使用“forestmodel”包（forest_model 函数）时，如何抑制/不显示效果估计、95% CI 和 p 值？

Question

我使用下面的代码创建了一个基于多变量 Cox PH 回归的森林图（来自

forestmodel

包）。然而，该图包括效果估计、95% 置信区间和 p 值，从而使森林图本身显得更小。如何抑制/省略图中的估计值以及 95% CI 和 p 值？预先感谢！

cox_model <- coxph(Surv(`_t`, died) ~ x1 + x2 + x3 + x4 + x5, id = record_id, data = data)


forest_model(
        cox_model,
        panels = default_forest_panels(cox_model, factor_separate_line  = ),
        covariates = NULL,
        exponentiate = TRUE,
        funcs = NULL,
        factor_separate_line = FALSE,
        format_options = forest_model_format_options(),
        theme = theme_forest(),
        limits = NULL,
        breaks = NULL,
        return_data = FALSE,
        recalculate_width = TRUE,
        recalculate_height = TRUE,
        model_list = NULL,
        merge_models = FALSE,
        exclude_infinite_cis = TRUE,
)

我尝试使用

forest_panel

函数来指定要显示绘图的哪些列，但显然，这不起作用，或者我可能错过了一些东西。

Answer 1

您需要一个自定义面板。

自定义面板示例

  panels <- list(
  list(width = 0.03),
  list(width = 0.1, display = ~variable, fontface = "bold", heading = "Variable"),
  list(width = 0.1, display = ~level),
  list(width = 0.05, display = ~n, hjust = 1, heading = "N"),
  list(width = 0.05, display = ~n_events, width = 0.05, hjust = 1, heading = "Events"),
  list(width = 0.03),
  list(
    width = 0.55, item = "forest", hjust = 0.5, heading = "Hazard ratio", linetype = "dashed",
    line_x = 0
  )

森林图示例

library("survival")
library("dplyr")
pretty_lung <- lung %>%
  transmute(time,
            status,
            Age = age,
            Sex = factor(sex, labels = c("Male", "Female")),
            ECOG = factor(lung$ph.ecog),
            `Meal Cal` = meal.cal
  )

forest_model(coxph(Surv(time, status) ~ ., pretty_lung), panels) +
  scale_y_discrete(expand=c(0.1,0)) +
  ggtitle("Custom panel")