forest_bygroup: Forest plot: colored by groups

In PropensitySub: Treatment Effect Estimate in Strata with Missing Data

Description

Forest plot: colored by groups

Usage

forest_bygroup(
  data,
  summarystat,
  upperci,
  lowerci,
  population.name,
  group.name = population.name,
  color.group.name = population.name,
  stat.label = "Hazard Ratio",
  text.column = NULL,
  text.column.addition = NULL,
  color = NULL,
  stat.type.hr = TRUE,
  log.scale = FALSE,
  extra.stat = NULL,
  extra.stat.label = NULL,
  endpoint.name = "OS",
  study.name = "",
  draw = TRUE,
  shape.column = NULL,
  shape.vec = NULL
)

Arguments

data	data frame. plotting order will be following the order in the data frame.
summarystat	column name that indicates the summary statistics column (e.g. HR, ORR)
upperci, lowerci	column names that indicate the lower and upper CI of the summary statistics
population.name	column name of the column which indicates population names of the summary statistics
group.name	column name of the column which indicates which group each population is in. group names will be shown in the left panel of the forest plot
color.group.name	column name of the column which indicates how to color different groups
stat.label	Y axis label text
text.column	column name of the column which provides texts to be display on the right side of the forest plot. If it is NULL, the text will be generated by pasting summary stat column, the lowerci column and the upperci column
text.column.addition	additional columns to put (will be placed on the right of the figures), could be a vector of multiple column names
color	customized colors to different groups.
stat.type.hr	whether the summary statistics is HR. If so, the forest plot will be centered at 1
log.scale	whether show summary statistics in log scale
extra.stat	column name of the column which indicates the extra statistics to be drawn on the forest plot. The extra statistics will be drawn by "+". Could be NULL
extra.stat.label	label of the extra.stat (to be shown on y axis)
endpoint.name, study.name	text to be shown in title
draw	whether to draw
shape.column	column to pass to adjust shape. Use NULL for none
shape.vec	a vector to sepecify shapes, e.g. c(15, 16).

Value

return forest plot of class grob.

Examples

 
library(dplyr)
clinical_1 <- clinical %>% mutate( 
  indicator = case_when(
    STRATUM == "strata_1" ~ 0, 
    STRATUM == "strata_2" ~ 1,
    is.na(STRATUM) & ARM == "experimental" ~ 1,
    TRUE ~ -1 
  ),
  ARM  = factor(ARM, levels = c("control","experimental")),
  BNLR = case_when(
    is.na(BNLR) ~ median(BNLR, na.rm = TRUE),
    TRUE ~ BNLR
  )
) 
ipw_res <- ipw_strata(
  data.in = clinical_1, formula = indicator ~ BECOG + SEX + BNLR,
  indicator.var = "indicator", tte = "OS_MONTH", event = "OS_EVENT", trt = "ARM",
  class.of.int = list("strata_1" = 1, "strata_2" = 0)
 )
boot_ipw <- bootstrap_propen(
  data.in = clinical_1, formula = indicator ~ BECOG + SEX + BNLR,
  indicator.var = "indicator", tte = "OS_MONTH", event = "OS_EVENT", trt = "ARM",
  class.of.int = list("strata_1" = 1, "strata_2" = 0),
  estimate.res = ipw_res, method = "ipw", n.boot = 5
)
ps_res <- ps_match_strata(
  data.in = clinical_1, formula = indicator ~ BECOG + SEX + BNLR,
  indicator.var = "indicator", tte = "OS_MONTH", event = "OS_EVENT", trt = "ARM",
  class.of.int = list("strata_1" = 1, "strata_2" = 0)
 )
boot_ps <- bootstrap_propen(
  data.in = clinical_1, formula = indicator ~ BECOG + SEX + BNLR, 
  indicator.var = "indicator", tte = "OS_MONTH", event = "OS_EVENT", trt = "ARM",
  class.of.int = list("strata_1" = 0, "strata_2" = 1),
  estimate.res = ps_res, method = "ps", n.boot = 5
)
boot.out.ipw <- boot_ipw$boot.out.est
boot.out.ps <- boot_ps$boot.out.est
ipw.ci.mat <- boot_ipw$est.ci.mat
ps.ci.mat <- boot_ps$est.ci.mat

data.fp <- data.frame(
  HR = round(exp(c(ipw.ci.mat[, 1], ps.ci.mat[, 1])), 2),
  LOWER = round(exp(c(ipw.ci.mat[, 2], ps.ci.mat[, 2])), 2),
  UPPER = round(exp(c(ipw.ci.mat[, 3], ps.ci.mat[, 3])), 2),
  ADA_Group = rep(rownames(ipw.ci.mat), 2),
  n = paste("n =", rep(table(clinical_1$indicator)[c("0", "1")], 2)),
  Methods_ADA = paste(
    rep(c("IPW", "PS"), each = 2), rep(rownames(ipw.ci.mat), 2)
  ),
  Methods = rep(c("IPW", "PS"), each = 2),
  bootstrapHR = c(
    boot.out.ipw[grep("HR", rownames(boot.out.ipw)), "Median"],
    boot.out.ps[grep("HR", rownames(boot.out.ps)), "Median"]
  )
)
forest_bygroup(
  data = data.fp, summarystat = "HR", upperci = "UPPER", lowerci = "LOWER",
  population.name = "Methods_ADA", group.name = "Methods",
  color.group.name = "ADA_Group", text.column.addition = "n",
  stat.label = "Hazard Ratio", text.column = NULL,
  stat.type.hr = TRUE, log.scale = FALSE, extra.stat = "bootstrapHR",
  extra.stat.label = "bootstrap median",
  endpoint.name = "OS", study.name = "Example Study", draw = TRUE
 ) 

PropensitySub documentation built on July 29, 2021, 9:07 a.m.