Survival Analysis Flashcards
What is survival analysis used for?
It analyzes time-to-event data, focusing on the duration until one or more events of interest occur.
Explain the concept of censored data in survival analysis.
Censored data occurs when the information about an individual’s time to event is incomplete.
What is the Kaplan-Meier estimator?
It estimates the survival function from lifetime data and can accommodate censored cases.
Describe how the Cox proportional hazards model works.
It models the hazard rate as a function of baseline hazard and covariates, assuming proportional hazards.
What are hazard ratios, and how are they interpreted?
Hazard ratios compare the hazard rates between two groups; values greater than 1 indicate higher risk in the treatment group compared to the control.
How do you test for the proportionality assumption in the Cox model?
By assessing whether the log(-log(survival)) plot is parallel across groups.
What is the log-rank test, and what does it test for?
It compares the survival distributions of two or more groups.
How do you handle ties in survival data?
Handling ties can be done by adjusting the risk sets or using specific tie-handling methods in the Cox model.
What is the difference between fixed and random effects in survival analysis?
Fixed effects are the same across individuals, while random effects vary and can include random variations not explained by the model.
How can you include time-dependent covariates in a Cox model?
By including them with time-varying coefficients or using stratified or extended Cox models.
What role does stratification play in survival analysis?
Stratification allows adjusting the analysis for variables that violate the proportional hazards assumption.
How do you adjust for confounding variables in survival analysis?
By including them as covariates in the model or using stratification.
What are competing risks, and how do they affect survival analysis?
Competing risks occur when different types of events interfere with the event of interest, necessitating special modeling approaches.
How do you assess model fit in survival analysis?
By using diagnostic plots, goodness-of-fit tests, and checking the assumptions of the model.
What are the assumptions underlying the Kaplan-Meier method?
Assumptions include independent and identically distributed survival times and no information on censored observations other than being censored.
What methods are used to compare multiple survival curves?
Using tests like the log-rank test or Wilcoxon test to determine if there are statistically significant differences between groups.
How is the cumulative hazard function calculated?
As the integral of the hazard function over time.
What is parametric survival analysis?
It assumes a specific distribution for the event times and estimates parameters of that distribution.
What are the implications of violating the proportionality assumption of the Cox model?
It can lead to biased or misleading estimates of the effect size.
How do you interpret survival function plots?
They show the probability of surviving past certain time points, with steeper curves indicating quicker declines in survival.
What is accelerated failure time modeling?
It models the logarithm of survival time, allowing for different time scales for covariates’ effects.
How do you deal with missing data in survival analysis?
By using methods like multiple imputation or weighting approaches to adjust for missing data.
What statistical software tools are commonly used for survival analysis?
Tools like R (survival package), SAS (PROC LIFETEST), and Stata are popular.
How do competing risks influence the interpretation of survival probabilities?
They complicate the analysis as each type of event must be treated as a potential censoring for the other types.
What is interval censoring, and how is it handled?
Occurs when the exact event time is unknown but falls within a certain range; handled using specialized statistical methods.
Why is it important to assess the impact of covariates on survival times?
Covariates can provide insights into risk factors and allow for more tailored predictions and treatments.
How do you select the appropriate survival model for your data?
By considering the distribution of data, the research question, and the assumptions each model makes.
What is the role of goodness-of-fit tests in survival analysis?
Goodness-of-fit tests assess how well the model describes the observed data.
How can Bayesian methods be applied to survival analysis?
Bayesian methods allow for incorporating prior knowledge and obtaining full probability distributions of model parameters.
What is the Weibull distribution, and how is it used in survival analysis?
It is used for its flexibility in modeling various shapes of hazard functions and is common in reliability engineering.
How do you estimate the survival function using life tables?
Using grouped data to estimate survival probabilities at certain times.
What are frailty models, and when should they be used?
Frailty models account for unobserved heterogeneity in survival data and are useful when dealing with grouped or clustered data.
How do you interpret the results of a multivariate survival analysis?
By analyzing the effect sizes and confidence intervals of the covariates included in the model.
What are the common pitfalls in interpreting survival analysis results?
Common pitfalls include ignoring censored data, misinterpreting the effects of covariates, and failing to check model assumptions.
How does the choice of time scale affect the results of survival analysis?
The choice affects the interpretation of times, such as age as time scale versus time-on-study.
What are the benefits of using spline functions in survival models?
Spline functions can model complex hazard functions more flexibly than traditional methods.
How do you validate a survival analysis model?
Through techniques like cross-validation, bootstrapping, or external validation with new data sets.
What are landmark analysis and conditional survival, and how are they used?
Landmark analysis focuses on survival from a specific ‘landmark’ time point; conditional survival estimates survival probabilities conditional on having survived to a specific time.
How do you handle outliers in survival data?
Outliers can be addressed by robust estimation techniques or sensitivity analysis to understand their impact.
What are the ethical considerations in conducting survival analysis?
Ensuring informed consent, protecting privacy, and responsibly handling sensitive data are key considerations.
