I provide online survival analysis tutoring for graduate students in the Los Angeles and San Francisco Bay Area metros. I regularly work with students from programs at UCLA, USC, UC Irvine, and Caltech, as well as UC Berkeley, Stanford University, UC San Francisco (UCSF), and other UC and private graduate programs. All tutoring is delivered online; I do not maintain a physical office in these cities.
Survival analysis is used when the outcome of interest is the time until an event occurs. This includes patient survival, disease recurrence, job duration, program participation, system failure, or time-to-exit outcomes. Graduate students often struggle with properly defining the event, handling censoring, choosing the correct model, and interpreting hazard ratios without overclaiming.
My focus is on helping you build a survival analysis that is methodologically correct, clearly explained, and defensible in graduate coursework, qualifying exams, theses, and applied research papers.
Speak Directly With the Tutor
If you’re unsure how to define your event, handle censoring, or interpret a Cox model, reach out directly. You’ll be speaking with the tutor who actually works through the models with you.
Call/Text: 510-398-0006
Email: tutor@californiagraduatetutor.com
Topics Covered in Survival Analysis Tutoring
- Defining events, time origin, and follow-up windows
- Right-censoring and administrative censoring
- Kaplan–Meier survival curves and median survival
- Log-rank tests and group comparisons
- Cox proportional hazards models
- Interpreting hazard ratios correctly
- Checking proportional hazards assumptions
- Parametric survival models (exponential, Weibull)
- Competing risks and when Cox models fail
Core Survival Analysis Quantities (MathJax Standard)
The survival function represents the probability that an event has not occurred by time \(t\):
\[ S(t) = \Pr(T > t) \]
The hazard function measures the instantaneous risk of the event:
\[ h(t) = \lim_{\Delta t \to 0} \frac{\Pr(t \le T < t+\Delta t \mid T \ge t)}{\Delta t} \]
In the Cox proportional hazards model:
\[ h(t \mid x) = h_0(t)\exp(x^\top \beta) \]
A hazard ratio \(\exp(\beta_k)\) represents the multiplicative change in hazard for a one-unit increase in a covariate. We focus on interpretation, not just estimation—especially what hazard ratios do not mean.
A Graduate-Level Survival Analysis Workflow
- Define the event precisely and justify it
- Choose an appropriate time origin
- Explain censoring and why it is non-informative
- Explore data with Kaplan–Meier curves
- Select Cox vs parametric vs competing risks models
- Check assumptions and robustness
- Interpret results in substantive terms
- Write a methods section that can be defended
Software Support
- R: survival objects, diagnostics, and reporting
- Stata: stset, Cox models, and post-estimation
- SPSS: survival procedures and interpretation
Related Statistics Support
Need Survival Analysis Help From the Person Doing the Tutoring?
If you want clear answers, correct modeling, and explanations you can stand behind, reach out directly. You won’t be routed through sales or intake staff.
Call/Text: 510-398-0006 | Email: tutor@californiagraduatetutor.com