Individual Level Anchor-Based Approach
The individual level anchor approach is a method to determine the
clinical significance of changes in individual patient outcomes over
time. It is centered around the minimally important difference (MDI) of
an instrument. If a change is equal or greater than this difference, a
clinically significant change is inferred. We will use the
cs_anchor() function for this analysis.
Basic Analysis
Let’s start with a basic clinical significance distribution analysis
using the antidepressants dataset. We are interested in the
Mind over Mood Depression Inventory (mom_di) measurements
and want to set Minimally Important Difference (MID) for an improvement
to a value of 7.
anchor_results <- antidepressants |>
cs_anchor(
id = patient,
time = measurement,
outcome = mom_di,
mid_improvement = 7
)
#> ℹ Your "Before" was set as pre measurement and and your "After" as post.
#> • If that is not correct, please specify the pre measurement with the argument
#> "pre".Here’s a breakdown of the code:
patient,measurement, andmom_diare variables representing patient identifiers, assessment time points, and the Mind over Mood Depression Intentory (MoM-DI) scores, respectively.mid_improvementsets the for improvement to 7.
Handling Warnings
Sometimes, you may encounter warnings when using this function. You can turn off the warning by explicitly specifying the pre-measurement time point using the pre parameter. This can be helpful when your data lacks clear pre-post measurement labels.
Printing and Summarizing the Results
# Print the results
anchor_results
#>
#> ── Clinical Significance Results ──
#>
#> Individual anchor-based approach with a 7 point decrease in instrument scores
#> indicating a clinical significant improvement.
#> Category | n | Percent
#> ----------------------------
#> Improved | 331 | 59.64%
#> Unchanged | 157 | 28.29%
#> Deteriorated | 67 | 12.07%
# Get a summary
summary(anchor_results)
#>
#> ── Clinical Significance Results ──
#>
#> Individual anchor-based analysis of clinical significance with a 7 point
#> decrease in instrument scores (mom_di) indicating a clinical significant
#> improvement.
#> There were 555 participants in the whole dataset of which 555 (100%) could be
#> included in the analysis.
#>
#> ── Individual Level Results
#> Category | n | Percent
#> ----------------------------
#> Improved | 331 | 59.64%
#> Unchanged | 157 | 28.29%
#> Deteriorated | 67 | 12.07%Visualizing the Results
Data with More Than Two Measurements
When working with data that has more than two measurements, you must explicitly define the pre and post measurement time points using the pre and post parameters.
claus_results <- claus_2020 |>
cs_anchor(
id = id,
time = time,
outcome = bdi,
pre = 1,
post = 4,
mid_improvement = 7
)
summary(claus_results)
#>
#> ── Clinical Significance Results ──
#>
#> Individual anchor-based analysis of clinical significance with a 7 point
#> decrease in instrument scores (bdi) indicating a clinical significant
#> improvement.
#> There were 43 participants in the whole dataset of which 40 (93%) could be
#> included in the analysis.
#>
#> ── Individual Level Results
#> Category | n | Percent
#> ---------------------------
#> Improved | 25 | 62.50%
#> Unchanged | 11 | 27.50%
#> Deteriorated | 4 | 10.00%
plot(claus_results)
Group the Analysis
It is also possible to provide a grouping variable present in your data to group the analysis. The resulting plot distinguishes the effects for the provided groups.
anchor_results_grouped <- claus_2020 |>
cs_anchor(
id = id,
time = time,
outcome = bdi,
pre = 1,
post = 4,
mid_improvement = 7,
group = treatment
)
anchor_results_grouped
#>
#> ── Clinical Significance Results ──
#>
#> Individual anchor-based approach with a 7 point decrease in instrument scores
#> indicating a clinical significant improvement.
#> Group | Category | n | Percent
#> -----------------------------------
#> TAU | Improved | 8 | 20.00%
#> TAU | Unchanged | 7 | 17.50%
#> TAU | Deteriorated | 4 | 10.00%
#> PA | Improved | 17 | 42.50%
#> PA | Unchanged | 4 | 10.00%
#> PA | Deteriorated | 0 | 0.00%
# And plot the groups
plot(anchor_results_grouped)
Analyzing Positive Outcomes
In some cases, higher values of an outcome may be considered better.
You can specify this using the better_is argument. Let’s
see an example with the WHO-5 score where higher values are considered
better. Suppose the MID is 4 in this case.
anchor_results_who <- claus_2020 |>
cs_anchor(
id = id,
time = time,
outcome = who,
pre = 1,
post = 4,
mid_improvement = 4,
better_is = "higher"
)
anchor_results_who
#>
#> ── Clinical Significance Results ──
#>
#> Individual anchor-based approach with a 4 point increase in instrument scores
#> indicating a clinical significant improvement.
#> Category | n | Percent
#> ---------------------------
#> Improved | 15 | 37.50%
#> Unchanged | 25 | 62.50%
#> Deteriorated | 0 | 0.00%
# And plot the groups
plot(anchor_results_who)