Referências

1.

Armitage P, Berry G, Matthews JNS. Statistical methods in medical research. John Wiley & Sons; 2008.

2.

Massad E, Silveira PSP, Menezes RX de, Ortega NRS. Métodos quantitativos em medicina. Editora Manole Ltda; 2004.

3.

Kendall MG. Studies in the history of probability and statistics. Where shall the history of statistics begin? Biometrika. 1960;47(3/4):447–9.

4.

Breve história dos censos [Internet]. Instituto Nacional de Estatistica. Statistics Portugal; 2014. Available from: https://censos.ine.pt/xportal/xmain?xpid=CENSOS&xpgid=censos_bhistoria

5.

Salgado-Neto G, Salgado A. Sir francis galton e os extremos superiores da curva normal. Revista de Ciências Humanas. 2011;45(1):223–39.

6.

Stolley PD, Lasky T. The beginnings of epidemiology. In: Investigating disease patterns. Scientific American Library; 2000. p. 23–49.

7.

Editors History com. Florence Nightingale. https://www.history.com/topics/womens-history/florence-nightingale-1;

8.

Moore DS. Topics in inferency. In: The basic practice of statistics. W.H. Freeman; 2000. p. 417.

9.

Salsburg D. Uma senhora toma chá... In: Uma senhora toma chá. Zahar; 2009. p. 17–23.

10.

Hald A. Biography of fisher. In: A history of parametric statistics inference from bernoulli to fisher,1713-1935. John Wiley & Sons; 2007. p. 159–63.

11.

Kruskal W. The significance of fisher: A review of r.a. Fisher: The life of a scientist. Journal of the American Statistical Association [Internet]. 1980;75(372):1019–30. Available from: https://doi.org/10.1080/01621459.1980.10477590

12.

Stolley PD, Lasky T. Lung cancer: New methods of studying disease. In: Investigating disease patterns. Scientific American Library; 2000. p. 51–79.

13.

Matthews R, Chalmers I, Rothwell P. Douglas g altman: Statistician, researcher, and driving force behind global initiatives to improve the reliability of health research. British Medical Journal Publishing Group; 2018.

14.

Altman DG. The scandal of poor medical research. Vol. 308, Bmj. British Medical Journal Publishing Group; 1994. p. 283–4.

15.

R Core Team. The r project for statistical computing | what is r? Disponível em: https://www.r-project.org/about.html; 2022.

16.

R Core Team. The r project for statistical computing | CRAN mirrors. Disponível em: https://cran.r-project.org/mirrors.html; 2022.

17.

Whitney L et al. R programming language continues to grow in popularity [Internet]. TechRepublic. 2020. Available from: https://www.techrepublic.com/article/r-programming-language-continues-to-grow-in-popularity

18.

Oliveira Filho PF de. Natureza dos dados. In: Epidemiologia e bioestatística–fundamentos para a leitura crítica. 2ª edição. Editora Rubio; 2022. p. 3–6.

19.

Kirkwood BR, Sterne JA. Defining the data. In: Essential medical statistics. Second Edition. Blackwell Science Company; 2003. p. 9–14.

20.

Sternbach GL. The glasgow coma scale. The Journal of emergency medicine. 2000;19(1):67–71.

21.

Pediatrics AA of, Obstetricians AC of. The apgar score. Pediatrics. 2006;117(4):1444–7.

22.

Bowers D. First things first-the nature of data. In: Medical statistics from scratch. Second Edition. John Wiley; Sons; 2008. p. 3–13.

23.

Ribeiro Mendes F. O que é um trabalho científico. In: Iniciacão cientifica. Autonomia Editora; 2012. p. 17–26.

24.

Hulley SB, Cummings SR, Browner WS, Grady DG, Newman TB. Elaborando a questão de pesquisa e desenvolvendo o plano de estudo. In: Delineando a pesquisa clinica. Quarta Edição. Artmed Editora; 2015. p. 15–24.

25.

McCombes S. Sampling methods [Internet]. https://www.scribbr.com/methodology/sampling-methods/. scribbr.com Team; 2019. Available from: https://www.scribbr.com/

26.

Callegari-Jacques SM. Amostras. In: Bioestatistica: Principios e aplicações. Artmed Editora; 2003. p. 146–7.

27.

Faul F, Erdfelder E, Lang A-G, Buchner A. G* power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior research methods. 2007;39(2):175–91.

