tabcox: Generate Summary Tables of Fitted Cox Proportional Hazards...
In tab: Functions for Creating Summary Tables for Statistical Reports

Description Usage Arguments Details Value Note Author(s) References See Also Examples

This function performs Cox proportional hazards regression using the R package survival [1, 2] and summarizes the results in a clean table for a statistical report.

tabcox(x, time, delta, latex = FALSE, xlabels = NULL, cluster = NULL,
       robust.se = TRUE, decimals = 2, p.decimals = c(2, 3), p.cuts = 0.01,
       p.lowerbound = 0.001, p.leading0 = TRUE, p.avoid1 = FALSE, n = FALSE,
       events = FALSE, coef = "n", greek.beta = FALSE, binary.compress = TRUE,
       bold.colnames = TRUE, bold.varnames = FALSE, bold.varlevels = FALSE,
       predictor.colname = "Variable", suppress.beta = FALSE,
       print.html = FALSE, html.filename = "table1.html")

`x`	For single predictor, vector of values; for multiple predictors, data frame or matrix with one column per predictor. Categorical variables should be of class "factor."
`time`	Numeric values for time to event or censoring.
`delta`	Indicator variable where 1 = event observed, 0 = censored.
`latex`	If `TRUE`, object returned is formatted for printing in LaTeX using xtable [3]; if `FALSE`, formatted for copy-and-pasting from RStudio into a word processor.
`xlabels`	Optional character vector to label the `x` variables and their levels. If unspecified, generic labels are used.
`cluster`	Optional vector indicating clusters of subjects.
`robust.se`	Only a valid option if `cluster` is specified. In that case, setting to `TRUE` requests robust sandwich method standard errors, while setting to `FALSE` requests normal standard errors.
`decimals`	Number of decimal places for the regression coefficients, standard errors, hazard ratios, and confidence intervals.
`p.decimals`	Number of decimal places for p-values. If a vector is provided rather than a single value, number of decimal places will depend on what range the p-value lies in. See `p.cuts`.
`p.cuts`	Cut-point(s) to control number of decimal places used for p-values. For example, by default `p.cuts = 0.1` and `p.decimals = c(2, 3)`. This means that p-values in the range [0.1, 1] will be printed to two decimal places, while p-values in the range [0, 0.1) will be printed to three decimal places.
`p.lowerbound`	Controls cut-point at which p-values are no longer printed as their value, but rather <lowerbound. For example, by default `p.lowerbound = 0.001`. Under this setting, p-values less than 0.001 are printed as `<0.001`.
`p.leading0`	If `TRUE`, p-values are printed with 0 before decimal place; if `FALSE`, the leading 0 is omitted.
`p.avoid1`	If `TRUE`, p-values rounded to 1 are not printed as 1, but as `>0.99` (or similarly depending on `p.decimals` and `p.cuts`).
`n`	If `TRUE`, the table returned will include a column for sample size.
`events`	If `TRUE`, the table returned will include a column for number of events observed (i.e. uncensored observations).
`coef`	If set to `"x"`, function will standardize all variables in `x` that are continuous, providing standardized regression coefficients. Then, the interpretation of each hazard ratio is the hazard ratio associated with a one standard deviation increase in the predictor.
`greek.beta`	If `TRUE`, column headings refer to regression parameters as Greek letter beta rather than Beta. Only used when `latex = TRUE`.
`binary.compress`	If `TRUE`, only one row of the table is dedicated to parameter estimates for each binary factor predictor. If `FALSE`, the table displays separate rows for the variable name and the two levels for each binary factor predictor, much like the presentation for factor variables with more than two levels.
`bold.colnames`	If `TRUE`, column headings are printed in bold font. Only applies if `latex = TRUE`.
`bold.varnames`	If `TRUE`, variable names in the first column of the table are printed in bold font. Only applies if `latex = TRUE`.
`bold.varlevels`	If `TRUE`, levels of each factor variable are printed in bold font. Only applies if `latex = TRUE` and there is at least one factor variable included as a predictor.
`predictor.colname`	Character string with desired column heading for the column of predictors.
`suppress.beta`	If `FALSE`, the Beta (SE) column is not included. May often be preferred since the point and interval estimate for the hazard ratio contains the same information, but are easier to interpret.
`print.html`	If `TRUE`, function prints a .html file to the current working directory.
`html.filename`	Character string indicating the name of the .html file that gets printed if `print.html = TRUE`.

A character matrix that summarizes the fitted Cox PH regression model. If latex = TRUE, the character matrix will be formatted for inserting into a Markdown/Sweave/knitr report using the xtable package [3].

I may eventually write a function to create a summary table based on an object from coxph rather than the data vectors themselves (like how tabglm works).

If you wish to paste your tables into Word, you can use either of these approaches:

1. Use the write.cb function in the Kmisc package [4]. If your table is stored in a character matrix named table1, use write.cb(table1) to copy the table to your clipboard. Paste the result into Word, then highlight the text and go to Insert - Table - Convert Text to Table... OK.

2. Set print.html = TRUE. This will result in a .html file writing to your current working directory. When you open this file, you will see a nice looking table that you can copy and paste into Word. You can control the name of this file with html.filename.

If you wish to use LaTeX, R Markdown, knitr, Sweave, etc., set latex = TRUE and then use xtable [3]. You may have to set sanitize.text.function = identity when calling print.xtable.

If you have suggestions for additional options or features, or if you would like some help using any function in tab, please e-mail me at vandomed@gmail.com. Thanks!

Dane R. Van Domelen

1. Therneau T (2013). A Package for Survival Analysis in S. R package version 2.37-4, https://cran.r-project.org/package=survival.

2. Terry M. Therneau and Patricia M. Grambsch (2000). Modeling Survival Data: Extending the Cox Model. Springer, New York. ISBN 0-387-98784-3.

3. Dahl DB (2013). xtable: Export tables to LaTeX or HTML. R package version 1.7-1, https://cran.r-project.org/package=xtable.

4. Kevin Ushey (2013). Kmisc: Kevin Miscellaneous. R package version 0.5.0. https://CRAN.R-project.org/package=Kmisc.

Acknowledgment: This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-0940903.

coxph
tabfreq
tabmeans
tabmedians
tabmulti
tabglm
tabgee
tabfreq.svy
tabmeans.svy
tabmedians.svy
tabmulti.svy
tabglm.svy

# Load in sample dataset d and drop rows with missing values
data(d)
d <- d[complete.cases(d), ]

# Create labels for race levels
races <- c("White", "Black", "Mexican American", "Other")

# Test whether race is associated with survival
coxtable1 <- tabcox(x = d$Race, time = d$time, delta = d$delta,
                    xlabels = c("Race", races))

# Test whether age, sex, race, and treatment group are associated with survival
coxtable2 <- tabcox(x = d[,c("Age", "Sex", "Race", "Group")], time = d$time,
                    delta = d$delta,
                    xlabels = c("Age", "Male", "Race", races, "Treatment"))