References
- Andridge RR, Little RJ. 2010. A review of hot deck imputation for survey non-response. Int Stat Rev. 78(1):40–64. doi:https://doi.org/10.1111/j.1751-5823.2010.00103.x.
- Barnett AG, McElwee P, Nathan A, Burton NW, Turrell G. 2017. Identifying patterns of item missing survey data using latent groups: an observational study. BMJ Open. 7(10):e017284. doi:https://doi.org/10.1136/bmjopen-2017-017284.
- Berglund P, Heeringa SG. 2014. Multiple imputation of missing data using SAS. SAS Institute. https://www.sas.com/store/prodBK_65370_en.html?storeCode=SAS_US
- Buuren Van S, Groothuis-Oudshoorn K. 2011. mice: multivariate Imputation by Chained Equations in R. J Stat Softw. 45(1):1–67.
- Cheema JR. 2014. A review of missing data handling methods in education research. Rev Educ Res. 84:487–508. doi:https://doi.org/10.3102/0034654314532697.
- Cresswell SL. 2009. Possible early signs of athlete burnout: a prospective study. J Sci Med Sport. 12:393–398. doi:https://doi.org/10.1016/j.jsams.2008.01.009.
- D’Agostino RB Jr, Rubin DB. 2000. Estimating and using propensity scores with partially missing data. J Am Stat Assoc. 95(451):749–759. doi:https://doi.org/10.1080/01621459.2000.10474263.
- European Medicines Agency. Guideline on missing data in confirmatory clinical trials. 2010; EMA/CPMP/EWP/1776/99 R1:1–12.
- Heymans MW, Eekhout I. Applied missing data analysis with SPSS and (R) Studio. 2019. https://bookdown.org/mwheymans/bookmi/
- Jakobsen JC, Gluud C, Wetterslev J, Winkel P. 2017. When and how should multiple imputation be used for handling missing data in randomised clinical trials–a practical guide with flowcharts. BMC Med Res Methodol. 17:162. doi:https://doi.org/10.1186/s12874-017-0442-1.
- Jeličić H, Phelps E, Lerner RM. 2009. Use of missing data methods in longitudinal studies: the persistence of bad practices in developmental psychology. Dev Psychol. 45(4):1195–1199. doi:https://doi.org/10.1037/a0015665.
- Kowarik A, Templ M. 2016. Imputation with the R Package VIM. J Stat Softw. 74(7):1–16. doi:https://doi.org/10.18637/jss.v074.i07.
- Little RJ, D’Agostino R, Cohen ML, Dickersin K, Emerson SS, Farrar JT, Frangakis C, Hogan JW, Molenberghs G, Murphy SA, et al. 2012. The prevention and treatment of missing data in clinical trials. N Engl J Med. 367(14):1355–1360. doi:https://doi.org/10.1056/NEJMsr1203730.
- Little RJ, Rubin DB. 2019. Statistical analysis with missing data. John Wiley & Sons.
- McElreath R. 2020. Statistical rethinking: a Bayesian course with examples in R and Stan. 2nd.
- Moher D, Liberati A, Tetzlaff J, Altman DG, Group TP. 2009. Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. PLoS Med. 6(7):e1000097. doi:https://doi.org/10.1371/journal.pmed.1000097.
- Nakagawa S, Freckleton RP. 2008. Missing inaction: the dangers of ignoring missing data. Trends Ecol Evol. 23:592–596. doi:https://doi.org/10.1016/j.tree.2008.06.014.
- Newman DA. 2014. Missing data: five practical guidelines. Organ Res Methods. 17:372–411. doi:https://doi.org/10.1177/1094428114548590.
- Peugh JL, Enders CK. 2004. Missing data in educational research: a review of reporting practices and suggestions for improvement. Rev Educ Res. 74:525–556. doi:https://doi.org/10.3102/00346543074004525.
- Royston P, White IR. 2011. Multiple imputation by chained equations (MICE): implementation in Stata. J Stat Softw. 45(4):1–20. doi:https://doi.org/10.18637/jss.v045.i04.
- Rubin DB. 1976. Inference and missing data. Biometrika. 63(3):581–592. doi:https://doi.org/10.1093/biomet/63.3.581.
- Sainani KL. 2015. Dealing with missing data. Phys Med Rehabil. 7:990–994.
- Sainani KL, Borg DN, Caldwell AR, et al.2020. Call to increase statistical collaboration in sports science, sport and exercise medicine and sports physiotherapy. Br J Sports Med. 54(1):1–5. doi:https://doi.org/10.1136/bjsports-2019-100608
- Sampson JA, Murray A, Williams S, et al.2018. Injury risk-workload associations in NCAA american college football. J Sci Med Sport. 21:1215–1220. doi:https://doi.org/10.1016/j.jsams.2018.05.019
- Schafer JL, Graham JW. 2002. Missing data: our view of the state of the art. Psychol Methods. 7:147–177. doi:https://doi.org/10.1037/1082-989X.7.2.147.
- Scheffer J. 2002. Dealing with missing data. Res Lett Inf Math Sci. 3:153–160.
- Sterne JC, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, Wood AM, Carpenter JR. 2009. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 338:b2393. doi:https://doi.org/10.1136/bmj.b2393.
- Tierney N. 2017. Visdat: visualising whole data frames. J Open Sour Software. 2:355. doi:https://doi.org/10.21105/joss.00355.
- Tierney N, Cook D, McBain M, Fay C, O’Hara-Wild M, others. Naniar: data structures, summaries, and visualisations for missing data. R Package version 0.6.0. 2019.
- Tierney NJ, Cook DH. 2018. Expanding tidy data principles to facilitate missing data exploration, visualization and assessment of imputations. arxiv.org. arXiv preprint arXiv:180902264.
- Tierney NJ, Harden FA, Harden MJ, Mengersen KL. 2015. Using decision trees to understand structure in missing data. BMJ Open. 5:e007450. doi:https://doi.org/10.1136/bmjopen-2014-007450.
- Van Buuren S. 2012. Flexible imputation of missing data. New York: CRC Press.
- Wickham H, François R, Henry L, Müller K (2021). dplyr: a grammar of data manipulation. R package version 1.0.4. https://CRAN.R-project.org/package=dplyr
- Wood AM, White IR, Thompson SG Are missing outcome data adequately handled? A review of published randomized controlled trials in major medical journals. Clin Trials. 2004;1:368–376.
- Young C, Luo W, Gastin P, Tran J, Dwyer D. 2019a. Modelling match outcome in australian football: improved accuracy with large databases. Int J Comp Sci Sport. 18(1):80–92. doi:https://doi.org/10.2478/ijcss-2019-0005.
- Young CM, Luo W, Gastin P, Tran J, Dwyer DB. 2019b. The relationship between match performance indicators and outcome in australian football. J Sci Med Sport. 22:467–471. doi:https://doi.org/10.1016/j.jsams.2018.09.235.