28.

Cohen J. Statistical power analysis for the behavioral sciences. Lawrence Erlbaum Associates; 1988.

29.

Grimes DA, Schulz KF. An overview of clinical research: The lay of the land. The lancet. 2002;359(9300):57–61.

30.

Fletcher RH, Fletcher SW, Fletcher GS. Prognóstico. In: Epidemiologia clínica: Elementos essenciais. Artmed Editora; 2014. p. 108–9.

31.

Grimes DA, Schulz KF. Descriptive studies: What they can and cannot do. The Lancet. 2002;359(9301):145–9.

32.

Fletcher RH, Fletcher SW, Fletcher GS. Risco: Da doença à exposição. In: Epidemiologia clínica: Elementos essenciais. Artmed Editora; 2014. p. 88.

33.

Grimes DA, Schulz KF. Compared to what? Finding controls for case-control studies. The Lancet. 2005;365(9468):1429–33.

34.

Ernster VL. Nested case-control studies. Preventive Medicine. 1994;23(5):587–90.

35.

Newman TB, Browner WS, Cummings SR, Hulley SB. Delineando estudos de caso-controle. In: Delineando a pesquisa clinica. Quarta Edição. Artmed Editora; 2015. p. 111.

36.

Grimes DA, Schulz KF. Cohort studies: Marching towards outcomes. The Lancet. 2002;359(9303):341–5.

37.

Fletcher RH, Fletcher SW, Fletcher GS. Risco: Da doença à exposição. In: Epidemiologia clínica: Elementos essenciais. Artmed Editora; 2014. p. 68.

38.

Kannel WB, McGee DL. Diabetes and cardiovascular risk factors: The framingham study. Circulation. 1979;59(1):8–13.

39.

Celentano DD, Szklo M. Cohort studies. In: Gordis epidemiology. 6th Edition. Elsevier; 2019. p. 179.

40.

Coutinho M. Principios de epidemiologia clínica aplicada a cardiologia. Arquivos Brasileiros de Cardiologia. 1998;71:109–16.

41.

McCambridge J, Witton J, Elbourne DR. Systematic review of the hawthorne effect: New concepts are needed to study research participation effects. Journal of Clinical Epidemiology. 2014;67(3):267–77.

42.

Bland JM, Altman DG. Statistic notes: Regression towards the mean. BMJ. 1994;308(6942):1499.

43.

Fletcher RH, Fletcher SW, Fletcher GS. Tratamento. In: Epidemiologia clínica: Elementos essenciais. Artmed Editora; 2014. p. 143.

44.

Kabisch M, Ruckes C, Seibert-Grafe M, Blettner M. Randomized controlled trials: Part 17 of a series on evaluation of scientific publications. Deutsches Ärzteblatt International. 2011;108(39):663.

45.

Elander G, Hermerén G. Placebo effect and randomized clinical trials. Theoretical Medicine. 1995;16(2):171–82.

46.

Schulz KF, Grimes DA. Blinding in randomised trials: Hiding who got what. The Lancet. 2002;359(9307):696–700.

47.

Montori VM, Guyatt GH. Intention-to-treat principle. CMAJ. 2001;165(10):1339–41.

48.

Christensen E. Methodology of superiority vs. Equivalence trials and non-inferiority trials. Journal of hepatology. 2007;46(5):947–54.

49.

Health Improvement O for, Disparities. Crossover randomised controlled trial: Comparative studies [Internet]. Office for Health Improvement and Disparities. UK Health improvement; 2020. Available from: https://www.gov.uk/guidance/crossover-randomised-controlled-trial-comparative-studies

50.

Physicians’ Health Study Research Group* SC of the. Final report on the aspirin component of the ongoing physicians’ health study. New England Journal of Medicine. 1989;321(3):129–35.

51.

Hennekens CH, Buring JE, et al. Lack of effect of long-term supplementation with beta carotene on the incidence of malignant neoplasms and cardiovascular disease. New England Journal of Medicine. 1996;334(18):1145–9.

52.

Stanley K. Design of randomized controlled trials. Circulation. 2007;115(9):1164–9.

53.

Chang W. Cookbook for r. Cookbook for R. http://www.cookbook-r.com; 2021.

54.

Verzani J. Using r for introductory statistics. Chapman; Hall/CRC; 2004.

