diff --git a/docs/make.jl b/docs/make.jl
index 83cc199..4c08352 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -6,6 +6,7 @@ makedocs(
     authors = "Alex Arslan",
     pages = [
         "Home" => "index.md",
+        "Getting Started" => "getting_started.md",
         "Event Times" => "events.md",
         "Kaplan-Meier" => "km.md",
         "Nelson-Aalen" => "na.md",
diff --git a/docs/src/getting_started.md b/docs/src/getting_started.md
new file mode 100644
index 0000000..a04cf90
--- /dev/null
+++ b/docs/src/getting_started.md
@@ -0,0 +1,100 @@
+# Getting Started
+
+## Cox proportional hazards regression model
+
+This tutorial shows how to fit a Cox model and set up the [`EventTime`](@ref)
+values for right censored events.
+
+### Dataset
+
+In this example we'll use a table containing criminal
+[recidivism](https://en.wikipedia.org/wiki/Recidivism) data from Rossi et al. (1980).
+The data pertain to 432 convicts who were released from Maryland state prisons
+in the 1970s and who were followed for one year after release.
+The released convicts were randomly assigned to receive or not receive financial aid
+with equal probability.
+The outcome of interest is the time from release to rearrest.
+
+The dataset is available as a CSV file in this package's `test/data/` directory.
+To load the data as a `DataFrame`, we'll use the [CSV](https://github.com/JuliaData/CSV.jl)
+and [DataFrames](https://github.com/JuliaData/DataFrames.jl) packages.
+
+```julia-repl
+julia> using Survival, StatsModels, CSV, DataFrames
+
+julia> rossi = CSV.read(joinpath(pkgdir(Survival), "test", "data", "rossi.csv")), DataFrame);
+```
+
+### Fitting the model
+
+We now construct the event times used as the model response.
+The `EventTime` constructor accepts a time value and an indicator of whether the value
+was observed (`true`) or right-censored (`false`).
+The times in this data frame are in the column `week` and the arrest status in `arrest`.
+An `arrest` value of 1 indicates an observed event (arrest) and 0 indicates censoring.
+
+```julia-repl
+julia> rossi.event = EventTime.(rossi.week, rossi.arrest .== 1);
+
+julia> first(rossi, 10)
+5×10 DataFrame
+ Row │ arrest  week   fin    age    race   wexp   mar    paro   prio   event
+     │ Int64   Int64  Int64  Int64  Int64  Int64  Int64  Int64  Int64  EventTim…
+─────┼───────────────────────────────────────────────────────────────────────────
+   1 │      1     20      0     27      1      0      0      1      3  20
+   2 │      1     17      0     18      1      0      0      1      8  17
+   3 │      1     25      0     19      0      1      0      1     13  25
+   4 │      0     52      1     23      1      1      1      1      1  52+
+   5 │      0     52      0     19      0      1      0      1      3  52+
+```
+
+To fit the Cox model, we can use `fit(CoxModel, ...)` or the shorthand `coxph(...)`.
+
+```julia-repl
+julia> model = coxph(@formula(event ~ fin + age + race + wexp + mar + paro + prio), rossi)
+StatsModels.TableRegressionModel{CoxModel{Float64}, Matrix{Float64}}
+
+event ~ fin + age + race + wexp + mar + paro + prio
+
+Coefficients:
+────────────────────────────────────────────────
+        Estimate  Std.Error    z value  Pr(>|z|)
+────────────────────────────────────────────────
+fin   -0.379416   0.191379   -1.98253     0.0474
+age   -0.0574299  0.0219988  -2.61059     0.0090
+race   0.31392    0.307995    1.01924     0.3081
+wexp  -0.14981    0.212226   -0.705898    0.4803
+mar   -0.433724   0.38187    -1.13579     0.2560
+paro  -0.0848615  0.195756   -0.433505    0.6646
+prio   0.091521   0.0286469   3.1948      0.0014
+────────────────────────────────────────────────
+```
+
+### Accessing values
+
+Many of the common functions for accessing model parameters used in packages such as
+[GLM](https://github.com/JuliaStats/GLM.jl) are extended for use with `CoxModel`s.
+
+For example, the model coefficient estimates can be extracted with `coef`:
+
+```julia-repl
+julia> coef(model)
+7-element Vector{Float64}:
+ -0.3794158823466362
+ -0.057429889713653676
+  0.313920393830735
+ -0.14980964863737226
+ -0.43372380447995285
+ -0.08486148372086805
+  0.09152099594619753
+```
+
+Similarly, the standard errors of the estimates are accessible with `stderror(model)`
+and the full variance-covariance matrix with `vcov(model)`.
+
+Other available functions include:
+- `loglikelihood`, the log likelihood of the fitted model
+- `nullloglikelihood`, the log likelihood of the null model
+- `dof`, degrees of freedom
+- `nobs`, the number of observations used to fit the model
+- `coeftable`, a table of coefficient names, estimates, standard errors, z-values, and p-values