| Foundational Principles/Basics | Kaplan-Meier Estimator | Cox Proportional Hazards Model | Applications and Real-World Examples | Advanced Topics |
|---|---|---|---|---|
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What is the primary objective of survival analysis?
To analyze time-to-event data.
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Describe the Kaplan-Meier estimator and its purpose in survival analysis.
A non-parametric method to estimate the survival function.
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What is the Cox proportional hazards model, and how does it work?
A semi-parametric model to assess the relationship between covariates and survival time.
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Name three fields where survival analysis is commonly applied and provide an example for each.
Medical research - analyzing patient survival rates, finance - analyzing time to default, etc.
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What are competing risks in survival analysis, and how do they impact the analysis?
They occur when there are multiple possible outcomes that may prevent the event of interest.
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Define censoring and explain its significance in survival analysis.
Censoring occurs when the event of interest is not observed for some subjects.
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What is the interpretation of the Kaplan-Meier curve?
It shows the probability of survival over time.
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Explain the concept of hazard ratios in the Cox model.
It represents the ratio of hazards between two groups.
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How is survival analysis used in medical research?
To assess treatment effectiveness or disease prognosis.
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Explain the concept of time-varying covariates in survival analysis.
Covariates whose values change over time and affect the hazard rate.
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What are the key assumptions of survival analysis?
Proportional hazards assumption, non-informative censoring, etc.
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How does the Kaplan-Meier estimator handle censored data?
It calculates survival probabilities at each censored time point.
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What assumptions are made in the Cox proportional hazards model?
Proportional hazards assumption, etc.
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Can survival analysis be applied in business or marketing? If so, how?
Yes, for customer churn analysis or time to conversion analysis.
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What is frailty modelling, and when is it used in survival analysis?
It incorporates unobserved heterogeneity into survival models, often used in longitudinal studies.
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Explain the difference between hazard rate and survival function.
Hazard rate is the instantaneous rate of failure at a given time, while survival function is the probability of surviving beyond that time.
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What are the limitations of the Kaplan-Meier estimator?
Cannot estimate hazard ratios, assumes independence between censoring and survival times, etc.
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How are covariates incorporated into the Cox model?
By estimating regression coefficients for each covariate.
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Describe a real-world scenario where survival analysis helped make significant decisions.
Clinical trials determining drug efficacy.
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Describe the difference between parametric and non-parametric survival models.
Parametric models assume a specific distribution for survival times, while non-parametric models make no assumptions about the distribution.
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How does survival analysis handle incomplete data?
It utilizes statistical techniques like Kaplan-Meier estimation or Cox proportional hazards modelling to accommodate censored observations.
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Provide an example of when you might use the Kaplan-Meier estimator.
In clinical trials to estimate survival rates.
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When might you choose to use the Cox model over other survival analysis techniques?
When the proportional hazards assumption holds, and you want to assess the impact of multiple covariates.
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How does survival analysis contribute to understanding disease progression?
By estimating survival probabilities and identifying factors influencing disease progression.
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How does machine learning intersect with survival analysis, and what are some emerging techniques?
Machine learning methods can be used to predict survival outcomes or identify complex relationships between predictors and survival.
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