55.

Rinker TW, Kurkiewicz D. Pacman: Package management for r [Internet]. Buffalo, New York; 2018. Available from: http://github.com/trinker/pacman

56.

Damiani A, Milz B, Lente C, al et. Ciência de dados em r [Internet]. R6 Consultoria; 2015. Available from: https://livro.curso-r.com/index.html

57.

Zuur AF, Ieno EN, Meesters EH. Getting data into r. In: A beginner’s guide to r. Springer; 2009. p. 29–56.

58.

Wickham H, Grolemund G. 15 factors|r for data science [Internet]. Welcome | R for Data Science. O’Reilly; 2017. Available from: https://r4ds.had.co.nz/factors.html

59.

Ooms J. Writexl: Export data frames to excel ’xlsx’ format [Internet]. 2022. Available from: https://CRAN.R-project.org/package=writexl

60.

Team RC. Write.table: Data output/CSV files [Internet]. DataCamp; 2022. Available from: https://www.rdocumentation.org/packages/utils/versions/3.6.2/topics/write.table

61.

Wickham H, Averick M, Bryan J, Chang W, et al. Welcome to the tidyverse. Journal of open source software. 2019;4(43):1686.

62.

Wickham H, Girlich M. Tidyr: Tidy messy data [Internet]. 2022. Available from: https://CRAN.R-project.org/package=tidyr

63.

Wickham H. Tidy data. Journal of Statistical Software. 2014;59(10):11–23.

64.

Fisher RA. The use of multiple measurements in taxonomic problems. Annals of eugenics. 1936;7(2):179–88.

65.

Wickham H, François R, Henry L, Müller K, et al. Dplyr: A grammar of data manipulation. R package version 04. 2015;3:156.

66.

Wickham H, Bryan J. Readxl: Read excel files. R package version. 2019;1(1):1–3.

67.

Grolemund G, Wickham H. Dates and times made easy with lubridate. Journal of Statistical Software [Internet]. 2011;40(3):1–25. Available from: https://www.jstatsoft.org/v40/i03/

68.

Field A, Miles J, Field Z. Everithing you ever wanted to know about statistics (well, sort of). In: Discovering statistics using r. Sage Publications, Ltd; 2012. p. 38.

69.

Arango HG. Organização dos dados em tabelas. In: Bioestatística: Teórica e computacional. 3ª edição. Guanabara Koogan; 2009. p. 32–57.

70.

Oliveira Filho PF de. Tabelas. In: Epidemiologia e bioestatística-fundamentos para a leitura crítica. 2ª edição. Editora Rubio; 2022. p. 9–12.

71.

Xie Y. Knitr: A comprehensive tool for reproducible research in r. In: Implementing reproducible research. Chapman; Hall/CRC; 2018. p. 3–31.

72.

Zhu H et al. Construct complex table with “kable” and pipe syntax [Internet]. 2021. Available from: https://CRAN.R-project.org/package=kableExtra

73.

Arango HG. Números de classes e intervalo de classes. In: Bioestatística teórica e computacional. Terceira edição. Guanabara Koogan, RJ; 2009. p. 35–40.

74.

Rasmussen KM, Yaktine AL, et al. Weight gain during pregnancy: Reexamining the guidelines. 2009;

75.

Field A, Miles J, Field Z. Exploring data with graphs. In: Discovering statistics using r. Sage Publications, Ltd; 2012. p. 117.

76.

Wickham H. Getting started with ggplot2. In: ggplot2. Second edition. Springer; 2016. p. 11–31.

77.

Tufte ER. Aesthetics and technique in data graphical design. In: The visual display of quantitative information. Second edition. Graphics Press; 2001. p. 178.

78.

Lemon J, Bolker B, Oom S, et al. Package “plotrix.” Vienna: R Development Core Team. 2015;

79.

Kabacoff RI. Basic graphs. In: R in action: Data analysis and graphics with r. Manning Publications Co.; 2011. p. 120–4.

80.

Harrell FE, Dupont C. Hmisc: Harrell miscellaneous [Internet]. R package version. 2022. Available from: https://cran.r-project.org/web/packages/Hmisc/index.html

81.

Wickham H. ggplot2: Elegant graphics for data analysis [Internet]. Springer-Verlag New York; 2016. Available from: https://ggplot2.tidyverse.org

82.

