose and interpret a statistical test? An update for budding researchers. Journal of Family Medicine and Primary Care. 2021;10(8):2763. doi:10.4103/jfmpc.jfmpc_433_21
241.
Nayak B, Hazra A. How to choose the right statistical test? Indian Journal of Ophthalmology. 2011;59(2):85. doi:10.4103/0301-4738.77005
242.
Shankar S, Singh R. Demystifying statistics: How to choose a statistical test? Indian Journal of Rheumatology. 2014;9(2):77–81. doi:10.1016/j.injr.2014.04.002
243.
Curran-Everett D. Explorations in statistics: hypothesis tests and P values. Advances in Physiology Education. 2009;33(2):81–86. doi:10.1152/advan.90218.2008
244.
Goodman SN. Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy. Annals of Internal Medicine. 1999;130(12):995. doi:10.7326/0003-4819-130-12-199906150-00008
245.
McCaskey K, Rainey C. Substantive Importance and the Veil of Statistical Significance. Statistics, Politics and Policy. 2015;6(1-2). doi:10.1515/spp-2015-0001
246.
Vandenbroucke JP, Pearce N. From ideas to studies: how to get ideas and sharpen them into research questions. Clinical Epidemiology. 2018;Volume 10:253–264. doi:10.2147/clep.s142940
247.
Lakens D, Scheel AM, Isager PM. Equivalence Testing for Psychological Research: A Tutorial. Advances in Methods and Practices in Psychological Science. 2018;1(2):259–269. doi:10.1177/2515245918770963
248.
Sullivan GM, Feinn R. Using Effect Sizeor Why the P Value Is Not Enough. Journal of Graduate Medical Education. 2012;4(3):279–282. doi:10.4300/jgme-d-12-00156.1
249.
Cumming G, Finch S. Inference by Eye: Confidence Intervals and How to Read Pictures of Data. American Psychologist. 2005;60(2):170–180. doi:10.1037/0003-066x.60.2.170
250.
Goodman SN. Aligning statistical and scientific reasoning. Science. 2016;352(6290):1180–1181. doi:10.1126/science.aaf5406
251.
Greenhalgh T. How to read a paper: Statistics for the non-statistician. II: ̈Significanẗ relations and their pitfalls. BMJ. 1997;315(7105):422–425. doi:10.1136/bmj.315.7105.422
252.
Weintraub PG. The Importance of Publishing Negative Results. Journal of Insect Science. 2016;16(1):109. doi:10.1093/jisesa/iew092
253.
Altman DG, Bland JM. Statistics notes: Absence of evidence is not evidence of absence. BMJ. 1995;311(7003):485–485. doi:10.1136/bmj.311.7003.485
254.
Gelman A, Carlin J. Beyond Power Calculations. Perspectives on Psychological Science. 2014;9(6):641–651. doi:10.1177/1745691614551642
255.
Lu J, Qiu Y, Deng A. A note on Type S/M errors in hypothesis testing. British Journal of Mathematical and Statistical Psychology. 2018;72(1):1–17. doi:10.1111/bmsp.12132
256.
Kim HY. Statistical notes for clinical researchers: effect size. Restorative Dentistry & Endodontics. 2015;40(4):328. doi:10.5395/rde.2015.40.4.328
257.
Ben-Shachar MS, Lüdecke D, Makowski D. effectsize: Estimation of Effect Size Indices and Standardized Parameters. Journal of Open Source Software. 2020;5:2815. doi:10.21105/joss.02815
258.
Champely S. pwr: Basic Functions for Power Analysis.; 2020. https://CRAN.R-project.org/package=pwr.
259.
GREENLAND S, SCHLESSELMAN JJ, CRIQUI MH. THE FALLACY OF EMPLOYING STANDARDIZED REGRESSION COEFFICIENTS AND CORRELATIONS AS MEASURES OF EFFECT. American Journal of Epidemiology. 1986;123(2):203–208. doi:10.1093/oxfordjournals.aje.a114229
260.
Greenland S, Maclure M, Schlesselman JJ, Poole C, Morgenstern H. Standardized Regression Coefficients. Epidemiology. 1991;2(5):387–392. doi:10.1097/00001648-199109000-00015
261.
Bours MJL. Using mediators to understand effect modification and interaction. Journal of Clinical Epidemiology. setembro 2023. doi:10.1016/j.jclinepi.2023.09.005
262.
Altman DG, Matthews JNS. Statistics Notes: Interaction 1: heterogeneity of effects. BMJ. 1996;313(7055):486–486. doi:10.1136/bmj.313.7055.486
263.
