The goal of survival analysis is to establish a link between covariates and time to event. Survival analysis is a major tool used in clinical trials, and all the precautions needed for a successful trial need to be followed or else the statistical analysis will be fruitless. Survival analysis is a regression problem (where one wants to predict a continuous value), but with a twist. It differs from traditional regression in that some of the training data can only be partially observed. CD ComputaBio now offers professional survival analysis service to meet your research needs.

Survival Analysis Service Process

1. Describe the survival process

• We can help you study the distribution characteristics of survival time, estimate survival rate and mean survival time, and plot survival curves, etc.
• Based on the length of survival time, we can estimate the survival rate at each time point, and estimate the median survival time based on the survival rate.
• The Kaplan-Meier method and the life table method are generally used to analyze the survival characteristics based on the survival curves.

Figure 1Kaplan-Meier plot for overall survival. (Despina Koletsi, et al. 2017)

2. Comparison of survival over

We compared the survival rates of each sample by survival rates and their standard errors to explore whether there were differences in the survival course between groups, generally using Log-rank test and Breslow test.

3. Analysis of risk factors

We used survival analysis model to explore the protective and unfavorable factors affecting survival time and endpoint events, the magnitude and direction of factor effects, and the magnitude of relative risk, basically using Cox regression model.

4. Building mathematical models

We will build the final mathematical model for you, which is also done by Cox regression model.

The basic requirements of our survival analysis for the sample

1. The sample is obtained by random sampling method, and there should be a certain number of cases and proportion of deaths should not be too small.
2. The proportion of the complete data of the peal should not be too small.
3. The truncated values appear for unbiased reasons, and to prevent bias often analyze the age, occupation, region, and severity of disease of the truncated study subjects.
4. Survival time should be as accurate as possible.
5. Missing items should be filled in as much as possible.

Our Capabilities

CD ComputaBio offers you a complete and optimized survival analysis service. Through strict quality control and advanced computing platforms, we can help you with your survival analysis services.

Our Advantages

• CD ComputaBio offers a flexible and comprehensive approach to all services related to survival services.
• Our team works with scientists, QA/QC professionals and project managers from many pharmaceutical and biotech companies and organizations around the world.
• CD ComputaBio will complete your project on time, efficiently and to your specifications.

Applications

• To describe the survival times of members of a group
• Life tables
• Kaplan–Meier curves
• Survival function
• Hazard function
• To compare the survival times of two or more groups
• Log-rank test
• To describe the effect of categorical or quantitative variables on survival
• Cox proportional hazards regression
• Parametric survival models
• Survival trees
• Survival random forests

CD ComputaBio is a high-tech company focused on computational biology. We are dedicated to provide professional survival analysis solutions. If you have a need in computational biology, please feel free to contact us.

• Biological Model Service
• Statistical Data Analysis and Programming
• Data Visualization Service