Survival Analysis Techniques in Public Health Research

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Description

This exam evaluates survival analysis techniques pivotal in public health research, concentrating on time-to-event data and statistical modeling approaches.

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Exam Details

Duration: 1 hour

Prerequisites: Biostatistics, Advanced Epidemiology

Key Topics

  • Survival Analysis
  • Kaplan-Meier Estimation
  • Cox Proportional Hazards
  • Time-to-Event Data

Learning Outcomes

  • Explain Survival Analysis Concepts
  • Interpret Kaplan-Meier Curves
  • Discuss Cox Proportional Hazards
  • Analyze Time-to-Event Data

Full Description

This exam focuses on survival analysis techniques that are critical for researching time-to-event data in public health, including Kaplan-Meier estimates and Cox proportional hazards models.

Survival analysis is vital in understanding disease progression and patient outcomes over time, allowing public health practitioners to assess prognostic factors and treatment effects. Mastery of these techniques is essential for effective health research.

The examination will assess the candidate's verbal capability to explain different survival analysis methods, interpret associated statistics, and discuss their implications in public health contexts.

Candidates may also be tasked with evaluating examples of survival analysis studies to illustrate concepts learned.

Sample Questions

  • What is the purpose of the Kaplan-Meier estimator in survival analysis?
  • How do you interpret the results of a Cox proportional hazards model?

Field: Medical and Health Sciences

Subfield: Biostatistics

Specialization: Public Health Biostatistics

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