Referências

1.
Grami A. Discrete probability. In: 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. Accessed November 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.
Banerjee A, Chaudhury S. Statistics without tears: Populations and samples. Industrial Psychiatry Journal. 2010;19(1):60. doi:10.4103/0972-6748.77642
14.
Bland JM, Altman DG. Statistics Notes: Bootstrap resampling methods. BMJ. 2015;350(jun02 13):h2622-h2622. doi:10.1136/bmj.h2622
15.
Sahai H, Misra S. Definitions of sample variance: Some teaching problems to be overcome. The Statistician. 1992;41(1):55. doi:10.2307/2348636
16.
Altman DG, Bland JM. Statistics Notes: Units of analysis. BMJ. 1997;314(7098):1874-1874. doi:10.1136/bmj.314.7098.1874
17.
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
18.
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
19.
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
20.
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
21.
Resnik DB, Shamoo AE. Reproducibility and Research Integrity. Accountability in Research. 2016;24(2):116-123. doi:10.1080/08989621.2016.1257387
22.
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
23.
Mair P. Thou shalt be reproducible! A technology perspective. Frontiers in Psychology. 2016;7. doi:10.3389/fpsyg.2016.01079
24.
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
25.
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
26.
Berkson J. Limitations of the application of fourfold table analysis to hospital data. Biometrics Bulletin. 1946;2(3):47. doi:10.2307/3002000
27.
Ellsberg D. Risk, ambiguity, and the savage axioms. The Quarterly Journal of Economics. 1961;75(4):643. doi:10.2307/1884324
28.
Freedman DA, Freedman DA. A Note on Screening Regression Equations. The American Statistician. 1983;37(2):152-155. doi:10.1080/00031305.1983.10482729
29.
Freedman LS, Pee D. Return to a note on screening regression equations. The American Statistician. 1989;43(4):279. doi:10.2307/2685389
30.
Hand DJ. On Comparing Two Treatments. The American Statistician. 1992;46(3):190-192. doi:10.1080/00031305.1992.10475881
31.
LINDLEY DV. A STATISTICAL PARADOX. Biometrika. 1957;44(1-2):187-192. doi:10.1093/biomet/44.1-2.187
32.
Lord FM. A paradox in the interpretation of group comparisons. Psychological Bulletin. 1967;68(5):304-305. doi:10.1037/h0025105
33.
Lord FM. Statistical adjustments when comparing preexisting groups. Psychological Bulletin. 1969;72(5):336-337. doi:10.1037/h0028108
34.
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
35.
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
36.
Pearl J. Comment: Understanding Simpsons Paradox. The American Statistician. 2014;68(1):8-13. doi:10.1080/00031305.2014.876829
37.
Stein C. INADMISSIBILITY OF THE USUAL ESTIMATOR FOR THE MEAN OF a MULTIVARIATE NORMAL DISTRIBUTION. In: University of California Press; 1956:197-206. doi:10.1525/9780520313880-018
38.
De S, Sen A. The generalised Gamow-Stern problem. The Mathematical Gazette. 1996;80(488):345-348. doi:10.2307/3619568
39.
Feld SL. Why Your Friends Have More Friends Than You Do. American Journal of Sociology. 1991;96(6):1464-1477. doi:10.1086/229693
40.
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
41.
Aguinis H, Pierce CA, Culpepper SA. Scale Coarseness as a Methodological Artifact. Organizational Research Methods. 2008;12(4):623-652. doi:10.1177/1094428108318065
42.
Bryer J, Speerschneider K. Likert: Analysis and Visualization Likert Items.; 2016. https://CRAN.R-project.org/package=likert.
43.
Ferris TLJ. A new definition of measurement. Measurement. 2004;36(1):101-109. doi:10.1016/j.measurement.2004.03.001
44.
R Core Team. R: A Language and Environment for Statistical Computing.; 2023. https://www.R-project.org/.
45.
Healy MJR, Goldstein H. Regression to the mean. Annals of Human Biology. 1978;5(3):277-280. doi:10.1080/03014467800002891
46.
Altman DG, Bland JM. Measurement in medicine: The analysis of method comparison studies. The Statistician. 1983;32(3):307. doi:10.2307/2987937
47.
Olson K. What Are Data? Qualitative Health Research. 2021;31(9):1567-1569. doi:10.1177/10497323211015960
48.