Pinheiro J, Bates D, R Core Team. nlme: Linear and Nonlinear Mixed Effects Models.; 2023. https://CRAN.R-project.org/package=nlme.
264.
Sabanes Bove D, Dedic J, Kelkhoff D, et al. mmrm: Mixed Models for Repeated Measures.; 2022. https://CRAN.R-project.org/package=mmrm.
265.
Lenth RV. emmeans: Estimated Marginal Means, aka Least-Squares Means.; 2023. https://CRAN.R-project.org/package=emmeans.
266.
Baron RM, Kenny DA. The moderatormediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology. 1986;51(6):1173–1182. doi:10.1037/0022-3514.51.6.1173
267.
LATTER OH. THE EGG OF CUCULUS CANORUS: AN ENQUIRY INTO THE DIMENSIONS OF THE CUCKOO’S EGO AND THE RELATION OF THE VARIATIONS TO THE SIZE OF THE EGGS OF THE FOSTER-PARENT, WITH NOTES ON COLORATION, &c. Biometrika. 1902;1(2):164–176. doi:10.1093/biomet/1.2.164
268.
Aylmer Fisher R. The arrangement of field experiments. Ministry of Agriculture and Fisheries. 1926. doi:10.23637/ROTHAMSTED.8V61Q
269.
Lakens D, Caldwell A. Simulation-Based Power Analysis for Factorial Analysis of Variance Designs. Advances in Methods and Practices in Psychological Science. 2021;4:251524592095150. doi:10.1177/2515245920951503
270.
Wasserstein RL, Lazar NA. The ASA Statement on p-Values: Context, Process, and Purpose. The American Statistician. 2016;70(2):129–133. doi:10.1080/00031305.2016.1154108
271.
Altman N, Krzywinski M. P values and the search for significance. Nature Methods. 2017;14(1):3–4. doi:10.1038/nmeth.4120
272.
Heinze G, Dunkler D. Five myths about variable selection. Transplant International. 2016;30(1):6–10. doi:10.1111/tri.12895
273.
Diedenhofen B, Musch J. cocor: A Comprehensive Solution for the Statistical Comparison of Correlations. PLOS ONE. 2015;10:e0121945. doi:10.1371/journal.pone.0121945
274.
McHugh ML. The Chi-square test of independence. Biochemia Medica. 2013:143–149. doi:10.11613/bm.2013.018
275.
Kim HY. Statistical notes for clinical researchers: Chi-squared test and Fisher’s exact test. Restorative Dentistry & Endodontics. 2017;42(2):152. doi:10.5395/rde.2017.42.2.152
276.
Khamis H. Measures of Association: How to Choose? Journal of Diagnostic Medical Sonography. 2008;24(3):155–162. doi:10.1177/8756479308317006
277.
Allison JS, Santana L, (Jaco) Visagie IJH. A primer on simple measures of association taught at undergraduate level. Teaching Statistics. 2022;44(3):96–103. doi:10.1111/test.12307
278.
Dahlke JA, Wiernik BM. psychmeta: An R Package for Psychometric Meta-Analysis. Applied Psychological Measurement. 2018;43(3):415–416. doi:10.1177/0146621618795933
279.
Anscombe FJ. Graphs in Statistical Analysis. The American Statistician. 1973;27(1):17–21. doi:10.1080/00031305.1973.10478966
280.
Northrop PJ. anscombiser: Create Datasets with Identical Summary Statistics.; 2022. https://CRAN.R-project.org/package=anscombiser.
281.
Makowski D, Wiernik BM, Patil I, Lüdecke D, Ben-Shachar MS. correlation: Methods for Correlation Analysis.; 2022. https://CRAN.R-project.org/package=correlation.
282.
Lüdecke D, Ben-Shachar MS, Patil I, et al. easystats: Framework for Easy Statistical Modeling, Visualization, and Reporting.; 2022. https://easystats.github.io/easystats/.
283.
Kim JH. Multicollinearity and misleading statistical results. Korean Journal of Anesthesiology. 2019;72(6):558–569. doi:10.4097/kja.19087
284.
Schloerke B, Cook D, Larmarange J, et al. GGally: Extension to ’ggplot2’.; 2024. doi:10.32614/CRAN.package.GGally
285.
Arel-Bundock V. modelsummary: Data and Model Summaries in R. Journal of Statistical Software. 2022;103. doi:10.18637/jss.v103.i01
286.
Hidalgo B, Goodman M. Multivariate or Multivariable Regression? American Journal of Public Health. 2013;103(1):39–40. doi:10.2105/ajph.2012.300897
287.
