JBM  Vol.5 No.3 , March 2017
Post-Hoc Comparison in Survival Analysis: An Easy Approach
Abstract: Survival studies mainly deal with distribution of time to event. Often in such studies researchers are interested in comparing several treatment or prognostic groups. At the time of analysis, there is an unmeasured chance of making type I error, or finding a falsely significant difference between any two groups. The chance of making type I error is increased, if multiple groups are compared simultaneously. In this paper, survival analysis with Bonferroni correction is explained in easy way to cope up with this issue. The DLHS-3 data are taken to explain this methodology in the context of neonatal survival. Kaplan-meier plot with three survival comparison test is used to elaborate the application of Bonferroni correction.
Cite this paper: Tripathi, A. and Pandey, A. (2017) Post-Hoc Comparison in Survival Analysis: An Easy Approach. Journal of Biosciences and Medicines, 5, 112-119. doi: 10.4236/jbm.2017.53012.

[1]   Kleinbaum, D.G. (1996) Survival Analysis: A Self-Learning Text. Springer-Verlag, New York.

[2]   Hosmer, D.W. and Lemeshow, S. (1999) Applied Survival Analysis. John Wiley and Sons, New York.

[3]   Saville, D.J. (1990) Multiple Comparison Procedures: The Practical Solution. The American Statistician, 44, 174-180.

[4]   Toothaker, L.E. (1993) Multiple Comparison Procedures. No. 89, Sage, Thousand Oaks.

[5]   Abdi, H. and Williams, L.J. (2010) Turkey’s Honestly Significant Difference (HSD) test. Encyclopedia of Research Design. Sage, Thousand Oaks, 1-5.

[6]   Scheffe, H. (1999) The Analysis of Variance. Vol. 72, John Wiley & Sons, Hoboken.

[7]   Duncan, D.B. (1955) Multiple Range and Multiple F Tests. Biometrics, 11, 1-42.

[8]   Kleinbaum, D.G. and Klein, M. (2012) Survival Analysis: A Self-Learning Text. 3rd Edition, Springer, New York.

[9]   Mantel (1966) Evaluation of Survival Data and Two New Rank Order Statistics Arising in Its Consideration. Cancer Chemotherapy Reports, 50, 163-170.

[10]   Breslow, A. (1970) Thickness, Cross-Sectional Areas and Depth of Invasion in the Prognosis of Cutaneous Melanoma. Annals of Surgery, 172, 902-908.

[11]   Gehan, E. (1965) A Generalized Wilcoxon Test for Comparing Arbitrarily Singly-Censored Samples. Biometrika, 52, 203-223.

[12]   Tarone, R.E. and Ware, J. (1977) On Distribution-Free Tests for Equality of Survival Distributions. Biometrika, 64, 156-160.

[13]   Abdi, H. (2007) The Bonferonni and Sidák Corrections for Multiple Comparisons.

[14]   DLHS-3 District Level Health Survey 3. Conducted by “International Institute for Population Sciences” on Sample Basis in All over India during the Period 2007 to 2008.

[15]   Singh, G.P., et al. (2013) Factors Affecting Neonatal Mortality in Uttar Pradesh. Proceeding of Conference on Topic “Emerging Applications of Bayesian Statistics and Stochastic Modeling”, 55-65.

[16]   Ryan, T.H. (1960) Significance Tests for Multiple Comparisons of Proportions, Variances, and Other Statistics. Psychological Bulletin, 57, 318-328.

[17]   Games, P.A. and Howell, J.F. (1976) Pairwise Multiple Comparison Procedures with Unequal N’s and/or Variances: A Monte Carlo Study. Journal of Educational and Behavioural Statistics, 1, 113-125.

[18]   Richter, S.J. and McCann, M.H. (2012) Using the Tukey-Kramer Omnibus Test in the Hayter-Fisher Procedure. British Journal of Mathematical and Statistical Psychology, 65, 499-510.