Survival analysis is a statistical technique used for estimating the survival/disease recurrence of the patients under study. The term survival analysis is typically used in biomedical sciences where the time to death of patients or animals is observed. Multivariate analytical tools such as Cox proportional hazard model are used to study the impact of biomarkers on the clinical outcome. Such techniques help in identifying important biomarkers. Kaplan Meier survival analysis is another such tool. It is used for comparative analysis of survival rates across cohorts. For a cohort, patients can be grouped according to a particular prognostically significant marker or a clinico-pathological parameter.
Survival analysis helps in identifying biomarkers that are prognostically significant. Survival analysis also helps in categorizing patients into high and low risk groups. Based on this demarcation, homogeneous groups of patients are identified. The groups can be administered a common drug or treatment to determine their efficacy.
Previously used methods of identifying protein expression patterns involved studying specimens from just one patient. With Tissue microarrays (TMA) it is now possible to screen protein expression patterns in large number of tumors of different patients. Tissue microarrays coupled with statistical techniques such as Kaplan-Meier survival analysis help in accurate identification of clinico-pathological parameters and important biomarkers that influence the outcome.
TMA Foresight enables you to perform Kaplan-Meier survival analysis for classifying patients into distinct groups based on a particular biomarker or clinico-pathological parameter. The prognostic relevance of clusters is determined using log rank tests. The high and low risk groups are identified. The analysis is collated with the expression level of the biomarker in each group.
Survival Analysis using Kaplan Meier survival plots and log-rank test