Modeling Time in Medical Education Research: The Potential of New Flexible Parametric Methods of Survival Analysis


Time – the duration of a certain process or the timing of a specified event – plays a central role in many situations in medical research. Waiting time analysis (“survival analysis”) is a field of statistics providing the tools for solving the unique problems of such studies. In particular, waiting time analysis correctly handles the typical positively skewed distributions of waiting times as well as censored observations on study subjects for whom the target event does not occur before data collection ends. For decades, non-parametric Kaplan-Meier analysis and semiparametric Cox regression despite some inherent limitations have dominated waiting time analysis in medical contexts, while parametric models, although in principle offering important theoretical advantages, were scarcely applied in practice because of lacking flexibility. Recently, however, new flexible parametric methods (Royston-Parmar models) became available offering exciting new research potential. Surprisingly, although medical education research deals with a range of typical problems suited for waiting time analysis, the methods were rarely used in the past. By re-analyzing data from a previous investigation on study dropout of medical students, this is the first study demonstrating the usefulness and practical applications of waiting time analysis with special emphasis on Royston-Parmar models in a medical education research environment.

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Reibnegger, G. (2012). Modeling Time in Medical Education Research: The Potential of New Flexible Parametric Methods of Survival Analysis. Creative Education, 3, 916-922. doi: 10.4236/ce.2012.326139.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716-723. doi:10.1109/TAC.1974.1100705
[2] Andersson, T. M. L., Dickman, P. W., Eloranta, S., & Lambert, P. C. (2011). Estimating and modeling cure in population-based cancer studies within the framework of flexible parametric survival models. BMC Medical Research Methodology, 11, 96. doi:10.1186/1471-2288-11-96
[3] Bennett, S. (1983). Analysis of survival data by the proportional odds model. Statistics in Medicine, 2, 273-277. doi:10.1002/sim.4780020223
[4] Breslow, N. (1970). A generalized Kruskal-Wallis test for comparing K samples subject to unequal patterns of censorship. Biometrika, 57, 579-594. doi:10.1093/biomet/57.3.579
[5] Brown, B. W. Jr. (1982). Estimation in survival analysis: Parametric models, product-limit and life-table methods. In V. Miké, & K. E. Stanley (Eds.), Statistics in medical research. Methods and issues, with applications in cancer research (pp. 317-339). New York, Chichester, Brisbane, Toronto, Singapore: John Wiley and Sons.
[6] Cleves, M., Gutierrez, R., Gould, W., & Marchenko, Y. (2008). An introduction to survival analysis using stata (2nd ed.). College Station, TX: StataCorp LP., Stata Press.
[7] Colzani, E., Liljegren, A., Johansson, A. L. V., Adolfsson, J., Hellborg, H., Hall, P. F. L., & Czene, K. (2011). Prognosis of patients with breast cancer: Causes of death and effects of time since diagnosis, age, and tumor characteristics. Journal of Clinical Oncology, 29, 4014- 4021. doi:10.1200/JCO.2010.32.6462
[8] Cox, D. R. (1972). Regression models and life tables (with discussion). Journal of the Royal Statistical Society, 34, 187-220.
[9] De Champlain, A., Sample, L., Dillon, G. F., & Boulet, J. R. (2006). Modeling longitudinal performances on the United States Medical Licensing Examination and the impact of sociodemographic covariates: An application of survival data analysis. Academic Medicine, 81, S108-S111. doi:10.1097/00001888-200610001-00027
[10] Hjort, N. L. (1992). On inference in parametric survival data models. International Statistical Review, 60, 355-387. doi:10.2307/1403683
[11] Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53, 457-481. doi:10.1080/01621459.1958.10501452
[12] Kleinbaum, D. G., & Klein, M. (2005). Survival analysis: A self-learning text (2nd ed.). New York, NY: Springer Science, Business Media, LLC.
[13] Muller, H.-G., & Wang, J.-L. (1994). Hazard rate estimation under random censoring with varying kernels and bandwidths. Biometrics, 50, 61-76. doi:10.2307/2533197
[14] Nelson, C. P., Lambert, P. C., Suire, I. B., & Jones, D. R. (2007). Flexible parametric models for relative survival, with application in coronary heart disease. Statistics in Medicine, 26, 5486-5498. doi:10.1002/sim.3064
[15] O’Neill, L. D., Wallstedt, B., Eika, B., & Hartvigsen, J. (2011). Factors associated with dropout in medical education: A literature review. Medical Education, 45, 440-454. doi:10.1111/j.1365-2923.2010.03898.x
[16] Reibnegger, G., Caluba, H.-C., Ithaler, D., Manhal, S., Neges, H. M., & Smolle, J. (2010). Progress of medical students after open admission or admission based on knowledge tests. Medical Education, 44, 205- 214. doi:10.1111/j.1365-2923.2009.03576.x
[17] Reibnegger, G., Caluba, H.-C., Ithaler, D., Manhal, S., Neges, H. M., & Smolle, J. (2011). Dropout rates in medical students at one school before and after the installation of admission tests in Austria. Academic Medicine, 86, 1040-1048. doi:10.1097/ACM.0b013e3182223a1b
[18] Ries, A., Wingard, D., Morgan, C., Farrell, E., Letter, S., & Reznik, V. (2009). Retention of junior faculty in academic medicine at the University of California, San Diego. Academic Medicine, 84, 37-41. doi:10.1097/ACM.0b013e3181901174
[19] Royston, P., & Lambert, P. C. (2011). Flexible parametric survival analysis using stata: Beyond the Cox model. College Station, TX: StataCorp LP., Stata Press.
[20] Royston, P., & Parmar, M. K. B. (2002). Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Statistics in Medicine, 21, 2175-2197. doi:10.1002/sim.1203
[21] Schwarz, G. (1978) Estimating the dimension of a model. Annals of Statistics, 6, 461-464. doi:10.1214/aos/1176344136

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