Suits DB. Use of Dummy Variables in Regression Equations. Journal of the American Statistical Association. 1957;52(280):548–551. doi:10.1080/01621459.1957.10501412
288.
Healy MJ. Statistics from the inside. 16. Multiple regression (2). Archives of Disease in Childhood. 1995;73(3):270–274. doi:10.1136/adc.73.3.270
289.
Kaplan J. fastDummies: Fast Creation of Dummy (Binary) Columns and Rows from Categorical Variables.; 2023. https://CRAN.R-project.org/package=fastDummies.
290.
Sun GW, Shook TL, Kay GL. Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. Journal of Clinical Epidemiology. 1996;49(8):907–916. doi:10.1016/0895-4356(96)00025-x
291.
Fox J, Weisberg S. An R Companion to Applied Regression. Sage Publications, Inc.; 2019. https://www.john-fox.ca/Companion/.
292.
DALES LG, URY HK. An Improper Use of Statistical Significance Testing in Studying Covariables. International Journal of Epidemiology. 1978;7(4):373–376. doi:10.1093/ije/7.4.373
293.
Greenland S. Modeling and variable selection in epidemiologic analysis. American Journal of Public Health. 1989;79(3):340–349. doi:10.2105/ajph.79.3.340
294.
Box GEP. Science and Statistics. Journal of the American Statistical Association. 1976;71(356):791–799. doi:10.1080/01621459.1976.10480949
295.
Anderson D, Heiss A, Sumners J. equatiomatic: Transform Models into LaTeX Equations.; 2024. https://CRAN.R-project.org/package=equatiomatic.
296.
Spedicato GA. Discrete Time Markov Chains with R. The R Journal. 2017;9(2):84–104. doi:10.32614/RJ-2017-036
297.
Lüdecke D, Ben-Shachar MS, Patil I, Waggoner P, Makowski D. performance: An R Package for Assessment, Comparison and Testing of Statistical Models. Journal of Open Source Software. 2021;6:3139. doi:10.21105/joss.03139
298.
Henderson T. correctR: Corrected Test Statistics for Comparing Machine Learning Models on Correlated Samples.; 2025. https://CRAN.R-project.org/package=correctR.
299.
Lüdecke D. ggeffects: Tidy Data Frames of Marginal Effects from Regression Models. Journal of Open Source Software. 2018;3:772. doi:10.21105/joss.00772
300.
AALEN OO, FRIGESSI A. What can Statistics Contribute to a Causal Understanding? Scandinavian Journal of Statistics. 2007;34(1):155–168. doi:10.1111/j.1467-9469.2006.00549.x
301.
Vickers AJ, Assel M, Dunn RL, et al. Guidelines for Reporting Observational Research in Urology: The Importance of Clear Reference to Causality. European Urology. 2023;84(2):147–151. doi:10.1016/j.eururo.2023.04.027
302.
Hill AB. The Environment and Disease: Association or Causation? Proceedings of the Royal Society of Medicine. 1965;58(5):295–300. doi:10.1177/003591576505800503
303.
Rothman KJ, Greenland S. H ill’s Criteria for Causality. Encyclopedia of Biostatistics. fevereiro 2005. doi:10.1002/0470011815.b2a03072
304.
Shimonovich M, Pearce A, Thomson H, Keyes K, Katikireddi SV. Assessing causality in epidemiology: revisiting Bradford Hill to incorporate developments in causal thinking. European Journal of Epidemiology. 2020;36(9):873–887. doi:10.1007/s10654-020-00703-7
305.
Textor J, Zander B van der, Gilthorpe MS, Liskiewicz M, Ellison GT. Robust causal inference using directed acyclic graphs: the R package dagitty. International Journal of Epidemiology. 2016;45:1887–1894. doi:10.1093/ije/dyw341
306.
Barrett M. ggdag: Analyze and Create Elegant Directed Acyclic Graphs.; 2024. https://CRAN.R-project.org/package=ggdag.
307.
Andaur Navarro CL, Damen JAA, Smeden M van, et al. Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models. Journal of Clinical Epidemiology. 2023;154:8–22. doi:10.1016/j.jclinepi.2022.11.015
308.
Carriero A, Luijken K, Hond A de, Moons KGM, Calster B van, Smeden M van. The Harms of Class Imbalance Corrections for Machine Learning Based Prediction Models: A Simulation Study. Statistics in Medicine. 2025;44(3-4). doi:10.1002/sim.10320
309.
McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics. 1943;5(4):115–133. doi:10.1007/bf02478259
310.
Rosenblatt F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review. 1958;65(6):386–408. doi:10.1037/h0042519
311.