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
49.
Vetter TR. Fundamentals of Research Data and Variables. Anesthesia & Analgesia. 2017;125(4):1375-1380. doi:10.1213/ane.0000000000002370
50.
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
51.
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
52.
R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2023. https://www.R-project.org/.
53.
Pebesma E, Mailund T, Hiebert J. Measurement units in r. 2016;8. doi:10.32614/RJ-2016-061
54.
Firke S. Janitor: Simple Tools for Examining and Cleaning Dirty Data.; 2023. https://CRAN.R-project.org/package=janitor.
55.
Harrell Jr FE. Hmisc: Harrell Miscellaneous.; 2023. https://CRAN.R-project.org/package=Hmisc.
56.
Altman DG, Bland JM. Missing data. BMJ. 2007;334(7590):424-424. doi:10.1136/bmj.38977.682025.2c
57.
Heymans MW, Twisk JWR. Handling missing data in clinical research. Journal of Clinical Epidemiology. September 2022. doi:10.1016/j.jclinepi.2022.08.016
58.
Carpenter JR, Smuk M. Missing data: A statistical framework for practice. Biometrical Journal. 2021;63(5):915-947. doi:10.1002/bimj.202000196
59.
Yanagida T. Misty: Miscellaneous Functions.; 2023. https://CRAN.R-project.org/package=misty.
60.
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
61.
Tierney N, Cook D. Expanding tidy data principles to facilitate missing data exploration, visualization and assessment of imputations. 2023;105. doi:10.18637/jss.v105.i07
62.
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
63.
Austin PC, Buuren S van. Logistic regression vs. predictive mean matching for imputing binary covariates. Statistical Methods in Medical Research. September 2023. doi:10.1177/09622802231198795
64.
Buuren S van, Groothuis-Oudshoorn K. Mice: Multivariate imputation by chained equations in r. 2011;45:1-67. doi:10.18637/jss.v045.i03
65.
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
66.
Little RJA. Missing-Data Adjustments in Large Surveys. Journal of Business & Economic Statistics. 1988;6(3):287-296. doi:10.1080/07350015.1988.10509663
67.
Robitzsch A, Grund S. Miceadds: Some Additional Multiple Imputation Functions, Especially for Mice.; 2023. https://CRAN.R-project.org/package=miceadds.
68.
FitzJohn R. Ids: Generate Random Identifiers.; 2017. https://CRAN.R-project.org/package=ids.
69.
Brown C. Hash: Full Featured Implementation of Hash Tables/Associative Arrays/Dictionaries.; 2023. https://CRAN.R-project.org/package=hash.
70.
Hendricks P. Anonymizer: Anonymize data containing personally identifiable information. 2023. https://github.com/paulhendricks/anonymizer.
71.
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.
72.
Nowok B, Raab GM, Dibben C. Synthpop: Bespoke creation of synthetic data in r. 2016;74. doi:10.18637/jss.v074.i11
73.
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
74.
Hammill D. DataEditR: An Interactive Editor for Viewing, Entering, Filtering & Editing Data.; 2022. https://CRAN.R-project.org/package=DataEditR.
75.
Broman KW, Woo KH. Data Organization in Spreadsheets. The American Statistician. 2018;72(1):2-10. doi:10.1080/00031305.2017.1375989
76.
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
77.
Dowle M, Srinivasan A. Data.table: Extension of ‘Data.frame‘.; 2023. https://CRAN.R-project.org/package=data.table.
78.
Altman DG, Bland JM. Statistics notes Variables and parameters. BMJ. 1999;318(7199):1667-1667. doi:10.1136/bmj.318.7199.1667
79.
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
80.
Dettori JR, Norvell DC. The Anatomy of Data. Global Spine Journal. 2018;8(3):311-313. doi:10.1177/2192568217746998
81.
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
82.
Barkan H. Statistics in clinical research: Important considerations. Annals of Cardiac Anaesthesia. 2015;18(1):74. doi:10.4103/0971-9784.148325
83.
Bland JM, Altman DG. Statistics Notes: Transforming data. BMJ. 1996;312(7033):770-770. doi:10.1136/bmj.312.7033.770
84.
Fedorov V, Mannino F, Zhang R. Consequences of dichotomization. Pharmaceutical Statistics. 2009;8(1):50-61. doi:10.1002/pst.331
85.
