details_proportional_hazards_survival | R Documentation |

`survival::coxph()`

fits a Cox proportional hazards model.

For this engine, there is a single mode: censored regression

This model has no tuning parameters.

The **censored** extension package is required to fit this model.

library(censored) proportional_hazards() %>% set_engine("survival") %>% set_mode("censored regression") %>% translate()

## Proportional Hazards Model Specification (censored regression) ## ## Computational engine: survival ## ## Model fit template: ## survival::coxph(formula = missing_arg(), data = missing_arg(), ## weights = missing_arg(), x = TRUE, model = TRUE)

The model does not fit an intercept.

The main interface for this model uses the formula method since the
model specification typically involved the use of
`survival::Surv()`

.

The model formula can include *special* terms, such as
`survival::strata()`

. The allows the baseline
hazard to differ between groups contained in the function. The column
used inside `strata()`

is treated as qualitative no matter its type. To
learn more about using special terms in formulas with tidymodels, see
`?model_formula`

.

For example, in this model, the numeric column `rx`

is used to estimate
two different baseline hazards for each value of the column:

library(survival) proportional_hazards() %>% fit(Surv(futime, fustat) ~ age + strata(rx), data = ovarian) %>% extract_fit_engine() %>% # Two different hazards for each value of 'rx' basehaz()

## hazard time strata ## 1 0.02250134 59 rx=1 ## 2 0.05088586 115 rx=1 ## 3 0.09467873 156 rx=1 ## 4 0.14809975 268 rx=1 ## 5 0.30670509 329 rx=1 ## 6 0.46962698 431 rx=1 ## 7 0.46962698 448 rx=1 ## 8 0.46962698 477 rx=1 ## 9 1.07680229 638 rx=1 ## 10 1.07680229 803 rx=1 ## 11 1.07680229 855 rx=1 ## 12 1.07680229 1040 rx=1 ## 13 1.07680229 1106 rx=1 ## 14 0.05843331 353 rx=2 ## 15 0.12750063 365 rx=2 ## 16 0.12750063 377 rx=2 ## 17 0.12750063 421 rx=2 ## 18 0.23449656 464 rx=2 ## 19 0.35593895 475 rx=2 ## 20 0.50804209 563 rx=2 ## 21 0.50804209 744 rx=2 ## 22 0.50804209 769 rx=2 ## 23 0.50804209 770 rx=2 ## 24 0.50804209 1129 rx=2 ## 25 0.50804209 1206 rx=2 ## 26 0.50804209 1227 rx=2

Note that columns used in the `strata()`

function will not be estimated
in the regular portion of the model (i.e., within the linear predictor).

Predictions of type `"time"`

are predictions of the mean survival time.

Since risk regression and parametric survival models are modeling different characteristics (e.g. relative hazard versus event time), their linear predictors will be going in opposite directions.

For example, for parametric models, the linear predictor *increases with
time*. For proportional hazards models the linear predictor *decreases
with time* (since hazard is increasing). As such, the linear predictors
for these two quantities will have opposite signs.

tidymodels does not treat different models differently when computing
performance metrics. To standardize across model types, the default for
proportional hazards models is to have *increasing values with time*. As
a result, the sign of the linear predictor will be the opposite of the
value produced by the `predict()`

method in the engine package.

This behavior can be changed by using the `increasing`

argument when
calling `predict()`

on a model object.

This model can utilize case weights during model fitting. To use them,
see the documentation in case_weights and the examples
on `tidymodels.org`

.

The `fit()`

and `fit_xy()`

arguments have arguments called
`case_weights`

that expect vectors of case weights.

Andersen P, Gill R. 1982. Cox’s regression model for counting processes, a large sample study.

*Annals of Statistics*10, 1100-1120.

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