References cited by SimtablR

Full references for every method and recommendation that SimtablR cites, both on its help pages and in its educator advice. Advice messages show a short tag such as "(Ver Hoef & Boveng 2007)"; each entry below starts with that tag in bold, so you can look it up here. Use simtablr_guidance("teaching") to show the citation on every advice message, or advise(result) to list the advice for a stored result.

Details

Effect measures and association tests

Anscombe 1956. Anscombe, F. J. (1956). On estimating binomial response relations. Biometrika, 43(3/4), 461–464. doi:10.1093/biomet/43.3-4.461 .

Barros & Hirakata 2003. Barros, A. J. D., & Hirakata, V. N. (2003). Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Medical Research Methodology, 3, 21. doi:10.1186/1471-2288-3-21 .

Bender & Lange 2001. Bender, R., & Lange, S. (2001). Adjusting for multiple testing: when and how? Journal of Clinical Epidemiology, 54(4), 343–349. doi:10.1016/S0895-4356(00)00314-0 .

Campbell 2007. Campbell, I. (2007). Chi-squared and Fisher-Irwin tests of two-by-two tables with small sample recommendations. Statistics in Medicine, 26(19), 3661–3675. doi:10.1002/sim.2832 .

Fisher 1935. Fisher, R. A. (1935). The Design of Experiments. Oliver & Boyd.

Greenland & Robins 1985. Greenland, S., & Robins, J. M. (1985). Estimation of a common effect parameter from sparse follow-up data. Biometrics, 41(1), 55–68. doi:10.2307/2530643 .

Haldane 1956. Haldane, J. B. S. (1956). The estimation and significance of the logarithm of a ratio of frequencies. Annals of Human Genetics, 20(4), 309–311. doi:10.1111/j.1469-1809.1955.tb01285.x .

Katz et al. 1978. Katz, D., Baptista, J., Azen, S. P., & Pike, M. C. (1978). Obtaining confidence intervals for the risk ratio in cohort studies. Biometrics, 34(3), 469–474. doi:10.2307/2530610 .

Knol 2011. Knol, M. J., Le Cessie, S., Algra, A., Vandenbroucke, J. P., & Groenwold, R. H. H. (2012). Overestimation of risk ratios by odds ratios in trials and cohort studies: alternatives to logistic regression. Canadian Medical Association Journal, 184(8), 895–899. Published online 2011. doi:10.1503/cmaj.101715 .

Pearson 1900. Pearson, K. (1900). 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. Philosophical Magazine, Series 5, 50(302), 157–175. doi:10.1080/14786440009463897 .

Woolf 1955. Woolf, B. (1955). On estimating the relation between blood group and disease. Annals of Human Genetics, 19(4), 251–253. doi:10.1111/j.1469-1809.1955.tb01348.x .

Zou 2004. Zou, G. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702–706. doi:10.1093/aje/kwh090 .

Descriptive statistics and group balance

Altman 1991. Altman, D. G. (1991). Practical Statistics for Medical Research. Chapman & Hall.

Austin 2009. Austin, P. C. (2009). Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine, 28(25), 3083–3107. doi:10.1002/sim.3697 .

Bland & Altman 1996. Bland, J. M., & Altman, D. G. (1996). Statistics notes: Transforming data. BMJ, 312(7033), 770. doi:10.1136/bmj.312.7033.770 .

Yang & Dalton 2012. Yang, D., & Dalton, J. E. (2012). A unified approach to measuring the effect size between two groups using SAS. SAS Global Forum 2012, Paper 335-2012. https://support.sas.com/resources/papers/proceedings12/335-2012.pdf.

Regression models

Firth 1993. Firth, D. (1993). Bias reduction of maximum likelihood estimates. Biometrika, 80(1), 27–38. doi:10.1093/biomet/80.1.27 .

Fox & Monette 1992. Fox, J., & Monette, G. (1992). Generalized collinearity diagnostics. Journal of the American Statistical Association, 87(417), 178–183. doi:10.1080/01621459.1992.10475190 .

Hanley, Negassa, Edwardes & Forrester 2003. Hanley, J. A., Negassa, A., Edwardes, M. D., & Forrester, J. E. (2003). Statistical analysis of correlated data using generalized estimating equations: an orientation. American Journal of Epidemiology, 157(4), 364–375. doi:10.1093/aje/kwf215 .

Heinze & Schemper 2002. Heinze, G., & Schemper, M. (2002). A solution to the problem of separation in logistic regression. Statistics in Medicine, 21(16), 2409–2419. doi:10.1002/sim.1047 .