Osborne J. Improving your data transformations: Applying the box-cox transformation. University of Massachusetts Amherst. 2010. doi:10.7275/QBPC-GK17
86.
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
87.
Venables WN, Ripley BD. Modern applied statistics with s. 2002. https://www.stats.ox.ac.uk/pub/MASS4/.
88.
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
89.
Altman DG, Royston P. The cost of dichotomising continuous variables. BMJ. 2006;332(7549):1080.1. doi:10.1136/bmj.332.7549.1080
90.
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
91.
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
92.
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
93.
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
94.
Barnier J, Briatte F, Larmarange J. Questionr: Functions to Make Surveys Processing Easier.; 2023. https://CRAN.R-project.org/package=questionr.
95.
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
96.
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
97.
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
98.
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
99.
Fleiss JL. Measuring nominal scale agreement among many raters. Psychological Bulletin. 1971;76(5):378-382. doi:10.1037/h0031619
100.
S M. Frequency distribution. Journal of Pharmacology and Pharmacotherapeutics. 2011;2(1):54-56. doi:10.4103/0976-500x.77120
101.
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
102.
SCOTT DW. On optimal and data-based histograms. Biometrika. 1979;66(3):605-610. doi:10.1093/biomet/66.3.605
103.
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
104.
R Core Team. R: A language and environment for statistical computing. 2024. https://www.R-project.org/.
105.
R Core Team. R: A language and environment for statistical computing. 2023. https://www.R-project.org/.
106.
Kay M. Ggdist: Visualizations of distributions and uncertainty in the grammar of graphics. 2024;30. doi:10.1109/TVCG.2023.3327195
107.
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
108.
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
109.
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
110.
Kanji G. 100 Statistical Tests.; 2006. doi:10.4135/9781849208499
111.
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
112.
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
113.
S. M. Measures of central tendency: The mean. Journal of Pharmacology and Pharmacotherapeutics. 2011;2(2):140-142. doi:10.4103/0976-500x.81920
114.
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
115.
Krzywinski M, Altman N. Error bars. Nature Methods. 2013;10(10):921-922. doi:10.1038/nmeth.2659
116.
Manikandan S. Measures of dispersion. Journal of Pharmacology and Pharmacotherapeutics. 2011;2(4):315-316. doi:10.4103/0976-500x.85931
117.
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
118.
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
119.
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
120.
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
121.
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
122.
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. Accessed April 11, 2025.
123.
Komsta L. Outliers: Tests for Outliers.; 2022. https://CRAN.R-project.org/package=outliers.
124.
Chatfield C. Exploratory data analysis. European Journal of Operational Research. 1986;23(1):5-13. doi:10.1016/0377-2217(86)90209-2
125.
Ferketich S, Verran J. Technical Notes. Western Journal of Nursing Research. 1986;8(4):464-466. doi:10.1177/019394598600800409
126.
Kerr NL. HARKing: Hypothesizing After the Results are Known. Personality and Social Psychology Review. 1998;2(3):196-217. doi:10.1207/s15327957pspr0203_4
127.
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
128.
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
129.
Krasser R. Explore: Simplifies Exploratory Data Analysis.; 2023. https://CRAN.R-project.org/package=explore.
130.
Petersen AH, Ekstrøm CT. dataMaid: Your assistant for documenting supervised data quality screening in r. 2019;90. doi:10.18637/jss.v090.i06
131.
Cui B. DataExplorer: Automate Data Exploration and Treatment.; 2020. https://CRAN.R-project.org/package=DataExplorer.
132.
Dayanand Ubrangala, R K, Prasad Kondapalli R, Putatunda S. SmartEDA: Summarize and Explore the Data.; 2022. https://CRAN.R-project.org/package=SmartEDA.
133.
Mock T. gtExtras: Extending Gt for Beautiful HTML Tables.; 2023. https://CRAN.R-project.org/package=gtExtras.
134.
Nijs V. Radiant: Business Analytics Using r and Shiny.; 2023. https://CRAN.R-project.org/package=radiant.
135.
Gerring J. Mere Description. British Journal of Political Science. 2012;42(4):721-746. doi:10.1017/s0007123412000130
136.
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
137.
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
138.
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
139.
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
140.
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
141.
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
142.
