clinpubr (version 1.1.1)

regression_basic_results: Basic results of logistic or Cox regression.

Description

Generate the result table of logistic or Cox regression with different settings of the predictor variable and covariates. Also generate KM curves for Cox regression.

Usage

regression_basic_results(
  data,
  x,
  y,
  time = NULL,
  time2 = NULL,
  model_covs = NULL,
  cluster = NULL,
  pers = c(0.1, 10, 100),
  factor_breaks = NULL,
  factor_labels = NULL,
  quantile_breaks = NULL,
  quantile_labels = NULL,
  label_with_range = FALSE,
  save_output = FALSE,
  figure_type = "png",
  ref_levels = "lowest",
  est_nsmall = 2,
  p_nsmall = 3,
  pval_eps = 0.001,
  median_nsmall = 0,
  colors = NULL,
  xlab = NULL,
  legend_title = x,
  legend_pos = c(0.8, 0.8),
  pval_pos = NULL,
  n_y_pos = 0.9,
  height = 6,
  width = 6,
  ...
)

Value

A list of results, including the regression table and the KM curve plots.

Arguments

data

A data frame.

x

A character string of the predictor variable.

y

A character string of the outcome variable.

time

A character string of the time variable. If NULL, logistic regression is used. Otherwise, Cox proportional hazards regression is used.

time2

A character string of the ending time of the interval for interval censored or counting process data only.

model_covs

A character vector or a named list of covariates for different models. If NULL, only the crude model is used.

cluster

A character string of the cluster variable. If set, correct for heteroscedasticity and for correlated responses from cluster samples using rms::robcov().

pers

A numeric vector of the denominators of variable x. Set this denominator to obtain a reasonable OR or HR.

factor_breaks

A numeric vector of the breaks to factorize the x variable.

factor_labels

A character vector of the labels for the factor levels.

quantile_breaks

A numeric vector of the quantile breaks to factorize the x variable.

quantile_labels

A character vector of the labels for the quantile levels.

label_with_range

A logical value indicating whether to add the range of the levels to the labels.

save_output

A logical value indicating whether to save the results.

figure_type

A character string of the figure type. Can be "png", "pdf", and other types that ggplot2::ggsave() support.

ref_levels

A vector of strings of the reference levels of the factor variable. You can use "lowest" or "highest" to select the lowest or highest level as the reference level. Otherwise, any level that matches the provided strings will be used as the reference level.

est_nsmall

An integer specifying the precision for the estimates in the plot.

p_nsmall

An integer specifying the number of decimal places for the p-values.

pval_eps

The threshold for rounding p values to 0.

median_nsmall

The minimum number of digits to the right of the decimal point for the median survival time.

colors

A vector of colors for the KM curves.

xlab

A character string of the x-axis label of the survival plot.

legend_title

A character string of the title of the legend.

legend_pos

A numeric vector of the position of the legend.

pval_pos

A numeric vector of the position of the p-value.

n_y_pos

A numerical of range 0 to 1 to assign the y position of total sample count. NULL to hide.

height

The height of the plot.

width

The width of the plot.

...

Additional arguments passed to the survminer::ggsurvplot function for KM curve.

Details

The function regression_basic_results generates the result table of logistic or Cox regression with different settings of the predictor variable and covariates. The setting of the predictor variable includes the original x, the standardized x, the log of x, and x divided by denominators in pers as continuous variables, and the factorization of the variable including split by median, by quartiles, and by factor_breaks and quantile_breaks. The setting of the covariates includes different models with different covariates.

Examples

data(cancer, package = "survival")
# coxph model with time assigned
regression_basic_results(cancer,
  x = "age", y = "status", time = "time",
  model_covs = list(Crude = c(), Model1 = c("ph.karno"), Model2 = c("ph.karno", "sex")),
  save_output = FALSE,
  ggtheme = survminer::theme_survminer(font.legend = c(14, "plain", "black")) # theme for KM
)

# logistic model with time not assigned
cancer$dead <- cancer$status == 2
regression_basic_results(cancer,
  x = "age", y = "dead", ref_levels = c("Q3", "High"),
  model_covs = list(Crude = c(), Model1 = c("ph.karno"), Model2 = c("ph.karno", "sex")),
  save_output = FALSE
)