Survival Analysis

Question 1

What is survival analysis used for?

Answer 1

It analyzes time-to-event data, focusing on the duration until one or more events of interest occur.

Question 2

Explain the concept of censored data in survival analysis.

Answer 2

Censored data occurs when the information about an individual’s time to event is incomplete.

Question 3

What is the Kaplan-Meier estimator?

Answer 3

It estimates the survival function from lifetime data and can accommodate censored cases.

Question 4

Describe how the Cox proportional hazards model works.

Answer 4

It models the hazard rate as a function of baseline hazard and covariates, assuming proportional hazards.

Question 5

What are hazard ratios, and how are they interpreted?

Answer 5

Hazard ratios compare the hazard rates between two groups; values greater than 1 indicate higher risk in the treatment group compared to the control.

Question 6

How do you test for the proportionality assumption in the Cox model?

Answer 6

By assessing whether the log(-log(survival)) plot is parallel across groups.

Question 7

What is the log-rank test, and what does it test for?

Answer 7

It compares the survival distributions of two or more groups.

Question 8

How do you handle ties in survival data?

Answer 8

Handling ties can be done by adjusting the risk sets or using specific tie-handling methods in the Cox model.

Question 9

What is the difference between fixed and random effects in survival analysis?

Answer 9

Fixed effects are the same across individuals, while random effects vary and can include random variations not explained by the model.

Question 10

How can you include time-dependent covariates in a Cox model?

Answer 10

By including them with time-varying coefficients or using stratified or extended Cox models.

Question 11

What role does stratification play in survival analysis?

Answer 11

Stratification allows adjusting the analysis for variables that violate the proportional hazards assumption.

Question 12

How do you adjust for confounding variables in survival analysis?

Answer 12

By including them as covariates in the model or using stratification.

Question 13

What are competing risks, and how do they affect survival analysis?

Answer 13

Competing risks occur when different types of events interfere with the event of interest, necessitating special modeling approaches.

Question 14

How do you assess model fit in survival analysis?

Answer 14

By using diagnostic plots, goodness-of-fit tests, and checking the assumptions of the model.

Question 15

What are the assumptions underlying the Kaplan-Meier method?

Answer 15

Assumptions include independent and identically distributed survival times and no information on censored observations other than being censored.

Question 16

What methods are used to compare multiple survival curves?

Answer 16

Using tests like the log-rank test or Wilcoxon test to determine if there are statistically significant differences between groups.

Question 17

How is the cumulative hazard function calculated?

Answer 17

As the integral of the hazard function over time.

Question 18

What is parametric survival analysis?

Answer 18

It assumes a specific distribution for the event times and estimates parameters of that distribution.

Question 19

What are the implications of violating the proportionality assumption of the Cox model?

Answer 19

It can lead to biased or misleading estimates of the effect size.

Question 20

How do you interpret survival function plots?

Answer 20

They show the probability of surviving past certain time points, with steeper curves indicating quicker declines in survival.

Question 21

What is accelerated failure time modeling?

Answer 21

It models the logarithm of survival time, allowing for different time scales for covariates’ effects.

Question 22

How do you deal with missing data in survival analysis?

Answer 22

By using methods like multiple imputation or weighting approaches to adjust for missing data.

Question 23

What statistical software tools are commonly used for survival analysis?

Answer 23

Tools like R (survival package), SAS (PROC LIFETEST), and Stata are popular.

Question 24

How do competing risks influence the interpretation of survival probabilities?

Answer 24

They complicate the analysis as each type of event must be treated as a potential censoring for the other types.

Question 25

What is interval censoring, and how is it handled?

Answer 25

Occurs when the exact event time is unknown but falls within a certain range; handled using specialized statistical methods.

Question 26

Why is it important to assess the impact of covariates on survival times?

Answer 26

Covariates can provide insights into risk factors and allow for more tailored predictions and treatments.

Question 27

How do you select the appropriate survival model for your data?

Answer 27

By considering the distribution of data, the research question, and the assumptions each model makes.

Question 28

What is the role of goodness-of-fit tests in survival analysis?

Answer 28

Goodness-of-fit tests assess how well the model describes the observed data.

Question 29

How can Bayesian methods be applied to survival analysis?

Answer 29

Bayesian methods allow for incorporating prior knowledge and obtaining full probability distributions of model parameters.

Question 30

What is the Weibull distribution, and how is it used in survival analysis?

Answer 30

It is used for its flexibility in modeling various shapes of hazard functions and is common in reliability engineering.

Question 31

How do you estimate the survival function using life tables?

Answer 31

Using grouped data to estimate survival probabilities at certain times.

Question 32

What are frailty models, and when should they be used?

Answer 32

Frailty models account for unobserved heterogeneity in survival data and are useful when dealing with grouped or clustered data.

Question 33

How do you interpret the results of a multivariate survival analysis?

Answer 33

By analyzing the effect sizes and confidence intervals of the covariates included in the model.

Question 34

What are the common pitfalls in interpreting survival analysis results?

Answer 34

Common pitfalls include ignoring censored data, misinterpreting the effects of covariates, and failing to check model assumptions.

Question 35

How does the choice of time scale affect the results of survival analysis?

Answer 35

The choice affects the interpretation of times, such as age as time scale versus time-on-study.

Question 36

What are the benefits of using spline functions in survival models?

Answer 36

Spline functions can model complex hazard functions more flexibly than traditional methods.

Question 37

How do you validate a survival analysis model?

Answer 37

Through techniques like cross-validation, bootstrapping, or external validation with new data sets.

Question 38

What are landmark analysis and conditional survival, and how are they used?

Answer 38

Landmark analysis focuses on survival from a specific 'landmark' time point; conditional survival estimates survival probabilities conditional on having survived to a specific time.

Question 39

How do you handle outliers in survival data?

Answer 39

Outliers can be addressed by robust estimation techniques or sensitivity analysis to understand their impact.

Question 40

What are the ethical considerations in conducting survival analysis?

Answer 40

Ensuring informed consent, protecting privacy, and responsibly handling sensitive data are key considerations.