Gohel D, Skintzos P. Flextable: Functions for Tabular Reporting.; 2023. https://CRAN.R-project.org/package=flextable.
143.
Thériault R. Rempsyc: Convenience functions for psychology. 2023;8:5466. doi:10.21105/joss.05466
144.
Rich B. Table1: Tables of Descriptive Statistics in HTML.; 2023. https://CRAN.R-project.org/package=table1.
145.
Sjoberg DD, Whiting K, Curry M, Lavery JA, Larmarange J. Reproducible summary tables with the gtsummary package. 2021;13:570-580. doi:10.32614/RJ-2021-053
146.
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
147.
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
148.
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
149.
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
150.
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
151.
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
152.
Wickham H. ggplot2: Elegant graphics for data analysis. 2016. https://ggplot2.tidyverse.org.
153.
Sievert C. Interactive web-based data visualization with r, plotly, and shiny. 2020. https://plotly-r.com.
154.
Wei T, Simko V. R package corrplot: Visualization of a correlation matrix. 2021. https://github.com/taiyun/corrplot.
155.
Weissgerber TL, Winham SJ, Heinzen EP, et al. Reveal, Dont Conceal. Circulation. 2019;140(18):1506-1518. doi:10.1161/circulationaha.118.037777
156.
Xiao N. Ggsci: Scientific Journal and Sci-Fi Themed Color Palettes for Ggplot2.; 2023. https://CRAN.R-project.org/package=ggsci.
157.
Urbanek S, Johnson K. Tiff: Read and Write TIFF Images.; 2022. https://CRAN.R-project.org/package=tiff.
158.
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
159.
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
160.
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
161.
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
162.
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
163.
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
164.
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
165.
Champely S. Pwr: Basic Functions for Power Analysis.; 2020. https://CRAN.R-project.org/package=pwr.
166.
Iddi S, Donohue MC. Power and sample size for longitudinal models in r-the longpower package and shiny app. 2022;14:264-281.
167.
Lakens D, Caldwell A. Simulation-based power analysis for factorial analysis of variance designs. 2021;4:251524592095150. doi:10.1177/2515245920951503
168.
Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR, Olino TM. Tutorial: Power analyses for interaction effects in cross-sectional regressions. 2022. doi:10.31234/osf.io/5ptd7
169.
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
170.
Goodman SN. Aligning statistical and scientific reasoning. Science. 2016;352(6290):1180-1181. doi:10.1126/science.aaf5406
171.
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
172.
Weintraub PG. The Importance of Publishing Negative Results. Journal of Insect Science. 2016;16(1):109. doi:10.1093/jisesa/iew092
173.
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
174.
Gelman A, Carlin J. Beyond Power Calculations. Perspectives on Psychological Science. 2014;9(6):641-651. doi:10.1177/1745691614551642
175.
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
176.
Mair P, Wilcox R. Robust statistical methods in r using the WRS2 package. 2020;52. doi:10.3758/s13428-019-01246-w
177.
Mair P, Wilcox R, Indrajeet P. A Collection of Robust Statistical Methods.; 2025. https://CRAN.R-project.org/package=WRS2.
178.
Lüdecke D. Ggeffects: Tidy data frames of marginal effects from regression models. 2018;3:772. doi:10.21105/joss.00772
179.
Textor J, Zander B van der, Gilthorpe MS, Liskiewicz M, Ellison GT. Robust causal inference using directed acyclic graphs: The r package dagitty. 2016;45. doi:10.1093/ije/dyw341
180.
Barrett M. Ggdag: Analyze and Create Elegant Directed Acyclic Graphs.; 2024. https://CRAN.R-project.org/package=ggdag.
181.
Lüdecke D, Ben-Shachar MS, Patil I, Waggoner P, Makowski D. Performance: An r package for assessment, comparison and testing of statistical models. 2021;6:3139. doi:10.21105/joss.03139
182.
Kim HY. Statistical notes for clinical researchers: effect size. Restorative Dentistry & Endodontics. 2015;40(4):328. doi:10.5395/rde.2015.40.4.328
183.
Ben-Shachar MS, Lüdecke D, Makowski D. Effectsize: Estimation of effect size indices and standardized parameters. 2020;5:2815. doi:10.21105/joss.02815
184.