Rosenblatt F. Perceptron Simulation Experiments. Proceedings of the IRE. 1960;48(3):301–309. doi:10.1109/jrproc.1960.287598
312.
Falbel D, Luraschi J. torch: Tensors and Neural Networks with ’GPU’ Acceleration.; 2025. doi:10.32614/CRAN.package.torch
313.
Ushey K, Allaire J, Tang Y. reticulate: Interface to ’Python’.; 2025. doi:10.32614/CRAN.package.reticulate
314.
Selivanov D, Bickel M, Wang Q. text2vec: Modern Text Mining Framework for R.; 2023. doi:10.32614/CRAN.package.text2vec
315.
Heckman MG, Davis JM, Crowson CS. Post Hoc Power Calculations: An Inappropriate Method for Interpreting the Findings of a Research Study. The Journal of Rheumatology. 2022;49(8):867–870. doi:10.3899/jrheum.211115
316.
Iddi S, Donohue MC. Power and Sample Size for Longitudinal Models in R – The longpower Package and Shiny App. The R Journal. 2022;14:264–282.
317.
Rodríguez del Águila M, González-Ramírez A. Sample size calculation. Allergologia et Immunopathologia. 2014;42(5):485–492. doi:10.1016/j.aller.2013.03.008
318.
Bacchetti P. Ethics and Sample Size. American Journal of Epidemiology. 2005;161(2):105–110. doi:10.1093/aje/kwi014
319.
Ying X, Robinson KA, Ehrhardt S. Re-evaluating the role of pilot trials in informing effect and sample size estimates for full-scale trials: a meta-epidemiological study. BMJ Evidence-Based Medicine. 2023;28(6):383–391. doi:10.1136/bmjebm-2023-112358
320.
Andrade C. Sample Size and its Importance in Research. Indian Journal of Psychological Medicine. 2020;42(1):102–103. doi:10.4103/ijpsym.ijpsym_504_19
321.
Gamble C, Krishan A, Stocken D, et al. Guidelines for the Content of Statistical Analysis Plans in Clinical Trials. JAMA. 2017;318(23):2337. doi:10.1001/jama.2017.18556
322.
Bland JM, Altman DG. Statistics notes: Matching. BMJ. 1994;309(6962):1128–1128. doi:10.1136/bmj.309.6962.1128
323.
Grant MJ, Booth A. A typology of reviews: an analysis of 14 review types and associated methodologies. Health Information & Libraries Journal. 2009;26(2):91–108. doi:10.1111/j.1471-1842.2009.00848.x
324.
Sut N. Study Designs in Medicine. Balkan Medical Journal. 2015;31(4):273–277. doi:10.5152/balkanmedj.2014.1408
325.
Souza AC de, Alexandre NMC, Guirardello E de B, Souza AC de, Alexandre NMC, Guirardello E de B. Propriedades psicométricas na avaliação de instrumentos: avaliação da confiabilidade e da validade. Epidemiologia e Serviços de Saúde. 2017;26(3):649–659. doi:10.5123/s1679-49742017000300022
326.
Reeves BC, Wells GA, Waddington H. Quasi-experimental study designs seriespaper 5: a checklist for classifying studies evaluating the effects on health interventionsa taxonomy without labels. Journal of Clinical Epidemiology. 2017;89:30–42. doi:10.1016/j.jclinepi.2017.02.016
327.
Echevarría-Guanilo ME, Gonçalves N, Romanoski PJ. PSYCHOMETRIC PROPERTIES OF MEASUREMENT INSTRUMENTS: CONCEPTUAL BASIS AND EVALUATION METHODS – PART II. Texto & Contexto – Enfermagem. 2019;28. doi:10.1590/1980-265x-tce-2017-0311
328.
Chassé M, Fergusson DA. Diagnostic Accuracy Studies. Seminars in Nuclear Medicine. 2019;49(2):87–93. doi:10.1053/j.semnuclmed.2018.11.005
329.
Chidambaram AG, Josephson M. Clinical research study designs: The essentials. PEDIATRIC INVESTIGATION. 2019;3(4):245–252. doi:10.1002/ped4.12166
330.
Erdemir A, Mulugeta L, Ku JP, et al. Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective. Journal of Translational Medicine. 2020;18(1). doi:10.1186/s12967-020-02540-4
331.
Yang B, Olsen M, Vali Y, et al. Study designs for comparative diagnostic test accuracy: A methodological review and classification scheme. Journal of Clinical Epidemiology. 2021;138:128–138. doi:10.1016/j.jclinepi.2021.04.013
332.
