Generic estimator of survival and cumulative hazard functions about scikit-survival HOT 8 OPEN

This feature has been requested before and I do plan to add in the next release (probably in February). In the meantime, please have a look at #15 (comment) for alternative ways.

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Looks perfectly fine to me. As mentioned in #15 (comment), the only downside is that this way is still subject to the proportional hazards assumption.

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Hi,

Are there any updates on implementing these functions for CoxnetSurvivalAnalysis?

Thanks.

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Unfortunately no, I couldn't find the time to implement this yet. Any contributions would appreciated.

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Hi, I read through the comments about workarounds for this: specifically "using the predicted risk scores as a feature in a Cox model" to get predicted survival functions.

My question: is it appropriate to use the risk scores as the only feature in a Cox model?

I have tried to implement this and the results look sensible at first glance. Would be grateful if someone could take a look at my code below, and let me know if there is anything fundamentally wrong with this approach. Many thanks.

X = data[feature_cols]
y = data[['censor','time']]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)

estimator = GradientBoostingSurvivalAnalysis(subsample=0.95)
estimator.fit(X_train, y_train)

# predicted risk scores - reshaping for next step
y_hat_train = estimator.predict(X_train).reshape(-1, 1)
y_hat_test = estimator.predict(X_test).reshape(-1, 1)

# fit on risk scores from first step ONLY
cox_ph = CoxPHSurvivalAnalysis()
cox_ph.fit(y_hat_train, y_train)

surv_func_train = cox_ph.predict_survival_function(y_hat_train)
surv_func_test = cox_ph.predict_survival_function(y_hat_test)

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Just curious-- the implementation of survival function and cumulative hazard function for generic survival models that you were hoping to get to: is that "using the predicted risk scores as a feature in a Cox model" or Van Belle et al.'s method as mentioned in #15, or yet some different methods? Thanks.

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I was planning on using the approach proposed by Van Belle et al. Unfortunately, due to other obligations, my time is limited at the moment.

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At least for GradientBoostingSurvivalAnalysis w/ coxph loss, you could do something similar to what's in lifelines:

def generate_baselines(self, X, y):
    X, event, time = check_arrays_survival(X, y, accept_sparse=['csr', 'csc', 'coo'], dtype=DTYPE)
    
    ind_hazards = pd.DataFrame({
        "P": np.exp(self.predict(X)),
        "E": event
        "T": time
    })

    ind_hazards_summed_over_durations = ind_hazards.groupby("T")[["P", "E"]].sum()
    ind_hazards_summed_over_durations["P"] = (
        ind_hazards_summed_over_durations["P"].loc[::-1].cumsum()
    )

    baseline_hazard = ind_hazards_summed_over_durations["E"] / ind_hazards_summed_over_durations["P"]

    baseline_cumulative_hazard = baseline_hazard.cumsum()

    baseline_survival = np.exp(-baseline_cumulative_hazard)
    
    return baseline_hazard, baseline_cumulative_hazard, baseline_survival

From which survival = baseline_survival.values ** hazard_ratio

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