Wickham H. A layered grammar of graphics. Journal of Computational and Graphical Statistics. 2010;19(1):3–28.

83.

Wickham H, Seidel D. Scales: Scale functions for visualization [Internet]. 2022. Available from: https://CRAN.R-project.org/package=scales

84.

Debnath L, Basu K. A short history of probability theory and its applications. International Journal of Mathematical Education in Science and Technology. 2015;46(1):13–39.

85.

Menezes RX de. Introdução à probabilidade. In: Massad E, Menezes RX de, Silveira PSP, Ortega NRS, editors. Métodos quantitativos em medicina. Barueri, São Paulo: Editora Manole Ltda.; 2004. p. 151–87.

86.

Pagano M, Kimberly G. Theoretical probability distributions. In: Principles of biostatistics. Second Edition. CRC Press; 2000. p. 162.

87.

Gonzalez JCS. Normal distribution in r [Internet]. R CODER. 2021. Available from: https://r-coder.com/

88.

Jain S. A guide to dnorm, pnorm, rnorm, and qnorm in r [Internet]. GeeksforGeeks. 2022. Available from: https://www.geeksforgeeks.org/

89.

Robertson E, O’Connor J. Jacob (jacques) bernoulli [Internet]. Maths History. School of Mathematics; Statistics, University of St Andrews; 2022. Available from: https://mathshistory.st-andrews.ac.uk/Biographies/Bernoulli_Jacob/

90.

Fisher LD, Van Belle G. Poisson random variables. In: Biostatistics: A methodology for the health sciences. New York, NY: John Wiley & Sons; 1993. p. 211–8.

91.

Peat J, Barton B. Descriptive statistics. In: Medical statistics : A guide to SPSS, data analysis, and critical appraisal. New York, NY: John Wiley & Sons; 2014. p. 24–51.

92.

Joanes D, Gill C. Comparing measures of sample skewness and kurtosis. Journal of the Royal Statistical Society. 1998;47(1):183–9.

93.

George D, Mallery P. Descriptive statistics. In: IBM SPSS statistics 26 step by step: A simple guide and reference. New York, NY: Taylor & Francis Group; 2020. p. 114–20.

94.

Pagano M, Gavreau K. The central limit theorem. In: Principles of biostatistics. Second Edition. Pacific Grove, CA: Duxbury; 2000. p. 197–8.

95.

Motulsky H. The theory of confidence intervals. In: Intuitive biostatistics: A nonmathematical guide to statistical thinking. Second Edition. New York, NY: Oxford University Press; 2010. p. 96–102.

96.

Signorell A et al. DescTools: Tools for descriptive statistics [Internet]. 2022. Available from: https://cran.r-project.org/package=DescTools

97.

Kelen GD, Brown CB, Ashton J. Statistical reasoning in clinical trials: Hypothesis testing. Am J Emerg Med. 1988;1(1):52–61.

98.

Menezes RX de, Burattini MN. Testes de hipótese e intervalos de confiança. In: Massad E, Menezes RX de, Silveira PSP, Ortega NRS, editors. Métodos quantitativos em medicina. Barueri, São Paulo: Editora Manole Ltda.; 2004. p. 225–41.

99.

Guyatt G, Jaeschke R, Heddle N, et al. Basic statistics for clinicians: 1. Hypothesis testing. CMAJ: Canadian Medical Association Journal. 1995;152(1):27.

100.

Fletcher RH, Fletcher SW, Fletcher GS. Acaso. In: Epidemiologia clínica: Elementos essenciais. Quinta Edição. Artmed Editora; 2014. p. 108–9.

101.

Menezes RX de, Burattini MN. Testes de hipótese e intervalos de confiança. In: Massad E, Menezes RX de, Silveira PSP, Ortega NRS, editors. Métodos quantitativos em medicina. Barueri, São Paulo: Editora Manole Ltda.; 2004. p. 225–41.

102.

Pagano M, Kimberly G. Comparison of two means. In: Principles of biostatistics. Second Edition. CRC Press; 2000. p. 262–72.

103.

Zimmerman DW. A note on preliminary tests of equality of variances. Br J Math Stat Psychol. 2004;57(1):173–81.

104.

Razali NM, Wah YB, et al. Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests. Journal of statistical modeling and analytics. 2011;2(1):21–33.

105.