Chipman H, Bingham D. Let’s practice what we preach: Planning and interpreting simulation studies with design and analysis of experiments. Canadian Journal of Statistics. 2022;50(4):1228–1249. doi:10.1002/cjs.11719
333.
Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research. 2021;133:285–296. doi:10.1016/j.jbusres.2021.04.070
334.
Lim WM, Kumar S. Guidelines for interpreting the results of bibliometric analysis: A sensemaking approach. Global Business and Organizational Excellence. agosto 2023. doi:10.1002/joe.22229
335.
Elm E von, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies. Annals of Internal Medicine. 2007;147(8):573. doi:10.7326/0003-4819-147-8-200710160-00010
336.
Rosseel Y. lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software. 2012;48. doi:10.18637/jss.v048.i02
337.
Contributors semTools. semTools: Useful tools for structural equation modeling.; 2016. https://CRAN.R-project.org/package=semTools.
338.
William Revelle. psych: Procedures for Psychological, Psychometric, and Personality Research.; 2023. https://CRAN.R-project.org/package=psych.
339.
Findley MG, Kikuta K, Denly M. External Validity. Annual Review of Political Science. 2021;24(1):365–393. doi:10.1146/annurev-polisci-041719-102556
340.
Scott WA. Reliability of Content Analysis: The Case of Nominal Scale Coding. Public Opinion Quarterly. 1955;19(3):321. doi:10.1086/266577
341.
Cohen J. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement. 1960;20(1):37–46. doi:10.1177/001316446002000104
342.
Mathews I, Pearson K. I. Mathematical contributions to the theory of evolution. VII. On the correlation of characters not quantitatively measurable. Philosophical Transactions of the Royal Society of London Series A, Containing Papers of a Mathematical or Physical Character. 1901;195(262-273):1–47. doi:10.1098/rsta.1900.0022
343.
Banerjee M, Capozzoli M, McSweeney L, Sinha D. Beyond kappa: A review of interrater agreement measures. Canadian Journal of Statistics. 1999;27(1):3–23. doi:10.2307/3315487
344.
Lehnert B. BlandAltmanLeh: Plots (Slightly Extended) Bland-Altman Plots.; 2015. https://CRAN.R-project.org/package=BlandAltmanLeh.
345.
Gagnier JJ, Lai J, Mokkink LB, Terwee CB. COSMIN reporting guideline for studies on measurement properties of patient-reported outcome measures. Quality of Life Research. 2021;30(8):2197–2218. doi:10.1007/s11136-021-02822-4
346.
Streiner DL, Kottner J. Recommendations for reporting the results of studies of instrument and scale development and testing. Journal of Advanced Nursing. 2014;70(9):1970–1979. doi:10.1111/jan.12402
347.
Kottner J, Audigé L, Brorson S, et al. Guidelines for Reporting Reliability and Agreement Studies (GRRAS) were proposed. Journal of Clinical Epidemiology. 2011;64(1):96–106. doi:10.1016/j.jclinepi.2010.03.002
348.
Steckelberg A, Balgenorth A, Berger J, Mühlhauser I. Explaining computation of predictive values: 2 × 2 table versus frequency tree. A randomized controlled trial [ISRCTN74278823]. BMC Medical Education. 2004;4(1). doi:10.1186/1472-6920-4-13
349.
Greenhalgh T. How to read a paper: Papers that report diagnostic or screening tests. BMJ. 1997;315(7107):540–543. doi:10.1136/bmj.315.7107.540
350.
Neth H, Gaisbauer F, Gradwohl N, Gaissmaier W. riskyr: Rendering Risk Literacy more Transparent.; 2022. https://CRAN.R-project.org/package=riskyr.
351.
Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PMM. The diagnostic odds ratio: a single indicator of test performance. Journal of Clinical Epidemiology. 2003;56(11):1129–1135. doi:10.1016/s0895-4356(03)00177-x
352.
Kuhn, Max. Building Predictive Models in R Using the caret Package. Journal of Statistical Software. 2008;28(5):1–26. doi:10.18637/jss.v028.i05
353.
Xu J, Zhang Y, Miao D. Three-way confusion matrix for classification: A measure driven view. Information Sciences. 2020;507:772–794. doi:10.1016/j.ins.2019.06.064
354.
He Z, Zhang Q, Song M, Tan X, Wang W. Four overlooked errors in ROC analysis: how to prevent and avoid. BMJ Evidence-Based Medicine. 2024;30(3):208–211. doi:10.1136/bmjebm-2024-113078
355.
Park SH, Goo JM, Jo CH. Receiver Operating Characteristic (ROC) Curve: Practical Review for Radiologists. Korean Journal of Radiology. 2004;5(1):11. doi:10.3348/kjr.2004.5.1.11
356.
