# Quick start

This is a short tour that goes from raw data to a survival curve, a plot, and a fitted model. Each step is only a few lines. The rest of the guide explains every piece in depth, and links are collected at the end.


# Represent the data

Survival data pairs a follow-up time with an indicator of whether the event was actually observed or the subject was censored (still event-free when we last saw them). Greenwood keeps the two together in a [Surv](../reference/Surv.md#greenwood.Surv) object. We use the bundled `lung` dataset, the survival times of patients with advanced lung cancer.


``` python
import greenwood as gw

lung = gw.load_dataset("lung", backend="polars")
y = gw.Surv.right(lung["time"], event=(lung["status"] == 2))
y
```


    Surv(type=right, n=228, events=165)


The summary reports 228 patients, of whom 165 died during the study; the rest were censored. One detail to note: this dataset codes `status` as 1 for censored and 2 for dead, so we turn it into a plain event indicator with `status == 2`.


# Estimate the survival curve

The Kaplan-Meier estimator turns those times into a survival curve, the probability of surviving past each point in time. Printing the fitted estimator reports the median survival.


``` python
lung = gw.load_dataset("lung", backend="polars")
y = gw.Surv.right(lung["time"], event=(lung["status"] == 2))

km = gw.KaplanMeier().fit(y)
km
```


    KaplanMeier (Kaplan-Meier survival estimate)

        n  events  median  0.95LCL  0.95UCL
      228     165     310      285      363


The median is 310 days: half of the patients are still alive at 310 days. The two columns after it give a 95 percent confidence interval for that median, 285 to 363 days.


# Plot the curve

[plot_survival](../reference/plot_survival.md#greenwood.plot_survival) draws the curve as an interactive chart, with a shaded confidence band and a notch wherever a patient was censored.


``` python
lung = gw.load_dataset("lung", backend="polars")
y = gw.Surv.right(lung["time"], event=(lung["status"] == 2))

km = gw.KaplanMeier().fit(y)

gw.plot_survival(km)
```


The curve starts at 1.0 and steps down at each death. It does not reach 0 because some patients were still alive at the end of follow-up, so their survival time is only known to be at least that long.


# Model a covariate effect

To ask how a characteristic changes the risk of death, fit a Cox proportional hazards model. Here we include `age` and `sex`. Printing the model shows a coefficient table.


``` python
lung = gw.load_dataset("lung", backend="polars")
y = gw.Surv.right(lung["time"], event=(lung["status"] == 2))

cox = gw.CoxPH().fit(y, lung[["age", "sex"]])
cox
```


    CoxPH (Cox proportional hazards model, ties='efron')

            coef  exp(coef)  se(coef)       z         p
    age  0.01705      1.017  0.009223   1.848   0.06459
    sex  -0.5132     0.5986    0.1675  -3.065  0.002178

    n = 228, events = 165
    Likelihood ratio test = 14.12 on 2 df, p = 0.0008574


The `exp(coef)` column holds hazard ratios. For `sex` (coded 1 for male, 2 for female) the hazard ratio is about 0.60, which means women had roughly 40 percent lower risk of death than men over the study, holding age fixed. A ratio below 1 is lower risk, above 1 is higher risk.


# Next steps

That is the core loop: represent the data, estimate a curve, visualize it, and model an effect. Each part of the guide takes one of these steps further.

- [Survival data and the Surv object](survival-data.md) and [Data sources and formats](data-sources.md) cover the input side in full.
- [Estimating survival with Kaplan-Meier](kaplan-meier.md), [Comparing groups](comparing-groups.md), and [Visualizing survival](visualization.md) cover description and comparison.
- [Cox regression](cox-regression.md) and [Parametric survival models](parametric-models.md) cover modeling, and [Prediction performance](prediction-performance.md) covers evaluation.