Survival Analysis

CD Genomics relies on its strong professional advantages to provide clinicians and scientific researchers with comprehensive and high-quality data statistical analysis services. With years of experience in statistical analysis projects, CD Genomics provides one-stop survival analysis services to help customers analyze the phenomena and results of events in clinical medicine and the time of clinical phenomena and results, so as to analyze and infer the survival time of organisms or people.

Introduction

Survival analysis refers to the method of analyzing and inferring the survival time of organisms or humans based on the data obtained from experiments or investigations, and studying the relationship between survival time and outcome and many influencing factors and their degree. It is also called survival rate analysis. Survival analysis of clinical medical data is a statistical analysis method that combines the phenomena and results of events in clinical medicine (end-point events) and the time experienced by clinical phenomena and results.

The Purpose of Survival Analysis

Fig 1. The purpose of survival analysis.

• Describe the survival process: Predict the overall survival rate at different times, calculate the median survival time, and draw the survival function curve. Statistical methods include Kaplan-Meier (K-M) method and life table method.
• Comparative study results: Compare the survival rates of different treatment groups, such as comparing the survival rates of different therapies for tumors, to understand which treatment plan is better. Statistical methods include log-rank test and so on.
• Analysis of influencing factors: Study the effect of one or some factors on survival rate or survival time. To improve the prognosis of patients with tumors, the main factors affecting the prognosis of patients should be understood, including the patient’s age, gender, course of disease, tumor stage, treatment plan, etc.
• Survival risk prediction: Establish a cox regression prediction model.

Applications of Survival Analysis

1. Estimate the survival rate and median survival time of each survival time.
2. Draw various curves, such as survival function, risk function curve, etc.
3. Compare the survival time of a certain research factor at different levels.
4. After controlling a certain stratification factor, compare the survival time distribution of different levels of research factors.
5. Make pairwise comparisons of the survival time distribution of multiple groups.

Advantages of CD Genomics

• Efficient computing power: ultra-large-scale computing resources, massive throughput, ultra-strong generalization, and extremely fast data analysis
• Professional analysis team: a data analysis team with rich experience in project analysis.
• Safety standard: high-standard data analysis process and safety standard.
• Cutting-edge algorithm model: Use machine learning, deep learning, intelligent analysis of complex data logic, and explore the nature of data.
• Fast turnaround cycle: Fast analysis cycle speeds up your research process.

What We Offer

In order to fully cover the data analysis needs of clinicians or scientific researchers, CD Genomics provides one-stop survival analysis services, and can select different analysis algorithms according to the customer's survival analysis purpose and the original data to obtain accurat analysis results. In addition to providing survival analysis, we also provide data analysis services such as cluster analysis for multivariate data, comparative analysis of measurement data, regression analysis, correlation analysis, and principal component analysis. For our data analysis services, if you have any questions, please feel free to contact our professional technical support. We are always ready to provide you with satisfactory services.

Service Process

* For research use only. Not for use in clinical diagnosis or treatment of humans or animals.