Park SH, Goo JM, Jo CH. UniODA vs ROC Analysis: Computing the “optimal” cut-point. Optimal Data Analysis. 2014;3(14):117–120. https://odajournal.com/wp-content/uploads/2019/01/v3a29.pdf.
357.
Hond AAH de, Steyerberg EW, Calster B van. Interpreting area under the receiver operating characteristic curve. The Lancet Digital Health. 2022;4(12):e853–e855. doi:10.1016/s2589-7500(22)00188-1
358.
Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77. doi:10.1186/1471-2105-12-77
359.
Ferreira ADS, Meziat-Filho N, Ferreira APA. Double threshold receiver operating characteristic plot for three-modal continuous predictors. Computational Statistics. 2021;36(3):2231–2245. doi:10.1007/s00180-021-01080-9
360.
Phillips B, Stewart LA, Sutton AJ. Cross hairs plots for diagnostic meta-analysis. Research Synthesis Methods. 2010;1(3-4):308–315. doi:10.1002/jrsm.26
361.
Sousa-Pinto PD with contributions from B. mada: Meta-Analysis of Diagnostic Accuracy.; 2022. https://CRAN.R-project.org/package=mada.
362.
Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ. outubro 2015:h5527. doi:10.1136/bmj.h5527
363.
Reeves BC, Gaus W. Guidelines for Reporting Non-Randomised Studies. Complementary Medicine Research. 2004;11(1):46–52. doi:10.1159/000080576
364.
Bland JM, Altman DG. Comparisons within randomised groups can be very misleading. BMJ. 2011;342(may06 2):d561–d561. doi:10.1136/bmj.d561
365.
Bruce CL, Juszczak E, Ogollah R, Partlett C, Montgomery A. A systematic review of randomisation method use in RCTs and association of trial design characteristics with method selection. BMC Medical Research Methodology. 2022;22(1). doi:10.1186/s12874-022-01786-4
366.
Vickers AJ, Altman DG. Statistics Notes: Analysing controlled trials with baseline and follow up measurements. BMJ. 2001;323(7321):1123–1124. doi:10.1136/bmj.323.7321.1123
367.
O Connell NS, Dai L, Jiang Y, et al. Methods for Analysis of Pre-Post Data in Clinical Research: A Comparison of Five Common Methods. Journal of Biometrics & Biostatistics. 2017;08(01). doi:10.4172/2155-6180.1000334
368.
Laird N. Further Comparative Analyses of Pretest-Posttest Research Designs. The American Statistician. 1983;37(4a):329–330. doi:10.1080/00031305.1983.10483133
369.
Cnaan A, Laird NM, Slasor P. Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. Statistics in Medicine. 1997;16(20):2349–2380. doi:10.1002/(sici)1097-0258(19971030)16:20<2349::aid-sim667>3.0.co;2-e
370.
Mallinckrodt CH, Lane PW, Schnell D, Peng Y, Mancuso JP. Recommendations for the Primary Analysis of Continuous Endpoints in Longitudinal Clinical Trials. Drug Information Journal. 2008;42(4):303–319. doi:10.1177/009286150804200402
371.
Assmann SF, Pocock SJ, Enos LE, Kasten LE. Subgroup analysis and other (mis)uses of baseline data in clinical trials. The Lancet. 2000;355(9209):1064–1069. doi:10.1016/s0140-6736(00)02039-0
372.
Stang A, Baethge C. Imbalance <em>p</em> values for baseline covariates in randomized controlled trials: a last resort for the use of <em>p</em> values? A pro and contra debate. Clinical Epidemiology. 2018;Volume 10:531–535. doi:10.2147/clep.s161508
373.
Bolzern JE, Mitchell A, Torgerson DJ. Baseline testing in cluster randomised controlled trials: should this be done? BMC Medical Research Methodology. 2019;19(1). doi:10.1186/s12874-019-0750-8
374.
Roberts C, Torgerson DJ. Understanding controlled trials: Baseline imbalance in randomised controlled trials. BMJ. 1999;319(7203):185–185. doi:10.1136/bmj.319.7203.185
375.
Gruijters SLK. Baseline comparisons and covariate fishing: Bad statistical habits we should have broken yesterday. julho 2020. http://dx.doi.org/10.31234/osf.io/qftwg.
376.
Vickers AJ. The use of percentage change from baseline as an outcome in a controlled trial is statistically inefficient: a simulation study. BMC Medical Research Methodology. 2001;1(1). doi:10.1186/1471-2288-1-6
377.
