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survival analysis

Last updated June 10, 2026, generated by Sumble

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What is survival analysis?

Survival analysis is a branch of statistics that deals with the analysis of time until an event occurs. This 'event' can be death, failure of a machine, or any other defined outcome of interest. It focuses on estimating the survival function (the probability of surviving beyond a certain time), comparing survival experiences between groups, and assessing the impact of predictor variables on survival time. Common techniques include Kaplan-Meier estimation, Cox proportional hazards regression, and parametric survival models. Survival analysis is used extensively in medical research, engineering, and economics.

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What other technologies are related to survival analysis?

survival analysis Competitor Technologies

Logistic Regression

Logistic Regression is a competing statistical method for classification and prediction, although less flexible in handling complex non-linear relationships compared to survival analysis.

Random Forest is a competitor, serving as an alternative machine learning method for prediction and classification tasks. It offers a non-parametric approach that can capture complex relationships, but is more black box in nature.

neural networks

Neural networks are a competing family of machine learning models that can perform both prediction and classification. The depth and complexity of neural networks allow them to capture complex relationships, making them a powerful alternative to survival analysis, especially when high accuracy is the main concern.

Decision trees are a competing approach for prediction and classification. They provide an interpretable alternative, though often less accurate than Random Forests or Neural Networks.

XGBoost (Extreme Gradient Boosting) is a competing method for prediction and classification, known for its high performance and flexibility. It is often used as a benchmark in machine learning competitions, making it a strong alternative to survival analysis for certain prediction tasks.

Number of organizations that mention technology

survival analysis

Logistic Regression

neural networks

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survival analysis Complementary Technologies

R is a complementary statistical programming language widely used for survival analysis and related statistical tasks. Packages like 'survival' and 'survminer' provide comprehensive tools for survival modeling.

SAS is a complementary statistical software suite that includes modules for survival analysis, offering a range of statistical tools. SAS is widely utilized in industry, and provides very detailed analysis tools, and is often the standard in certain regulated industries.

SQL (Structured Query Language) is complementary for data retrieval and manipulation, often used to prepare data for survival analysis. It is used to query and manage datasets that are fed into survival analysis tools.

Number of organizations that mention technology

survival analysis

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Which job functions commonly mention survival analysis?

0.3% of all Statistician jobs mention survival analysis

View 132 jobs on Sumble

123 Data Scientist jobs mention survival analysis

View 123 jobs on Sumble

7 Immunologist jobs mention survival analysis

View 7 jobs on Sumble

Data, Analytics & Machine Learning

321 Data, Analytics & Machine Learning jobs mention survival analysis

View 321 jobs on Sumble

Financial Analyst

45 Financial Analyst jobs mention survival analysis

View 45 jobs on Sumble

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Which organizations are mentioning survival analysis?

Bristol Myers Squibb

Manufacturing

9 team
mention survival analysis

65 people
use survival analysis

Roche

Health Care and Social Assistance

6 team
mention survival analysis

40 people
use survival analysis

GSK

Health Care and Social Assistance

5 team
mention survival analysis

78 people
use survival analysis

AbbVie

Manufacturing

4 team
mention survival analysis

78 people
use survival analysis

Merck

Health Care and Social Assistance

3 team
mention survival analysis

75 people
use survival analysis

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