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TMA Foresight

Kaplan Meier Survival Analysis

 
Glossary on Survival Analysis
Features

  • Hierarchical Clustering: Tissue microarray software can be used to group patients or biomarkers into relatively homogeneous sub-groups based on a set of variables. It identifies prognostically significant clusters of the patients based on biomarkers/clinico-pathological variables. The survival information of patients within each cluster is used to determine whether the clusters formed are significantly different from each other. TMA Foresight enables you to move the linkage bar over the dendogram which updates the Kaplan Meier plot and results of the Log Rank test accordingly. This functionality helps in determining prognostically significant clusters and in identifying high and low risk groups patients within a cohort.

  • Correlation Analysis: This tool measures the strength of association between any two variables. You can also analyze the partial association between two variables by controlling the effect of one or more variables. This functionality may help in understanding the genomic and proteomic level alterations in patients.

  • Principal Component Analysis: This tool reduces the dimensionality of the data set while retaining the variation in the data set as much as possible. TMA Foresight provides an axis to move over the 2D scatter plots to quickly generate clusters.
  • Cox Regression: This multivariate tool is used to identify prognostically significant markers and clinico-pathological parameters that have a significant impact on the outcome. The survival or recurrence function provides information about the risk of death or recurrence of a disease for a cohort.

  • Kaplan-Meier Survival Plot: This tool is used to visualize the Kaplan Meier survival and recurrence rate for a cohort. You can parstition the data based on a single variable and compare the survival functions. The significance of difference in the Kaplan Meier survival rates for a cohort can be tested using the log-rank test.

  • Test of Independence: To study the likelihood of two categorical variables being dependent on each other, TMA Foresight allows you to run Fisher's exact test or Chi-square test. This enables you to accept or reject the null hypothesis for association between any two biomarkers.

  • Descriptive Statistics: TMA Foresight calculates the mean, standard deviation and displays the range of different parameters. The information helps you quickly identify any abnormalities in the data.

  • Project Management: TMA Foresight organizes your data so that you can easily access it. The reports and plots generated are linked to the data from which they are derived.

  • Mapping: TMA data is usually both quantitative and qualitative. The qualitative variables may be character or alphanumeric. For any kind of analysis such variables need to be transformed to a numeric scale. TMA Foresight helps map character data to numeric values with a click of a button, so that you do not have to bother with entering the data yourself. You can even define the measurement level of each variable.

  • Replacing Missing Values: TMA Foresight assists in replacing the missing values for biomarkers or clinico-pathological parameters based on their measurement levels. This ensures the completeness of data for further analysis.

  • Data Filtering: This tool allows you to filter the data set based on certain set of conditions that help you to accomplish specific research goals.
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