Ghasemi A, Zahediasl S. Normality tests for statistical analysis: A guide for non-statisticians. International journal of endocrinology and metabolism. 2012;10(2):486.

106.

Yap BW, Sim CH. Comparisons of various types of normality tests. Journal of Statistical Computation and Simulation. 2011;81(12):2141–55.

107.

Fox J, Weisberg S. An r companion to applied regression [Internet]. Third. Thousand Oaks CA: Sage; 2019. Available from: https://socialsciences.mcmaster.ca/jfox/Books/Companion/

108.

Kassambara A. Rstatix: Pipe-friendly framework for basic statistical tests [Internet]. 2022. Available from: https://CRAN.R-project.org/package=rstatix

109.

Cohen J. Statistical power analysis for the behavioral sciences. 2nd Edition. Routledge; 1988.

110.

Lindenau JD, Guimaraes LSP. Calculating the effect size in SPSS. Revista HCPA [Internet]. 2012;32(3):363–81. Available from: https://seer.ufrgs.br/hcpa

111.

Field A, Miles J, Field Z. Comparing several means: ANOVA (GML 1). In: Discovering statistics using r. Sage Publications, Ltd; 2012. p. 399–400.

112.

Menezes RX de. Análise de variância. In: Massad E, Menezes RX de, Silveira PSP, Ortega NRS, editors. Métodos quantitativos em medicina. Barueri, São Paulo: Editora Manole Ltda.; 2004. p. 297–300.

113.

Garren ST. Package fastgraph [Internet]. CRAN. Comprehensive R Archive Network (CRAN); 1919. Available from: https://CRAN.R-project.org/package=fastGraph

114.

Peat J, Barton B. Continuous variables: Analysis of variance. In: Medical statistics : A guide to SPSS, data analysis, and critical appraisal. New York, NY: John Wiley & Sons; 2014. p. 114.

115.

Dag O, Dolgun A, Konar NM. Onewaytests: An r package for one-way tests in independent groups designs. R Journal. 2018;10(1):175–99.

116.

Ben-Shachar MS, Lüdecke D, Makowski D. Effectsize: Estimation of effect size indices and standardized parameters. Journal of Open Source Software. 2020;5(56):2815.

117.

Watson P. Rules of thumb on magnitudes of effect sizes [Internet]. MRC Cognition and Brain Sciences Unit. Cambridge University; 2021. Available from: https://imaging.mrc-cbu.cam.ac.uk/statswiki/FAQ/effectSize

118.

Field A, Miles J, Field Z. Factorial ANOVA (GLM3). In: Discovering statistics using r. Sage Publications, Ltd; 2012. p. 513–4.

119.

Kassambara A. Ggpubr:’ggplot2’ based publication ready plots [r package ggpubr version 0.5.0] [Internet]. The Comprehensive R Archive Network. Comprehensive R Archive Network (CRAN); 2022. Available from: https://cloud.r-project.org/web/packages/ggpubr/index.html

120.

Patterson R, Coffman J, Goldstein-Greenwood J, Others. Understanding diagnostic plots for linear regression analysis [Internet]. Research Data Services + Sciences. University of Virginia Library; 2015. Available from: https://data.library.virginia.edu/diagnostic-plots/

121.

Wickens TD, Keppel G. Two-way factorial experiments. In: Design and analysis: A researcher’s handbook. Pearson Prentice-Hall; 2004. p. 193–286.

122.

Maxwell SE, Delaney HD, Kelley K. Two-way between-subject factorial designs. In: Designing experiments and analyzing data: A model comparison perspective. Third Edition. Routledge; 2017. p. 312–82.

123.

Lenth R, Singmann H, Love J, Buerkner P, Herve M. Emmeans: Estimated marginal means, aka least-squares means. R package version. 2018;1(1):3.

124.

Rosenberg M. Society and the adolescent self-image. Princeton university press; 2015.

125.

Dini G, Quaresma M, Ferreira L, et al. Adaptação cultural e validação da versão brasileira da escala de autoestima de rosenberg. Revista Brasileira de Cirurgia Plástica. 2001;19(1):41–52.

126.

Huynh H, Feldt LS. Estimation of the box correction for degrees of freedom from sample data in randomized block and split-plot designs. Journal of Educational Statistics. 1976;1(1):69–82.

127.

