# Cox Proportional Modelling in Julia

**URL:** <https://discourse.julialang.org/t/cox-proportional-modelling-in-julia/94149>\
**Category:** New to Julia\
**Tags:** statistics\
**Created:** [February 6, 2023, 4:31pm UTC](https://discourse.julialang.org/t/cox-proportional-modelling-in-julia/94149 "2023-02-06T16:31:43Z")\
**Posts on this page:** 5\
**Page:** 1

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**Author:** ![Nobel\_Chowdary](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nobel_chowdary/32/45850_2.png) [@Nobel\_Chowdary](https://discourse.julialang.org/u/Nobel_Chowdary)\
**Post date:** [February 6, 2023, 4:31pm UTC](https://discourse.julialang.org/t/cox-proportional-modelling-in-julia/94149/1 "2023-02-06T16:31:43Z")

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The following code of mine shows Surv function not found. Can someone help me out on this

```julia
using DataFrames, SurvivalAnalysis, Survival

# Load example data
df = DataFrame(duration = [5, 3, 9, 8, 7, 4, 3, 2, 1],
                event = [1, 1, 1, 0, 0, 0, 0, 1, 1],
                x1 = randn(9),
                x2 = randn(9))

# Fit the proportional hazards model
fit = coxph(Surv(df.duration, df.event) ~ df.x1 + df.x2, df)

# Summarize the results
println(summary(fit))

```

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**Author:** ![nilshg](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nilshg/32/2283_2.png) [@nilshg](https://discourse.julialang.org/u/nilshg)\
**Post date:** [February 7, 2023, 11:13am UTC](https://discourse.julialang.org/t/cox-proportional-modelling-in-julia/94149/2 "2023-02-07T11:13:49Z")

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I can’t reproduce this:

```julia
julia> coxph(Surv(df.duration, df.event) ~ df.x1 + df.x2, df)
ERROR: MethodError: no method matching ~(::SurvivalAnalysis.IntSurv, ::Vector{Float64})

```

That said, I’m not sure I understand what you are doing - you seem to be mixing `SurvivalAnalysis` with `Survival`, two packages which implement similar (and overlapping) functionality, but aren’t meant to be used together as far as I know. `SurvivalAnalysis` doesn’t implement a Cox-PH model at present, and `Surv` is a type defined in the `Survival` package.

In `Survival`, a Cox-PH model is fit as follows:

```julia
julia> df.event = EventTime.(df.duration, df.event .== 1);

julia> coxph(@formula(event ~ x1 + x2), df)
StatsModels.TableRegressionModel{CoxModel{Float64}, Matrix{Float64}}

event ~ x1 + x2

Coefficients:
─────────────────────────────────────────────
     Estimate Std.Error z value Pr(>|z|)
─────────────────────────────────────────────
x1 0.474264 0.590107 0.803691 0.4216
x2 -0.310167 0.497345 -0.623646 0.5329
─────────────────────────────────────────────

```

See the docs here:

[https://juliastats.org/Survival.jl/latest/getting\_started/#Fitting-the-model](https://juliastats.org/Survival.jl/latest/getting_started/#Fitting-the-model)

In `Survival`, the syntax is:

```julia
f = kaplan_meier(@formula(Surv(Y, D) ~ 1), data)

```

However as I said above there’s no `coxph` method.

Also note that your syntax for calling the fitting procedure is off - the first arugment should be a formula, which you either create by using the `@formula` macro or by putting together `Term` objects yourself, not by passing the actual data directly.

I recommend you read the docs of both `Survival` and `SurvivalAnalysis` to understand the correct syntax and the functionality of each package, and maybe the docs of `StatsModels` as well to understand how `@formula` works.

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<div class="post-metadata">

**Author:** ![Nobel\_Chowdary](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nobel_chowdary/32/45850_2.png) [@Nobel\_Chowdary](https://discourse.julialang.org/u/Nobel_Chowdary)\
**Post date:** [February 7, 2023, 3:14pm UTC](https://discourse.julialang.org/t/cox-proportional-modelling-in-julia/94149/3 "2023-02-07T15:14:37Z")

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That is really great, for the clarity you gave to use either of survival or survival analysis package. Despite that, can you provide me the reference to perform the test-train split and train the model using survival regression and make a prediction on that, in a similar style to what you have mentioned.

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<div class="post-metadata">

**Author:** ![nilshg](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nilshg/32/2283_2.png) [@nilshg](https://discourse.julialang.org/u/nilshg)\
**Post date:** [February 7, 2023, 3:51pm UTC](https://discourse.julialang.org/t/cox-proportional-modelling-in-julia/94149/4 "2023-02-07T15:51:51Z")

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Splitting the data into test and train samples is just elementary data processing, nothing to do with either of those packages. I guess you just have to make sure that you randomly split on observation ids rather than rows (if you have panel data).

By “training” the model I assume you mean just fitting it - I’ve shown how to do this above. I don’t think `Survival` implements a `predict` function, so you’ll probably have to roll your own predictions from the estimated coefficients.

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<div class="post-metadata">

**Author:** ![Nobel\_Chowdary](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nobel_chowdary/32/45850_2.png) [@Nobel\_Chowdary](https://discourse.julialang.org/u/Nobel_Chowdary)\
**Post date:** [February 7, 2023, 4:02pm UTC](https://discourse.julialang.org/t/cox-proportional-modelling-in-julia/94149/5 "2023-02-07T16:02:27Z")

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Can you please help me out in formulating a way of predicting using the estimated coefficients as shown in your explanation of the code?