Podcast
Questions and Answers
What is the primary function of the aes function in ggplot?
What is the primary function of the aes function in ggplot?
What does the after_stat function do in ggplot?
What does the after_stat function do in ggplot?
What is the purpose of the theme_bw function in ggplot?
What is the purpose of the theme_bw function in ggplot?
What is the result of the code count(continent) %>% mutate(perc = n/sum(n) * 100)?
What is the result of the code count(continent) %>% mutate(perc = n/sum(n) * 100)?
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What does the scale_colour_grey function do in ggplot?
What does the scale_colour_grey function do in ggplot?
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What is the purpose of the group = 1 argument in the geom_bar function?
What is the purpose of the group = 1 argument in the geom_bar function?
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What is the result of the code gapminder2 %>% count(continent)?
What is the result of the code gapminder2 %>% count(continent)?
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What is the purpose of the mutate function in the code gapminder2 %>% count(continent) %>% mutate(perc = n/sum(n) * 100)?
What is the purpose of the mutate function in the code gapminder2 %>% count(continent) %>% mutate(perc = n/sum(n) * 100)?
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What is the purpose of removing observations with missing values for the variable cat_fbs?
What is the purpose of removing observations with missing values for the variable cat_fbs?
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What is the result of the code count(cat_fbs)?
What is the result of the code count(cat_fbs)?
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Study Notes
Survival Analysis
- Survival analysis is also known as duration analysis or time-to-event analysis.
- It's useful in follow-up studies where participants or patients are followed up until they develop the event of interest.
- Examples of such studies include retrospective cohort or prospective cohort studies.
- Survival analysis can be applied to any study that involves both the duration of follow-up and the event of interest.
Types of Survival Analysis
- There are three main types of survival analysis:
- Non-parametric survival analysis (e.g. Kaplan-Meier estimates)
- Semi-parametric survival analysis (e.g. Cox proportional hazards regression)
- Parametric survival analysis (e.g. Weibull parametric survival analysis)
Kaplan-Meier Estimates
- Kaplan-Meier estimates are used in non-parametric survival analysis.
- They are often performed at the beginning of an analysis to understand the trend of survival probabilities.
Cox Proportional Hazards (PH) Regression
- Cox PH regression is a type of semi-parametric survival analysis.
- It can be used for simple (univariable) or multiple (multivariable) regression analysis.
R Studio Project
- It is recommended to start a new analysis project by creating a new RStudio project.
Data Visualization
- ggplot can be used to create plots, such as bar charts and histograms.
- The y-axis can be adjusted to show proportions by using the
after_statfunction.
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Description
This quiz covers the concepts of survival analysis, Kaplan-Meier estimates, and Cox regression, including objectives and recommended readings from epidemiology and statistics books.