Girden ER. Two-factor studies with repeated measures on both factors. In: ANOVA: Repeated measures. Sage; 1992. p. 31–40. (QASS series; vol. 84).

128.

Muller KE, Barton CN. Approximate power for repeated-measures ANOVA lacking sphericity. Journal of the American Statistical Association. 1989;84(406):549–55.

129.

Greene Jr JW, Touchstone JC. Urinary estriol as an index of placental function. A study of 279 cases. Obstetrical & Gynecological Survey. 1963;18(3):356–9.

130.

Kassambara A. Correlation test between two variables in r [Internet]. STHDA - Statistical tools for high-throughput data analysis. 2021. Available from: http://www.sthda.com/english/wiki/correlation-test-between-two-variables-in-r

131.

Schober P, Boer C, Schwarte LA. Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia. 2018;126(5):1763–8.

132.

De Winter JC, Gosling SD, Potter J. Comparing the pearson and spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Psychological methods. 2016;21(3):273.

133.

Sedgwick P. Correlation versus linear regression. BMJ. 2013;346.

134.

Kim H-Y. Statistical notes for clinical researchers: Simple linear regression 3–residual analysis. Restorative dentistry & endodontics. 2019;44(1).

135.

Field A, Miles J, Field Z. Regression. In: Discovering statistics using r. Sage Publications, Ltd; 2012. p. 266–76.

136.

Peat J, Barton B. Correlation and regression. In: Medical statistics : A guide to SPSS, data analysis, and critical appraisal. New York, NY: John Wiley & Sons; 2014. p. 209.

137.

Altman DG, Gardner MJ. Statistics in medicine: Calculating confidence intervals for regression and correlation. British Medical Journal (Clinical research ed). 1988;296(6631):1238.

138.

Altman DG. Comparing groups: Categorical data. In: Practical statistics for medical research. London: Chapman & Hall/CRC; 1991. p. 244–7.

139.

Myszkowski N. Nhstplot package [Internet]. RDocumentation. 2020. Available from: https://rdocumentation.org/packages/nhstplot/versions/1.1.0

140.

Warnes GR, Bolker B, et al. Gmodels: Various r programming tools for model fitting [Internet]. CRAN R Project. 2022. Available from: https://rdrr.io/cran/gmodels/

141.

Comtois D. Summarytools: Tools to quickly and neatly summarize data [Internet]. CRAN R Project. 2022. Available from: https://github.com/dcomtois/summarytools

142.

Daniel WW, Cross CL. The chi-square distribution and analysis of frequencies. In: Practical statistics for medical research. Hoboken, NJ: John Wiley & Sons, Inc; 2013. p. 604–19.

143.

Eliasziw M, Donner A. Application of the McNemar test to non-independent matched pair data. Statistics in medicine. 1991;10(12):1981–91.

144.

Rosner B. Hypothesis testing: Categorical data. In: Fundamentals of biostatistics. Seventh Edition. Boston: Cengage; 2011. p. 377.

145.

Mayer M. Confintr: Confidence intervals [Internet]. 2022. Available from: https://CRAN.R-project.org/package=confintr

146.

Altman DG. Comparing groups: Continuos data. In: Practical statistics for medical research. London: Chapman & Hall/CRC; 1991. p. 194–7.

147.

Hothorn T, Hornik K, Van De Wiel MA, Zeileis A. A lego system for conditional inference. The American Statistician. 2006;60(3):257–63.

148.

Zar JH. Paired-sample hypotheses. In: Biostatistical analysis. Edinburgh: Pearson; 2014. p. 189–98.

149.

Karadimitriou SM, Marshall E. Kruskal-wallis in r [Internet]. Statistics Support for Students. Loughborough; Coventry Universities; 2020. Available from: https://www.statstutor.ac.uk/

150.

Zar JH. Nonparametric analysis of variance. In: Biostatistical analysis. Edinburgh: Pearson; 2014. p. 226–30.

151.

Kanji GK. The kruskal–wallis test. In: 100 statiscal tests. 3rd Edition. London: Sage publications; 2006. p. 220.

152.

Tomczak M, Tomczak E. The need to report effect size estimates revisited. An overview of some recommended measures of effect size. Trends in sport sciences. 2014;1(21):19–25.

153.

Dunn OJ. Multiple comparisons using rank sums. Technometrics. 1964;6(3):241–52.

