Author: Ethan Heinzen, Beth Atkinson, Jason Sinnwell

# The paired function

#### Ethan Heinzen, Beth Atkinson, Jason Sinnwell

# Introduction

Another one of the most common tables in medical literature includes summary statistics for a set of variables paired across two time points. Locally at Mayo, the SAS macro `%paired` was written to create summary tables with a single call. With the increasing interest in R, we have developed the function `paired()` to create similar tables within the R environment.

This vignette is light on purpose; `paired()` piggybacks off of tableby, so most documentation there applies here, too.

# Simple Example

The first step when using the `paired()` function is to load the `arsenal` package. We can’t use `mockstudy` here because we need a dataset with paired observations, so we’ll create our own dataset.

    library(arsenal)
    dat <- data.frame(
      tp = paste0("Time Point ", c(1, 2, 1, 2, 1, 2, 1, 2, 1, 2)),
      id = c(1, 1, 2, 2, 3, 3, 4, 4, 5, 6),
      Cat = c("A", "A", "A", "B", "B", "B", "B", "A", NA, "B"),
      Fac = factor(c("A", "B", "C", "A", "B", "C", "A", "B", "C", "A")),
      Num = c(1, 2, 3, 4, 4, 3, 3, 4, 0, NA),
      Ord = ordered(c("I", "II", "II", "III", "III", "III", "I", "III", "II", "I")),
      Lgl = c(TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE),
      Dat = as.Date("2018-05-01") + c(1, 1, 2, 2, 3, 4, 5, 6, 3, 4),
      stringsAsFactors = FALSE
    )

To create a simple table stratified by time point, use a `formula=` statement to specify the variables that you want summarized and the `id=` argument to specify the paired observations.

    p <- paired(tp ~ Cat + Fac + Num + Ord + Lgl + Dat, data = dat, id = id, signed.rank.exact = FALSE)
    summary(p)

| Time Point 1 (N=4) | Time Point 2 (N=4) | Difference (N=4) | p value
--- | --- | --- | --- | ---
Cat |  |  |  | 1.000
A | 2 (50.0%) | 2 (50.0%) | 1 (50.0%) |
B | 2 (50.0%) | 2 (50.0%) | 1 (50.0%) |
Fac |  |  |  | 0.261
A | 2 (50.0%) | 1 (25.0%) | 2 (100.0%) |
B | 1 (25.0%) | 2 (50.0%) | 1 (100.0%) |
C | 1 (25.0%) | 1 (25.0%) | 1 (100.0%) |
Num |  |  |  | 0.391
Mean (SD) | 2.750 (1.258) | 3.250 (0.957) | 0.500 (1.000) |
Range | 1.000 - 4.000 | 2.000 - 4.000 | -1.000 - 1.000 |
Ord |  |  |  | 0.174
I | 2 (50.0%) | 0 (0.0%) | 2 (100.0%) |
II | 1 (25.0%) | 1 (25.0%) | 1 (100.0%) |
III | 1 (25.0%) | 3 (75.0%) | 0 (0.0%) |
Lgl |  |  |  | 1.000
FALSE | 2 (50.0%) | 1 (25.0%) | 2 (100.0%) |
TRUE | 2 (50.0%) | 3 (75.0%) | 1 (50.0%) |
Dat |  |  |  | 0.182
Median | 2018-05-03 | 2018-05-04 | 0.500 |
Range | 2018-05-02 - 2018-05-06 | 2018-05-02 - 2018-05-07 | 0.000 - 1.000 |

The third column shows the difference between time point 1 and time point 2. For categorical variables, it reports the percent of observations from time point 1 which changed in time point 2.

# NAs

Note that by default, observations which do not have both timepoints are removed. This is easily changed using the `na.action = na.paired("<arg>")` argument. For example:

    p <- paired(tp ~ Cat + Fac + Num + Ord + Lgl + Dat, data = dat, id = id,
                signed.rank.exact = FALSE, na.action = na.paired("fill"))
    summary(p)

| Time Point 1 (N=6) | Time Point 2 (N=6) | Difference (N=6) | p value
--- | --- | --- | --- | ---
Cat |  |  |  | 1.000
N-Miss | 2 | 1 | 2 |
A | 2 (50.0%) | 2 (40.0%) | 1 (50.0%) |
B | 2 (50.0%) | 3 (60.0%) | 1 (50.0%) |
Fac |  |  |  | 0.261
N-Miss | 1 | 1 | 2 |
A | 2 (40.0%) | 2 (40.0%) | 2 (100.0%) |
B | 1 (20.0%) | 2 (40.0%) | 1 (100.0%) |
C | 2 (40.0%) | 1 (20.0%) | 1 (100.0%) |
Num |  |  |  | 0.391
N-Miss | 1 | 2 | 2 |
Mean (SD) | 2.200 (1.643) | 3.250 (0.957) | 0.500 (1.000) |
Range | 0.000 - 4.000 | 2.000 - 4.000 | -1.000 - 1.000 |
Ord |  |  |  | 0.174
N-Miss | 1 | 1 | 2 |
I | 2 (40.0%) | 1 (20.0%) | 2 (100.0%) |
II | 2 (40.0%) | 1 (20.0%) | 1 (100.0%) |
III | 1 (20.0%) | 3 (60.0%) | 0 (0.0%) |
Lgl |  |  |  | 1.000
N-Miss | 1 | 1 | 2 |
FALSE | 3 (60.0%) | 2 (40.0%) | 2 (100.0%) |
TRUE | 2 (40.0%) | 3 (60.0%) | 1 (50.0%) |
Dat |  |  |  | 0.182
N-Miss | 1 | 1 | 2 |
Median | 2018-05-04 | 2018-05-05 | 0.500 |
Range | 2018-05-02 - 2018-05-06 | 2018-05-02 - 2018-05-07 | 0.000 - 1.000 |

For more details, see the help page for `na.paired()`.

# Available Function Options

## Testing options

The tests used to calculate p-values differ by the variable type, but can be specified explicitly in the formula statement or in the control function.

The following tests are accepted:

- `paired.t`: A paired t-test.

- `mcnemar`: McNemar’s test.

- `signed.rank`: the signed-rank test.

- `sign.test`: the sign test.

- `notest`: Don’t perform a test.

## `paired.control` settings

A quick way to see what arguments are possible to utilize in a function is to use the `args()` command. Settings involving the number of digits can be set in `paired.control` or in `summary.tableby`.

    args(paired.control)

    ## function (diff = TRUE, numeric.test = "paired.t", cat.test = "mcnemar",
    ##     ordered.test = "signed.rank", date.test = "paired.t", mcnemar.correct = TRUE,
    ##     signed.rank.exact = NULL, signed.rank.correct = TRUE, ...)
    ## NULL

## `summary.tableby` settings

Since the “paired” object inherits “tableby”, the `summary.tableby` function is what’s actually used to format and print the table.

    args(arsenal:::summary.tableby)

    ## function (object, ..., labelTranslations = NULL, text = FALSE,
    ##     title = NULL, pfootnote = FALSE, term.name = "")
    ## NULL