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 universities such as UCLA, USC, UC Irvine, and Caltech in Southern California, as well as UC Berkeley, Stanford University, UC San Francisco (UCSF), and San José State in the Bay Area. All tutoring is delivered online, designed specifically for graduate-level coursework and research—there is no in-person office requirement.
Survival analysis appears across biostatistics, public health, medicine, economics, and applied social science programs when the outcome of interest is time until an event occurs. Whether you are analyzing patient survival times, program participation duration, job transitions, or system failure times, I help you select appropriate methods, implement them correctly, and explain your results at a level expected in graduate programs.
Many graduate students struggle not with running software, but with defining the event correctly, handling censoring, choosing between Kaplan–Meier, Cox, or parametric models, and interpreting hazard ratios without overstating conclusions. My goal is to help you build a survival analysis workflow you can confidently defend in coursework, exams, theses, or applied research projects.
Book Online Survival Analysis Tutoring (LA & SF Graduate Students)
Share your assignment or research question, how the event and time scale are defined, how censoring occurs, and which software you are using. I’ll help you choose the correct survival model and interpret results clearly.
Call/Text: 510-398-0006
Email: tutor@californiagraduatetutor.com
What Survival Analysis Tutoring Covers
- Time-to-event design: defining events, time origin, and follow-up windows
- Censoring mechanisms: right-censoring, administrative censoring, and implications
- Kaplan–Meier analysis: survival curves, medians, and group comparisons
- Log-rank tests: assumptions and interpretation
- Cox proportional hazards models: covariates, hazard ratios, and diagnostics
- Parametric survival models: exponential and Weibull specifications
- Competing risks: cause-specific hazards vs cumulative incidence
- Graduate reporting: figures, tables, assumptions, and limitations
Core Survival Concepts (MathJax Standard)
The survival function describes the probability that an event has not yet occurred:
\[ S(t) = \Pr(T > t) \]
The hazard function captures the instantaneous event rate:
\[ h(t) = \lim_{\Delta t \to 0} \frac{\Pr(t \le T < t+\Delta t \mid T \ge t)}{\Delta t} \]
In the Cox proportional hazards model:
\[ h(t \mid x) = h_0(t)\exp(x^\top \beta) \]
A coefficient is interpreted through a hazard ratio, \(\exp(\beta_k)\), representing the multiplicative change in hazard for a one-unit increase in a covariate. We focus heavily on interpretation—what hazard ratios mean, what they do not mean, and how to explain them correctly in graduate-level writing.
A Graduate-Level Survival Analysis Workflow
- Clarify the research question: prediction, explanation, or policy evaluation.
- Define the event: what counts, what does not, and why.
- Specify censoring: when and why observations end.
- Explore data: follow-up time, event counts, and missingness.
- Estimate models: KM, Cox, or parametric as appropriate.
- Check assumptions: proportional hazards and sensitivity.
- Interpret results: substantive meaning and uncertainty.
- Write it up: methods, assumptions, figures, and limitations.
Software Support
- R: survival analysis workflows and diagnostics
- Stata: stset, Cox models, and post-estimation
- SPSS: survival procedures and output interpretation
Related Statistics & Biostatistics Support
Need Graduate-Level Survival Analysis You Can Defend?
If censoring rules, Cox model interpretation, or competing risks are creating confusion, I can help you produce a clean survival analysis and a clear, graduate-level explanation— fully online for LA and San Francisco students.
Call/Text: 510-398-0006 | Email: tutor@californiagraduatetutor.com