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Predict survival probabilities from a parametric survival model

Source: R/surv_predict.R
surv_predict.Rd

Estimate model-based survival probabilities at user-specified follow-up times from a fitted survival::survreg() model.

Usage

surv_predict(
  model,
  newdata = NULL,
  times,
  digits = 1,
  format = c("flextable", "gt", "tibble"),
  theme = c("minimal")
)

Arguments

model

A fitted survreg model, or a surv_reg() object containing exactly one fitted model.

newdata

Optional data.frame of profiles for prediction. If NULL, one typical profile is built from the model data using medians for numeric variables and the most common level for categorical variables.

times

Numeric vector of follow-up times at which survival probability should be predicted.

digits

Number of digits for displayed follow-up times and probabilities.

format

Output format. One of "flextable" (default), "gt", or "tibble".

theme

Table styling preset.

Value

A list of class c("gtregression","surv_predict", ...) with elements:

table

A flextable, gt_tbl, or NULL when format = "tibble".

table_body

Tibble with predicted survival probabilities.

table_display

Display data frame used to render the table.

model

The fitted survreg model used for prediction.

newdata,times,distribution,format,source

Metadata fields.

Details

surv_predict() is for parametric survival regression models fitted by surv_reg() or survival::survreg(). It is not a Kaplan-Meier estimate and it is not a Cox prediction helper.

Supported distributions are "weibull", "exponential", "lognormal", and "loglogistic", matching surv_reg(). Predictions are conditional on the supplied profile and the chosen parametric distribution.

Examples

lung_data <- data_lungcancer
lung_data$trt <- factor(lung_data$trt, levels = c(1, 2),
                        labels = c("Standard", "Test"))

fit <- surv_reg(
  data = lung_data,
  time = time,
  event = status,
  exposures = "trt",
  adjust_for = c("age", "karno"),
  distribution = weibull
)

surv_predict(
  model = fit$models$trt,
  newdata = data.frame(
    trt = factor("Test", levels = levels(lung_data$trt)),
    age = 60,
    karno = 70
  ),
  times = c(90, 180, 365)
)

surv_predict(
  model = fit,
  times = c(90, 180),
  format = tibble
)
#> # A tibble: 2 × 8
#>   Profile .profile     trt     age karno  Time Survival.probability Distribution
#>     <int> <chr>        <fct> <dbl> <dbl> <dbl>                <dbl> <chr>       
#> 1       1 Typical pro… Stan…    62    60    90                0.478 weibull     
#> 2       1 Typical pro… Stan…    62    60   180                0.234 weibull