PREMIUM R MARKDOWN WORKFLOW

Survival Analysis,
from setup to reporting.

A complete, editable workflow for time-to-event research using Kaplan-Meier estimates and Cox proportional hazards models—with the checks and explanations a dissertation needs.

Purchase for $59 View the public preview

One-time purchase · Individual-use license · Secure digital delivery · Preview uses fully synthetic doctoral-program data

Run it firstConfirm the complete example works before adding your data.
Understand each stepConnect model choices and diagnostics to your research design.
Adapt your write-upUse dynamic results text as a transparent starting point.

WHAT IS INSIDE

More than model-fitting code.

The workflow follows the decisions a researcher must document, from defining time and censoring through interpreting adjusted effects.

01

Define the analysis

Specify the time origin, event, follow-up unit, censoring rules, and population at risk.

02

Audit the data

Review missingness, follow-up time, event coding, impossible values, and the analysis sample.

03

Describe survival

Create Kaplan-Meier estimates, confidence intervals, risk tables, and a log-rank comparison.

04

Fit adjusted models

Estimate Cox models, report hazard ratios, and generate adjusted survival curves.

05

Check assumptions

Evaluate proportional hazards, continuous-predictor form, and influential observations.

06

Report transparently

Export reusable tables and figures, create dynamic results text, and follow a reporting checklist.

DESIGNED FOR DOCTORAL RESEARCH

A practical starting point for a defensible analysis.

This workflow is useful when:

  • Your outcome is the time until a defined event.
  • Some participants have not experienced the event by the end of follow-up.
  • You need unadjusted curves and an adjusted regression model.
  • You need to explain assumptions and diagnostics in your dissertation.

Competing risks, recurrent events, time-varying predictors, delayed entry, clustered data, and interval censoring require extensions beyond this introductory workflow.

OUTPUT-ONLY PUBLIC PREVIEW

Review the rendered report.

Open full screen
Survival Analysis · Dissertation Stats Helper

The preview shows results, figures, and interpretation. The paid version includes all editable R Markdown source, the synthetic-data generator, and complete analysis code. Source code and session information are omitted from the public preview.

THE DOWNLOADABLE PACKAGE

Built to run,
learn from, and adapt.

The version 1.0.0 release includes the editable workflow, synthetic dataset and generator, exported examples, documentation, and supporting guidance shown in the preview.

  • Editable R Markdown source file
  • Reproducible synthetic dataset
  • Package installation helper
  • Modern figures and reusable tables
  • Model diagnostics and interpretation notes
  • Dynamic results-writing template
  • Exported example files
  • Start guide and reporting checklist

The workflow supports your analysis process and documentation. It does not replace your advisor, committee, institutional requirements, or study-specific statistical judgment.

VERSION 1.0.0

Complete workflow.
$59 one time.

Pay securely and receive the version 1.0.0 ZIP with the editable analysis, synthetic data, documentation, and rendered report.

Purchase for $59 Review purchase terms

COMMON QUESTIONS

Before you
use the workflow.

Do I need experience with R?

Basic familiarity with RStudio helps. The package is designed to include a start guide, an installation helper, a runnable synthetic example, and clearly marked settings.

Can I replace the example with my own data?

Yes. First run the complete synthetic example, then update the file path, variable names, coding rules, and model choices for your study.

Will this choose the correct survival model for me?

No. You must define the event, time origin, censoring process, estimand, predictors, and model in relation to your research question and design.

Can the workflow handle every survival-analysis design?

No. The report identifies common situations that need extensions, including competing risks, recurrent events, time-varying predictors, delayed entry, clustered data, and interval censoring.