154.

Straus SE, Glasziou P, et al. Diagnosis and screening. In: Evidence-based medicine: How to practice and teach EBM. Fifth Edition. Edinburgh: Elsevier; 2019. p. 185–218.

155.

Altman DG, Bland JM. Diagnostic tests. 1: Sensitivity and specificity. BMJ: British Medical Journal. 1994;308(6943):1552.

156.

Peixoto R de O, Nunes TA, Gomes CA. Indices diagnósticos da ultrassonografia abdominal na apendicite aguda: Influência do genero e constituição física, tempo evolutivo da doença e experiencia do radiologista. Revista do Colégio Brasileiro de Cirurgiões. 2011;38:105–11.

157.

Mark, Sergeant E, et al. epiR: Tools for the analysis of epidemiological data [Internet]. 2022. Available from: https://CRAN.R-project.org/package=epiR

158.

Altman DG, Bland JM. Diagnostic tests 2: Predictive values. Bmj. 1994;309(6947):102.

159.

Pereira Lima A, Vieira FJ, Oliveira GP de M, et al. Perfil clinico-epidemiologico da apendicite aguda: Analise retrospectiva de 638 casos. Revista do Colegio Brasileiro de Cirurgiões. 2016;43:248–53.

160.

Halkin A, Reichman J, Schwaber M, Paltiel O, Brezis M. Likelihood ratios: Getting diagnostic testing into perspective. QJM: monthly journal of the Association of Physicians. 1998;91(4):247–58.

161.

Deeks JJ, Altman DG. Diagnostic tests 4: Likelihood ratios. Bmj. 2004;329(7458):168–9.

162.

Guyatt G, Rennie D, et al. Diagnostic tests. In: User’s guides to medical literature: A manual for evidence-based clinical practice. 3rd Edition. New York: JAMA; 2015. p. 607–31.

163.

Oliveira Filho PF de. Testes diagnósticos. In: Epidemiologia e bioestatística: Fundamentos para a leitura crítica. Segunda Edição. Rio de Janeiro: Editora Rubio; 2022. p. 89–105.

164.

Fagan T. Nomogram for bayes’s theorem. New England Journal of Medicine. 1975;293:257.

165.

Caraguel CG, Vanderstichel R. The two-step fagan’s nomogram: Ad hoc interpretation of a diagnostic test result without calculation. BMJ Evidence-Based Medicine. 2013;18(4):125–8.

166.

Altman DG, Bland JM. Diagnostic tests 3: Receiver operating characteristic plots. BMJ: British Medical Journal. 1994;309(6948):188.

167.

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(1):1–8.

168.

Borges LSR. Diagnostic accuracy measures in cardiovascular research. Int J Cardiovasc Sci. 2016;29(3):218–22.

169.

DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics. 1988;44:837–45.

170.

Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3(1):32–5.

171.

Cohen J. A coefficient of agreement for nominal scales. Educational and Psychological Measurement. 1960;20(1):37–46.

172.

Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;159–74.

173.

Zeileis A, Meyer D, Hornik K. Residual-based shadings for visualizing (conditional) independence. Journal of Computational and Graphical Statistics. 2007;16(3):507–25.

174.

Feychting M, Osterlund B, Ahlbom A. Reduced cancer incidence among the blind. Epidemiology. 1998;9(5):490–4.

175.

Szklo M, Nieto FJ. Measuring disease occurrence. In: Epidemiology: Beyond the basics. Fourth Edition. Burlington, MA: Jones & Bartlett Learning; 2019. p. 80–1.

176.

Gross M. Oswego county revisited. Public health reports. 1976;91(2):168.

177.

Aragon TJ. Epitools: Epidemiology tools [Internet]. 2020. Available from: https://CRAN.R-project.org/package=epitools

178.

Szklo M, Nieto FJ. Measuring associations between exposures and outcomes. In: Epidemiology: Beyond the basics. Fourth Edition. Burlington, MA: Jones & Bartlett Learning; 2019. p. 88–94.

179.

Davies HTO, Crombie IK, Tavakoli M. When can odds ratios mislead? BMJ. 1998;316(7136):989–91.

180.

Hopkins WG. A scale of magnitudes for effect statistics [Internet]. A New View of Statistics. Sportscience; 2016. Available from: http://www.sportsci.org/resource/stats/index.html

181.