Brookes ST, Whitely E, Egger M, Smith GD, Mulheran PA, Peters TJ. Subgroup analyses in randomized trials: risks of subgroup-specific analyses; Journal of Clinical Epidemiology. 2004;57(3):229–236. doi:10.1016/j.jclinepi.2003.08.009
378.
Matthews JNS, Altman DG. Statistics Notes: Interaction 2: compare effect sizes not P values. BMJ. 1996;313(7060):808–808. doi:10.1136/bmj.313.7060.808
379.
Altman DG. Statistics Notes: Interaction revisited: the difference between two estimates. BMJ. 2003;326(7382):219–219. doi:10.1136/bmj.326.7382.219
380.
Hauck WW, Anderson S, Marcus SM. Should We Adjust for Covariates in Nonlinear Regression Analyses of Randomized Trials? Controlled Clinical Trials. 1998;19(3):249–256. doi:10.1016/s0197-2456(97)00147-5
381.
Kahan BC, Jairath V, Doré CJ, Morris TP. The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies. Trials. 2014;15(1). doi:10.1186/1745-6215-15-139
382.
Cao Y, Allore H, Vander Wyk B, Gutman R. Review and evaluation of imputation methods for multivariate longitudinal data with mixed-type incomplete variables. Statistics in Medicine. outubro 2022. doi:10.1002/sim.9592
383.
Schulz KF. CONSORT 2010 Statement: Updated Guidelines for Reporting Parallel Group Randomized Trials. Annals of Internal Medicine. 2010;152(11):726. doi:10.7326/0003-4819-152-11-201006010-00232
384.
Dayim A. consort: Create Consort Diagram.; 2023. https://CRAN.R-project.org/package=consort.
385.
Fantini D. easyPubMed: Search and Retrieve Scientific Publication Records from PubMed.; 2019. doi:10.32614/CRAN.package.easyPubMed
386.
Chamberlain S, Zhu H, Jahn N, Boettiger C, Ram K. rcrossref: Client for Various ’CrossRef’ ’APIs’.; 2022. doi:10.32614/CRAN.package.rcrossref
387.
Jahn N. roadoi: Find Free Versions of Scholarly Publications via Unpaywall.; 2024. doi:10.32614/CRAN.package.roadoi
388.
Moons KGM, Groot JAH de, Bouwmeester W, et al. Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist. PLoS Medicine. 2014;11(10):e1001744. doi:10.1371/journal.pmed.1001744
389.
Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. A basic introduction to fixed-effect and random-effects models for meta-analysis. Research Synthesis Methods. 2010;1(2):97–111. doi:10.1002/jrsm.12
390.
Viechtbauer W. Conducting meta-analyses in R with the metafor package. Vol 36.; 2010. doi:10.18637/jss.v036.i03
391.
Hozo SP, Djulbegovic B, Hozo I. Estimating the mean and variance from the median, range, and the size of a sample. BMC Medical Research Methodology. 2005;5(1). doi:10.1186/1471-2288-5-13
392.
Wan X, Wang W, Liu J, Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Medical Research Methodology. 2014;14(1). doi:10.1186/1471-2288-14-135
393.
Borenstein M. In a meta-analysis, the I-squared statistic does not tell us how much the effect size varies. Journal of Clinical Epidemiology. outubro 2022. doi:10.1016/j.jclinepi.2022.10.003
394.
Rücker G, Schwarzer G, Carpenter JR, Schumacher M. Undue reliance on I 2 in assessing heterogeneity may mislead. BMC Medical Research Methodology. 2008;8(1). doi:10.1186/1471-2288-8-79
395.
Grooth HJ de, Parienti JJ. Heterogeneity between studies can be explained more reliably with individual patient data. Intensive Care Medicine. julho 2023. doi:10.1007/s00134-023-07163-z
396.
Dettori JR, Norvell DC, Chapman JR. Seeing the Forest by Looking at the Trees: How to Interpret a Meta-Analysis Forest Plot. Global Spine Journal. 2021;11(4):614–616. doi:10.1177/21925682211003889
397.
Song, Eastwood, Gilbody, Duley, Sutton. Publication and related biases. Health Technology Assessment. 2000;4(10). doi:10.3310/hta4100
398.
Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–634. doi:10.1136/bmj.315.7109.629
399.
Peters JL. Comparison of Two Methods to Detect Publication Bias in Meta-analysis. JAMA. 2006;295(6):676. doi:10.1001/jama.295.6.676
400.
Sterne JAC, Sutton AJ, Ioannidis JPA, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 2011;343(jul22 1):d4002–d4002. doi:10.1136/bmj.d4002
401.
