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In this beginner-friendly tutorial, we will explore anchor-based
approaches to clinical significance using R. Anchor-based approaches are
commonly used in clinical research to assess the meaningfulness of
change in patient outcomes. We will be working with the
claus_2020
dataset and the cs_anchor()
function to demonstrate various aspects of these approaches.
Before we begin, ensure that you have the following prerequisites in place:
First, let’s have a look at the datasets, which come with the package.
library(clinicalsignificance)
antidepressants
#> # A tibble: 1,110 × 4
#> patient condition measurement mom_di
#> <chr> <fct> <fct> <dbl>
#> 1 S001 Wait List Before 50
#> 2 S001 Wait List After 36
#> 3 S002 Wait List Before 40
#> 4 S002 Wait List After 32
#> 5 S003 Wait List Before 38
#> 6 S003 Wait List After 41
#> 7 S004 Wait List Before 29
#> 8 S004 Wait List After 44
#> 9 S005 Wait List Before 37
#> 10 S005 Wait List After 45
#> # ℹ 1,100 more rows
claus_2020
#> # A tibble: 172 × 9
#> id age sex treatment time bdi shaps who hamd
#> <dbl> <dbl> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 54 Male TAU 1 33 9 0 25
#> 2 1 54 Male TAU 2 28 6 3 17
#> 3 1 54 Male TAU 3 28 9 7 13
#> 4 1 54 Male TAU 4 27 8 3 13
#> 5 2 52 Female PA 1 26 11 2 15
#> 6 2 52 Female PA 2 26 10 0 16
#> 7 2 52 Female PA 3 25 10 0 7
#> 8 2 52 Female PA 4 19 9 3 11
#> 9 3 54 Male PA 1 15 2 0 28
#> 10 3 54 Male PA 2 13 5 9 17
#> # ℹ 162 more rows
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.
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
, and
mom_di
are variables representing patient identifiers,
assessment time points, and the Mind over Mood Depression Intentory
(MoM-DI) scores, respectively.mid_improvement
sets the for improvement to 7.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.
# 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%
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)
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)
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)
The group level anchor-based approach assesses clinical significance for groups of patients, often in the context of treatment comparisons. Let’s explore this approach.
summary(anchor_results_group_level)
#>
#> ── Clinical Significance Results ──
#>
#> Groupwise anchor-based analysis of clinical significance (within groups) 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.
#>
#> ── Group Level Results
#> Difference | [Lower | Upper] | CI-Level | n | Category
#> -------------------------------------------------------------------------------------
#> -9.46 | -13.08 | -5.90 | 0.95 | 40 | Probably clinically significant effect
claus_2020 |>
cs_anchor(
id = id,
time = time,
outcome = bdi,
pre = 1,
post = 4,
mid_improvement = 7,
target = "group",
group = treatment
)
#>
#> ── Clinical Significance Results ──
#>
#> Groupwise anchor-based approach (within groups) with a 7 point decrease in
#> instrument scores indicating a clinical significant improvement.
#> Group | Median Difference | [Lower | Upper] | CI-Level | n | Category
#> -------------------------------------------------------------------------------------------------
#> TAU | -4.23 | -9.00 | 0.30 | 0.95 | 19 | Statistically not significant
#> PA | -13.68 | -18.06 | -8.95 | 0.95 | 21 | Large clinically significant effect
claus_2020 |>
cs_anchor(
id = id,
time = time,
outcome = bdi,
post = 4,
mid_improvement = 7,
target = "group",
group = treatment,
effect = "between"
)
#>
#> ── Clinical Significance Results ──
#>
#> Groupwise anchor-based approach (between groups) with a 7 point decrease in
#> instrument scores indicating a clinical significant improvement.
#> Group 1 | Group 2 | Median Difference | [Lower | Upper] | CI-Level | n (1) | n (2) | Category
#> ---------------------------------------------------------------------------------------------------------------------------
#> TAU | PA | -10.21 | -18.45 | -2.55 | 0.95 | 19 | 21 | Probably clinically significant effect
Anchor-based approaches are valuable tools in clinical research for assessing the clinical significance of changes in patient outcomes. In this tutorial, we’ve covered the individual and group-level anchor approaches, and you’ve learned how to perform these analyses using R. These techniques can help researchers and healthcare professionals make informed decisions about the effectiveness of treatments and interventions.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.