Madi JM, Souza R da S de, Araujo BF de, Oliveira Filho PF, et al. Prevalence of toxoplasmosis, HIV, syphilis and rubella in a population of puerperal women using whatman 903 filter paper. The Brazilian Journal of Infectious Diseases. 2010;14(1):24–9.

182.

Szklo M, Nieto FJ. Measuring associations between exposures and outcomes. In: Epidemiology: Beyond the basics. Fourth Edition. Burlington, MA: Jones & Bartlett Learning; 2019. p. 84–102.

183.

Szklo M, Nieto FJ. Measuring associations between exposures and outcomes. In: Epidemiology: Beyond the basics. Fourth Edition. Burlington, MA: Jones & Bartlett Learning; 2019. p. 97–8.

184.

Kohl M. Package “MKmisc” [Internet]. 2019. Available from: https://github.com/stamats/MKmisc

185.

Bender R. Calculating confidence intervals for the number needed to treat. Controlled clinical trials. 2001;22(2):102–10.

186.

Clark TG, Bradburn MJ, Love SB, Altman DG. Survival analysis part i: Basic concepts and first analyses. British Journal of Cancer. 2003;89(2):232–8.

187.

Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association. 1958;53(282):457–81.

188.

Peat J, Barton B. Survival analyses. In: Medical statistics : A guide to SPSS, data analysis, and critical appraisal. New York, NY: John Wiley & Sons; 2014. p. 352–3.

189.

Qiu W, Chavarro J, Lazarus R, Rosner B, Ma J. powerSurvEpi: Power and sample size calculation for survival analysis of epidemiological studies. R package version 00. 2015;9.

190.

Therneau T et al. A package for survival analysis in r. R package version. 2015;2(7).

191.

Kassambara A, Kosinski M, Biecek P, Fabian S. Survminer: Drawing survival curves using ggplot2. URL https://CRAN R-project org/package= survminer R package version 04. 2021;9.

192.

Bland JM, Altman DG. The logrank test. BMJ. 2004;328(7447):1073.

193.

Peat J, Barton B. Adjusted odds ratios. In: Medical statistics : A guide to SPSS, data analysis, and critical appraisal. New York, NY: John Wiley & Sons; 2014. p. 298–308.

194.

Leisch F, Dimitriadou E. Package mlbench [Internet]. CRAN. Comprehensive R Archive Network (CRAN); 2009. Available from: https://cran.r-project.org/web/packages/mlbench/index.html

195.

Field A, Miles J, Field Z. Logistic regression. In: Discovering statistics using r. Sage Publications, Ltd; 2012. p. 320.

196.

Hosmer Jr DW, Lemeshow S, Sturdivant RX. Interpretation of fitted logistic regression model. In: Applied logistic regression. Third Edition. John Wiley & Sons; 2013. p. 49–64.

197.

Ripley B, Venables B, Bates DM, et al. Support functions and datasets for venables and ripley’s MASS [Internet]. CRAN: The R Project for Statistical Computing. 2023. Available from: https://cran.r-project.org/web/packages/MASS/MASS.pdf

198.

Field A, Miles J, Field Z. Logistic regression. In: Discovering statistics using r. Sage Publications, Ltd; 2012. p. 318.

199.

Kabacoff RI. Generalized linear models. In: R in action: Data analysis and graphics with r. Manning Publications Co.; 2011. p. 317–22.

200.

Field A, Miles J, Field Z. Logistic regression. In: Discovering statistics using r. Sage Publications, Ltd; 2012. p. 344–5.

201.

Mansfield ER, Helms BP. Detecting multicollinearity. The American Statistician. 1982;36(3a):158–60.

202.

McFadden D. Quantitative methods for analysing travel behaviour of individuals: Some recent developments. In: Behavioural travel modelling. Routledge; 2021. p. 279–318.

203.

Field A, Miles J, Field Z. Outliers and influential cases. In: Discovering statistics using r. Sage Publications, Ltd; 2012. p. 288–92.

204.

Hoaglin DC, Welsch RE. The hat matrix in regression and ANOVA. The American Statistician. 1978;32(1):17–22.

205.

Stevens JP. Outliers and influential data points in regression analysis. Psychological bulletin. 1984;95(2):334.

206.

Fawcett T. An introduction to ROC analysis. Pattern recognition letters. 2006;27(8):861–74.