Duval S, Tweedie R. Trim and Fill: A Simple Funnel-PlotBased Method of Testing and Adjusting for Publication Bias in Meta-Analysis. Biometrics. 2000;56(2):455–463. doi:10.1111/j.0006-341x.2000.00455.x
402.
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. PLOS Medicine. 2021;18(3):e1003583. doi:10.1371/journal.pmed.1003583
403.
Lajeunesse MJ. Facilitating systematic reviews, data extraction, and meta-analysis with the metagear package for R. Methods in Ecology and Evolution. 2016;7(3):323–330. doi:10.1111/2041-210X.12472
404.
Moher D, Shamseer L, Clarke M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews. 2015;4(1). doi:10.1186/2046-4053-4-1
405.
Haddaway NR, Page MJ, Pritchard CC, McGuinness LA. PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis. Campbell Systematic Reviews. 2022;18:e1230. doi:10.1002/cl2.1230
406.
Silge J, Robinson D. tidytext: Text Mining and Analysis Using Tidy Data Principles in R. The Journal of Open Source Software. 2016;1. doi:10.21105/joss.00037
407.
Makowski D, Lüdecke D, Patil I, Thériault R, Ben-Shachar MS, Wiernik BM. Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption.; 2023. https://easystats.github.io/report/.
408.
Wallisch C, Bach P, Hafermann L, et al. Review of guidance papers on regression modeling in statistical series of medical journals. Mathes T, org. PLOS ONE. 2022;17(1):e0262918. doi:10.1371/journal.pone.0262918
409.
Lynggaard H, Bell J, Lösch C, et al. Principles and recommendations for incorporating estimands into clinical study protocol templates. Trials. 2022;23(1). doi:10.1186/s13063-022-06515-2
410.
Althouse AD, Below JE, Claggett BL, et al. Recommendations for Statistical Reporting in Cardiovascular Medicine: A Special Report From the American Heart Association. Circulation. 2021;144(4). doi:10.1161/circulationaha.121.055393
411.
Lee KJ, Tilling KM, Cornish RP, et al. Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework. Journal of Clinical Epidemiology. 2021;134:79–88. doi:10.1016/j.jclinepi.2021.01.008
412.
Vickers AJ, Assel MJ, Sjoberg DD, et al. Guidelines for Reporting of Figures and Tables for Clinical Research in Urology. Urology. 2020;142:1–13. doi:10.1016/j.urology.2020.05.002
413.
Assel M, Sjoberg D, Elders A, et al. Guidelines for Reporting of Statistics for Clinical Research in Urology. Journal of Urology. 2019;201(3):595–604. doi:10.1097/ju.0000000000000001
414.
Lang TA, Altman DG. Basic statistical reporting for articles published in Biomedical Journals: The Statistical Analyses and Methods in the Published Literature or the SAMPL Guidelines. International Journal of Nursing Studies. 2015;52(1):5–9. doi:10.1016/j.ijnurstu.2014.09.006
415.
Weissgerber TL, Milic NM, Winham SJ, Garovic VD. Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLOS Biology. 2015;13(4):e1002128. doi:10.1371/journal.pbio.1002128
416.
Sauerbrei W, Abrahamowicz M, Altman DG, Cessie S, Carpenter J. STRengthening Analytical Thinking for Observational Studies: the STRATOS initiative. Statistics in Medicine. 2014;33(30):5413–5432. doi:10.1002/sim.6265
417.
Groves T. Research methods and reporting. BMJ. 2008;337(oct22 1):a2201–a2201. doi:10.1136/bmj.a2201
418.
Stratton IM, Neil A. How to ensure your paper is rejected by the statistical reviewer. Diabetic Medicine. 2005;22(4):371–373. doi:10.1111/j.1464-5491.2004.01443.x
419.
Mansournia MA, Collins GS, Nielsen RO, et al. A CHecklist for statistical Assessment of Medical Papers (the CHAMP statement): explanation and elaboration. British Journal of Sports Medicine. 2021;55(18):1009–1017. doi:10.1136/bjsports-2020-103652
420.
Gil-Sierra MD, Fénix-Caballero S, Abdel kader-Martin L, et al. Checklist for clinical applicability of subgroup analysis. Journal of Clinical Pharmacy and Therapeutics. 2019;45(3):530–538. doi:10.1111/jcpt.13102
421.
Lee H, Cashin AG, Lamb SE, et al. A Guideline for Reporting Mediation Analyses of Randomized Trials and Observational Studies. JAMA. 2021;326(11):1045. doi:10.1001/jama.2021.14075