Bours MJL. Using mediators to understand effect modification and interaction. Journal of Clinical Epidemiology. September 2023. doi:10.1016/j.jclinepi.2023.09.005
185.
Altman DG, Matthews JNS. Statistics Notes: Interaction 1: heterogeneity of effects. BMJ. 1996;313(7055):486-486. doi:10.1136/bmj.313.7055.486
186.
Pinheiro J, Bates D, R Core Team. Nlme: Linear and Nonlinear Mixed Effects Models.; 2023. https://CRAN.R-project.org/package=nlme.
187.
Sabanes Bove D, Dedic J, Kelkhoff D, et al. Mmrm: Mixed Models for Repeated Measures.; 2022. https://CRAN.R-project.org/package=mmrm.
188.
Lenth RV. Emmeans: Estimated Marginal Means, Aka Least-Squares Means.; 2023. https://CRAN.R-project.org/package=emmeans.
189.
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
190.
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
191.
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
192.
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
193.
Aylmer Fisher R. The arrangement of field experiments. Ministry of Agriculture and Fisheries. 1926. doi:10.23637/ROTHAMSTED.8V61Q
194.
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
195.
Altman N, Krzywinski M. P values and the search for significance. Nature Methods. 2017;14(1):3-4. doi:10.1038/nmeth.4120
196.
Heinze G, Dunkler D. Five myths about variable selection. Transplant International. 2016;30(1):6-10. doi:10.1111/tri.12895
197.
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
198.
Introduction to r and RStudio. Practical Machine Learning in R. April 2020:25-52. doi:10.1002/9781119591542.ch2
199.
R Core Team. The comprehensive r archive network. 2021. https://cran.r-project.org.
200.
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
201.
Aden-Buie G, Schloerke B, Allaire J, Rossell Hayes A. Learnr: Interactive Tutorials for r.; 2023. https://CRAN.R-project.org/package=learnr.
202.
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
203.
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
204.
Selker R, Love J, Dropmann D. Jmv: The Jamovi Analyses.; 2023. https://CRAN.R-project.org/package=jmv.
205.
Love J. Jmvconnect: Connect to the Jamovi Statistical Spreadsheet.; 2022. https://CRAN.R-project.org/package=jmvconnect.
206.
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
207.
All r CRAN packages [full list]. 2025. https://r-packages.io/packages. Accessed February 11, 2025.
208.
R Core Team. R: A language and environment for statistical computing. 2023. https://www.R-project.org/.
209.
Schwab, Simon, Held, Leonhard. Statistical programming: Small mistakes, big impacts. Wiley-Blackwell Publishing, Inc. 2021. doi:10.5167/UZH-205154
210.
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
211.
Xie Y. formatR: Format r Code Automatically.; 2022. https://CRAN.R-project.org/package=formatR.
212.
Müller K, Walthert L. Styler: Non-Invasive Pretty Printing of r Code.; 2023. https://CRAN.R-project.org/package=styler.
213.
Hester J, Angly F, Hyde R, et al. Lintr: A Linter for r Code.; 2023. https://CRAN.R-project.org/package=lintr.
214.
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
215.
Allaire J, Xie Y, Dervieux C, et al. Rmarkdown: Dynamic Documents for r.; 2023. https://CRAN.R-project.org/package=rmarkdown.
216.
Gohel D, Ross N. Officedown: Enhanced r Markdown Format for Word and PowerPoint.; 2023. https://CRAN.R-project.org/package=officedown.
217.
Xie Y. Bookdown: Authoring books and technical documents with r markdown. 2023. https://github.com/rstudio/bookdown.
218.
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
219.
Ioannidis JPA. How to Make More Published Research True. PLoS Medicine. 2014;11(10):e1001747. doi:10.1371/journal.pmed.1001747
220.
Krieger N, Perzynski A, Dalton J. Projects: A Project Infrastructure for Researchers.; 2021. https://CRAN.R-project.org/package=projects.
221.
Schultze A, Tazare J. The role of programming code sharing in improving the transparency of medical research. BMJ. October 2023:p2402. doi:10.1136/bmj.p2402
222.
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
223.
Francisco Rodríguez-Sánchez, Connor P. Jackson, Shaurita D. Hutchins. Grateful: Facilitate citation of r packages. 2023. https://github.com/Pakillo/grateful.
224.
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
225.
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
226.