Long & Ervin 2000. Long, J. S., & Ervin, L. H. (2000). Using heteroscedasticity consistent standard errors in the linear regression model. The American Statistician, 54(3), 217–224. doi:10.1080/00031305.2000.10474549 .

Riley 2019. Riley, R. D., Snell, K. I. E., Ensor, J., et al. (2019). Minimum sample size for developing a multivariable prediction model: Part II - binary and time-to-event outcomes. Statistics in Medicine, 38(7), 1276–1296. doi:10.1002/sim.7992 .

van Smeden 2016. van Smeden, M., de Groot, J. A. H., Moons, K. G. M., et al. (2016). No rationale for 1 variable per 10 events criterion for binary logistic regression analysis. BMC Medical Research Methodology, 16, 163. doi:10.1186/s12874-016-0267-3 .

van Smeden 2019. van Smeden, M., Moons, K. G. M., de Groot, J. A. H., et al. (2019). Sample size for binary logistic prediction models: beyond events per variable criteria. Statistical Methods in Medical Research, 28(8), 2455–2474. doi:10.1177/0962280218784726 .

Ver Hoef & Boveng 2007. Ver Hoef, J. M., & Boveng, P. L. (2007). Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data? Ecology, 88(11), 2766–2772. doi:10.1890/07-0043.1 .

Westreich & Greenland 2013. Westreich, D., & Greenland, S. (2013). The Table 2 fallacy: presenting and interpreting confounder and modifier coefficients. American Journal of Epidemiology, 177(4), 292–298. doi:10.1093/aje/kws412 .

Survival analysis

Cox 1972. Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–202. doi:10.1111/j.2517-6161.1972.tb00899.x .

Grambsch & Therneau 1994. Grambsch, P. M., & Therneau, T. M. (1994). Proportional hazards tests and diagnostics based on weighted residuals. Biometrika, 81(3), 515–526. doi:10.1093/biomet/81.3.515 .

Diagnostic accuracy and ROC curves

Altman et al. 2000. Altman, D. G., Machin, D., Bryant, T. N., & Gardner, M. J. (2000). Statistics with Confidence (2nd ed.). BMJ Books.

Clopper & Pearson 1934. Clopper, C. J., & Pearson, E. S. (1934). The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika, 26(4), 404–413. doi:10.1093/biomet/26.4.404 .

Cohen 1960. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. doi:10.1177/001316446002000104 .

DeLong et al. 1988. DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics, 44(3), 837–845. doi:10.2307/2531595 .

Ewald 2006. Ewald, B. (2006). Post hoc choice of cut points introduced bias to diagnostic research. Journal of Clinical Epidemiology, 59(8), 798–801. doi:10.1016/j.jclinepi.2005.11.025 .

Fleiss, Cohen & Everitt 1969. Fleiss, J. L., Cohen, J., & Everitt, B. S. (1969). Large sample standard errors of kappa and weighted kappa. Psychological Bulletin, 72(5), 323–327. doi:10.1037/h0028106 .

Glas et al. 2003. Glas, A. S., Lijmer, J. G., Prins, M. H., Bonsel, G. J., & Bossuyt, P. M. M. (2003). The diagnostic odds ratio: a single indicator of test performance. Journal of Clinical Epidemiology, 56(11), 1129–1135. doi:10.1016/S0895-4356(03)00177-X .

Hanley & McNeil 1982. Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36. doi:10.1148/radiology.143.1.7063747 .

Robin et al. 2011. Robin, X., Turck, N., Hainard, A., et al. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12, 77. doi:10.1186/1471-2105-12-77 . This is also the source for the "pROC direction = 'auto'" advice tag.

Simel et al. 1991. Simel, D. L., Samsa, G. P., & Matchar, D. B. (1991). Likelihood ratios with confidence: sample size estimation for diagnostic test studies. Journal of Clinical Epidemiology, 44(8), 763–770. doi:10.1016/0895-4356(91)90128-V .

Sensitivity analysis

VanderWeele & Ding 2017. VanderWeele, T. J., & Ding, P. (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine, 167(4), 268–274. doi:10.7326/M16-2607 .

Reporting guidelines

STROBE item 14. von Elm, E., Altman, D. G., Egger, M., et al. (2007). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Medicine, 4(10), e296. doi:10.1371/journal.pmed.0040296 . Item 14 asks for the number of participants with missing data for each variable.

TRIPOD 2015. Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015). Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ, 350, g7594. doi:10.1136/bmj.g7594 .