clinicalsignificance (version 2.0.0)

cs_percentage: Percentage-Change Analysis of Clinical Significance

Description

cs_percentage() can be used to determine the clinical significance of intervention studies employing the percentage-change approach. For this, each individuals relative change compared to the pre intervention measurement and if this change exceeds a predefined change in percent points, this change is then deemed clinically significant.

Usage

cs_percentage(
  data,
  id,
  time,
  outcome,
  group = NULL,
  pre = NULL,
  post = NULL,
  pct_improvement = NULL,
  pct_deterioration = NULL,
  better_is = c("lower", "higher")
)

Value

An S3 object of class cs_analysis and cs_percentage

Arguments

data

A tidy data frame

id

Participant ID

time

Time variable

outcome

Outcome variable

group

Grouping variable (optional)

pre

Pre measurement (only needed if the time variable contains more than two measurements)

post

Post measurement (only needed if the time variable contains more than two measurements)

pct_improvement

Numeric, percent change that indicates a clinically significant improvement

pct_deterioration

Numeric, percent change that indicates a clinically significant deterioration (optional). If this is not set, pct_deterioration will be assumed to be equal to pct_improvement

better_is

Which direction means a better outcome for the used instrument? Available are

• "lower" (lower outcome scores are desirable, the default) and

• "higher" (higher outcome scores are desirable)

Computational details

Each participants change is calculated and then divided by the pre intervention score to estimate the individual's percent change. A percent change for an improvement as well as a deterioration can be provided separately and if pct_deterioration is not set, it will be assumed to be the same as pct_improvement.

Categories

Each individual's change may then be categorized into one of the following three categories:

• Improved, the change is greater than the predefined percent change in the beneficial direction

• Unchanged, the change is within the predefined percent change

• Deteriorated, the change is greater than the predefined percent change, but in the disadvantageous direction

Data preparation

The data set must be tidy, which corresponds to a long data frame in general. It must contain a patient identifier which must be unique per patient. Also, a column containing the different measurements and the outcome must be supplied. Each participant-measurement combination must be unique, so for instance, the data must not contain two "After" measurements for the same patient.

Additionally, if the measurement column contains only two values, the first value based on alphabetical, numerical or factor ordering will be used as the pre measurement. For instance, if the column contains the measurements identifiers "pre" and "post" as strings, then "post" will be sorted before "pre" and thus be used as the "pre" measurement. The function will throw a warning but generally you may want to explicitly define the "pre" and "post" measurement with arguments pre and post. In case of more than two measurement identifiers, you have to define pre and post manually since the function does not know what your pre and post intervention measurements are.

If your data is grouped, you can specify the group by referencing the grouping variable (see examples below). The analysis is then run for every group to compare group differences.

See Also

Main clinical signficance functions cs_anchor(), cs_combined(), cs_distribution(), cs_statistical()

Examples

cs_results <- claus_2020 |>
  cs_percentage(
    id,
    time,
    hamd,
    pre = 1,
    post = 4,
    pct_improvement = 0.3
  )

cs_results
summary(cs_results)
plot(cs_results)


# You can set different thresholds for improvement and deterioration
cs_results_2 <- claus_2020 |>
  cs_percentage(
    id,
    time,
    hamd,
    pre = 1,
    post = 4,
    pct_improvement = 0.3,
    pct_deterioration = 0.2
  )

cs_results_2
summary(cs_results_2)
plot(cs_results_2)


# You can group the analysis by providing a group column from the data
cs_results_grouped <- claus_2020 |>
  cs_percentage(
    id,
    time,
    hamd,
    pre = 1,
    post = 4,
    pct_improvement = 0.3,
    group = treatment
  )

cs_results_grouped
summary(cs_results_grouped)
plot(cs_results_grouped)


# The analyses can be performed for positive outcomes as well, i.e., outcomes
# for which a higher value is beneficial
cs_results_who <- claus_2020 |>
  cs_percentage(
    id,
    time,
    who,
    pre = 1,
    post = 4,
    pct_improvement = 0.3,
    better_is = "higher"
  )

cs_results_who
summary(cs_results_who)
plot(cs_results_who)
plot(cs_results_who, show = category)