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
227.
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
228.
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
229.
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
230.
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
231.
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
232.
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
233.
Meyer F, Perrier V. Esquisse: Explore and Visualize Your Data Interactively.; 2022. https://CRAN.R-project.org/package=esquisse.
234.
Diedenhofen B, Musch J. Cocor: A comprehensive solution for the statistical comparison of correlations. 2015;10:e0121945. doi:10.1371/journal.pone.0121945
235.
Khamis H. Measures of Association: How to Choose? Journal of Diagnostic Medical Sonography. 2008;24(3):155-162. doi:10.1177/8756479308317006
236.
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
237.
Dahlke JA, Wiernik BM. Psychmeta: An r package for psychometric meta-analysis. 2019;43. doi:10.1177/0146621618795933
238.
Anscombe FJ. Graphs in Statistical Analysis. The American Statistician. 1973;27(1):17-21. doi:10.1080/00031305.1973.10478966
239.
Northrop PJ. Anscombiser: Create Datasets with Identical Summary Statistics.; 2022. https://CRAN.R-project.org/package=anscombiser.
240.
Makowski D, Wiernik BM, Patil I, Lüdecke D, Ben-Shachar MS. Correlation: Methods for Correlation Analysis.; 2022. https://CRAN.R-project.org/package=correlation.
241.
Lüdecke D, Ben-Shachar MS, Patil I, et al. Easystats: Framework for Easy Statistical Modeling, Visualization, and Reporting.; 2022. https://easystats.github.io/easystats/.
242.
Griffith DM, Veech JA, Marsh CJ. Cooccur: Probabilistic species co-occurrence analysis in r. 2016;69. doi:10.18637/jss.v069.c02
243.
McHugh ML. The chi-square test of independence. Biochemia Medica. 2013:143-149. doi:10.11613/bm.2013.018
244.
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
245.
Anderson D, Heiss A, Sumners J. Equatiomatic: Transform Models into LaTeX Equations.; 2024. https://CRAN.R-project.org/package=equatiomatic.
246.
Henderson T. correctR: Corrected test statistics for comparing machine learning models on correlated samples. 2025. https://CRAN.R-project.org/package=correctR.
247.
Arel-Bundock V. Modelsummary: Data and model summaries in r. 2022;103. doi:10.18637/jss.v103.i01
248.
Hidalgo B, Goodman M. Multivariate or Multivariable Regression? American Journal of Public Health. 2013;103(1):39-40. doi:10.2105/ajph.2012.300897
249.
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
250.
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
251.
Kaplan J. fastDummies: Fast Creation of Dummy (Binary) Columns and Rows from Categorical Variables.; 2023. https://CRAN.R-project.org/package=fastDummies.
252.
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
253.
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
254.
Bland JM, Altman DG. Statistics notes: Matching. BMJ. 1994;309(6962):1128-1128. doi:10.1136/bmj.309.6962.1128
255.
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
256.
Sut N. Study designs in medicine. Balkan Medical Journal. 2015;31(4):273-277. doi:10.5152/balkanmedj.2014.1408
257.
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
258.
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
259.
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
260.
Chassé M, Fergusson DA. Diagnostic Accuracy Studies. Seminars in Nuclear Medicine. 2019;49(2):87-93. doi:10.1053/j.semnuclmed.2018.11.005
261.
Chidambaram AG, Josephson M. Clinical research study designs: The essentials. PEDIATRIC INVESTIGATION. 2019;3(4):245-252. doi:10.1002/ped4.12166
262.
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
263.
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
264.
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
265.
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
266.
Lim WM, Kumar S. Guidelines for interpreting the results of bibliometric analysis: A sensemaking approach. Global Business and Organizational Excellence. August 2023. doi:10.1002/joe.22229
267.
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
268.
Bacchetti P. Ethics and Sample Size. American Journal of Epidemiology. 2005;161(2):105-110. doi:10.1093/aje/kwi014
269.
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
270.
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
271.
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
272.
Goldfeld K, Wujciak-Jens J. Simstudy: Illuminating research methods through data generation. 2020;5:2763. doi:10.21105/joss.02763
273.
DeBruine L. Faux: Simulation for Factorial Designs.; 2023. doi:10.5281/zenodo.2669586
274.
