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Survival Analysis with R

HardData ScienceStatistics12 chapters

A practical introduction to analyzing time-to-event data, guiding students from basic survival estimation to multivariable regression modeling using R. Manage censored observations, fit Cox and parametric models, and perform essential diagnostics on real-world datasets.

What This Course Covers

Survival Analysis with R is structured into 12 chapters that build on each other progressively:

Chapter 1: Introduction▼
Chapter 2: Foundations of Survival Theory▼
Chapter 3: Nonparametric Estimation of Survival Curves▼
Chapter 4: Nonparametric Hypothesis Testing and Comparisons▼
Chapter 5: Semi-Parametric Regression▼
Chapter 6: Model Building: Covariate Selection and Splines▼
Chapter 7: Regression Diagnostics and Proportionality Checks▼
Chapter 8: Parametric Regression▼
Chapter 9: Time-Dependent Covariates and Dynamic Predictors▼
Chapter 10: Complex Event Types▼
Chapter 11: Study Design, Power, and Sample Size Determination▼
Chapter 12: Piecewise Hazards, Interval Censoring, and Lasso▼

Each chapter combines interactive AI tutoring with hands-on examples. After you learn the material, Lambdio's spaced repetition algorithm schedules review sessions at optimal intervals — so you retain concepts and techniques long-term.

How to Study Survival Analysis with R on Lambdio

Lambdio's AI-powered platform adapts to how Data Science courses are best learned. Here's our recommended approach:

Learning Mode
Standard Mode — for first-time learning of each chapter
Review Modes
Standard, Quiz — for spaced repetition reviews
Learning Priority
High Priority — controls how often the algorithm schedules reviews

Survival Analysis with R is a mathematically dense, code-intensive subject built on censored-data likelihoods, the partial likelihood, and a large family of named tests, residuals, and models. Standard Mode is the right fit because acquiring these skills requires structured explanations paired with hands-on R implementation and comprehension checks at each step; Socratic Mode would be inefficient for learning derivations, diagnostics, and package syntax. The Hard difficulty rating makes High priority the appropriate default, so the spaced repetition algorithm schedules frequent reviews that keep proportional hazards checks, the Breslow and Efron tie corrections, and the competing-risks distinction between cause-specific and Fine and Gray models fresh. Between sessions, use Quiz Mode for quick reinforcement of which residual answers which diagnostic question. A good tactic is to study a method such as the Cox model in Standard Mode, immediately fit it in R on a real dataset, then run a Quiz Mode check before the next High-priority review locks the concept in.

Interactive Quiz

Test your knowledge with these sample questions from the course. Tap an answer to see if you're right:

Q1: What does the censoring indicator delta equal when the event of interest is actually observed?
Q2: In the Weibull distribution, what does a shape parameter greater than one imply about the hazard?
Q3: Which estimator provides a nonparametric estimate of the cumulative hazard function?
Q4: Which test is mathematically equivalent to the score test from a Cox model with a single binary covariate?
Q5: Which residual is used to assess whether the proportional hazards assumption holds over time?
Q6: In competing risks, which function correctly estimates the probability of experiencing a specific event type over time?
Q7: In survival study design, statistical power depends primarily on which quantity?

What You'll Be Able to Do After This Course

  • ✓Distinguish right, left, and interval censoring and explain how informative censoring biases survival estimates
  • ✓Derive and interpret the survival, hazard, and cumulative hazard functions and their relationships
  • ✓Estimate survival curves nonparametrically using Kaplan-Meier and Nelson-Aalen with appropriate confidence intervals
  • ✓Compare survival distributions with log-rank, weighted, and stratified log-rank tests
  • ✓Fit and interpret Cox proportional hazards models using partial likelihood in R
  • ✓Adjust for confounders, select covariates, and model non-linear effects with splines and information criteria
  • ✓Diagnose Cox models using martingale, deviance, and Schoenfeld residuals and test the proportional hazards assumption
  • ✓Apply parametric accelerated failure time models and handle time-dependent covariates, competing risks, and high-dimensional variable selection

Frequently Asked Questions

Do I need prior experience with R to take this course?▼
What statistical background is required?▼
How long does it take to complete this course?▼
What software and R packages do I need?▼
How does Lambdio's AI tutor help me master survival analysis?▼

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