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BioStats > Survival Analysis > Flashcards

Survival Analysis Flashcards

1
Q

What is survival analysis used for?

A

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

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2
Q

Explain the concept of censored data in survival analysis.

A

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

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3
Q

What is the Kaplan-Meier estimator?

A

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

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4
Q

Describe how the Cox proportional hazards model works.

A

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

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5
Q

What are hazard ratios, and how are they interpreted?

A

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

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6
Q

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

A

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

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7
Q

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

A

It compares the survival distributions of two or more groups.

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8
Q

How do you handle ties in survival data?

A

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

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9
Q

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

A

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

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10
Q

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

A

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

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11
Q

What role does stratification play in survival analysis?

A

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

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12
Q

How do you adjust for confounding variables in survival analysis?

A

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

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13
Q

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

A

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

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14
Q

How do you assess model fit in survival analysis?

A

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

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15
Q

What are the assumptions underlying the Kaplan-Meier method?

A

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

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16
Q

What methods are used to compare multiple survival curves?

A

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

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17
Q

How is the cumulative hazard function calculated?

A

As the integral of the hazard function over time.

18
Q

What is parametric survival analysis?

A

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

19
Q

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

A

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

20
Q

How do you interpret survival function plots?

A

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

21
Q

What is accelerated failure time modeling?

A

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

22
Q

How do you deal with missing data in survival analysis?

A

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

23
Q

What statistical software tools are commonly used for survival analysis?

A

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

24
Q

How do competing risks influence the interpretation of survival probabilities?

A

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

25
Q

What is interval censoring, and how is it handled?

A

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

26
Q

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

A

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

27
Q

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

A

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

28
Q

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

A

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

29
Q

How can Bayesian methods be applied to survival analysis?

A

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

30
Q

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

A

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

31
Q

How do you estimate the survival function using life tables?

A

Using grouped data to estimate survival probabilities at certain times.

32
Q

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

A

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

33
Q

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

A

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

34
Q

What are the common pitfalls in interpreting survival analysis results?

A

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

35
Q

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

A

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

36
Q

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

A

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

37
Q

How do you validate a survival analysis model?

A

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

38
Q

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

A

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

39
Q

How do you handle outliers in survival data?

A

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

40
Q

What are the ethical considerations in conducting survival analysis?

A

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

BioStats (14 decks)

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