Cheng A, Kessler D, Mackinnon R, et al. Reporting Guidelines for Health Care Simulation Research. Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare. 2016;11(4):238-248. doi:10.1097/sih.0000000000000150
275.
Rosseel Y. Lavaan: An r package for structural equation modeling. 2012;48. doi:10.18637/jss.v048.i02
276.
Contributors semTools. semTools: Useful Tools for Structural Equation Modeling.; 2016. https://CRAN.R-project.org/package=semTools.
277.
William Revelle. Psych: Procedures for Psychological, Psychometric, and Personality Research.; 2023. https://CRAN.R-project.org/package=psych.
278.
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
279.
Scott WA. Reliability of content analysis: The case of nominal scale coding. Public Opinion Quarterly. 1955;19(3):321. doi:10.1086/266577
280.
Cohen J. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement. 1960;20(1):37-46. doi:10.1177/001316446002000104
281.
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
282.
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
283.
Lehnert B. BlandAltmanLeh: Plots (Slightly Extended) Bland-Altman Plots.; 2015. https://CRAN.R-project.org/package=BlandAltmanLeh.
284.
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
285.
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
286.
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
287.
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
288.
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
289.
Neth H, Gaisbauer F, Gradwohl N, Gaissmaier W. Riskyr: Rendering Risk Literacy More Transparent.; 2022. https://CRAN.R-project.org/package=riskyr.
290.
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
291.
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
292.
Sousa-Pinto PD with contributions from B. Mada: Meta-Analysis of Diagnostic Accuracy.; 2022. https://CRAN.R-project.org/package=mada.
293.
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
294.
Robin X, Turck N, Hainard A, et al. pROC: An open-source package for r and s+ to analyze and compare ROC curves. 2011;12:77.
295.
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
296.
Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ. October 2015:h5527. doi:10.1136/bmj.h5527
297.
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
298.
Reeves BC, Gaus W. Guidelines for Reporting Non-Randomised Studies. Complementary Medicine Research. 2004;11(1):46-52. doi:10.1159/000080576
299.
Bland JM, Altman DG. Comparisons within randomised groups can be very misleading. BMJ. 2011;342(may06 2):d561-d561. doi:10.1136/bmj.d561
300.
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
301.
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
302.
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
303.
Laird N. Further Comparative Analyses of Pretest-Posttest Research Designs. The American Statistician. 1983;37(4a):329-330. doi:10.1080/00031305.1983.10483133
304.
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
305.
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
306.
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
307.
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
308.
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
309.
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
310.
Gruijters SLK. Baseline comparisons and covariate fishing: Bad statistical habits we should have broken yesterday. July 2020. http://dx.doi.org/10.31234/osf.io/qftwg.
311.
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
312.
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
313.
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
314.
Altman DG. Statistics notes: Interaction revisited: The difference between two estimates. BMJ. 2003;326(7382):219-219. doi:10.1136/bmj.326.7382.219
315.
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
316.
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
317.
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. October 2022. doi:10.1002/sim.9592
318.
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
319.
Dayim A. Consort: Create Consort Diagram.; 2023. https://CRAN.R-project.org/package=consort.
320.
Lajeunesse MJ. Facilitating systematic reviews, data extraction, and meta-analysis with the metagear package for r. 2016;7:323-330.
321.
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
322.
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. 2022;18:e1230. doi:10.1002/cl2.1230
323.
Borenstein M. In a meta-analysis, the I-squared statistic does not tell us how much the effect size varies. Journal of Clinical Epidemiology. October 2022. doi:10.1016/j.jclinepi.2022.10.003
324.
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
325.
Grooth HJ de, Parienti JJ. Heterogeneity between studies can be explained more reliably with individual patient data. Intensive Care Medicine. July 2023. doi:10.1007/s00134-023-07163-z
326.
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
327.
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/.
328.
Wallisch C, Bach P, Hafermann L, et al. Review of guidance papers on regression modeling in statistical series of medical journals. Mathes T, ed. PLOS ONE. 2022;17(1):e0262918. doi:10.1371/journal.pone.0262918
329.
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
330.
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
331.
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
332.
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
333.
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
334.
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
335.
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
336.
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
337.
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
338.
Groves T. Research methods and reporting. BMJ. 2008;337(oct22 1):a2201-a2201. doi:10.1136/bmj.a2201
339.
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
340.
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
341.
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