Author(s)

Arunabh Tripathi¹, Anant Pandey²

¹Department of Biostatistics, AIIMS, New Delhi, India.
²Center of National Injury Surveillance, PGIMER, Dr. RML Hospital, New Delhi, India.

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.

Survival Analysis, Bonferroni Correction, Kaplan-Meier Plot

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.