Anchor-Based Approaches"

In clinicalsignificance: A Toolbox for Clinical Significance Analyses in Intervention Studies

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Introduction

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.

Prerequisites

Before we begin, ensure that you have the following prerequisites in place:

• R installed on your computer.
• Basic understanding of R programming concepts.

Looking at the Datasets

First, let's have a look at the datasets, which come with the package.

library(clinicalsignificance)

antidepressants
claus_2020

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
  )

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.

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.

anchor_results <- antidepressants |> 
  cs_anchor(
    id = patient,
    time = measurement,
    outcome = mom_di,
    pre = "Before",
    mid_improvement = 7
  )

Printing and Summarizing the Results

# Print the results
anchor_results

# Get a summary
summary(anchor_results)

Visualizing the Results

# Plot the results
plot(anchor_results)

# Show clinical significance categories
plot(anchor_results, show = category)

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)
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

# 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

# And plot the groups
plot(anchor_results_who)

Group Level Anchor-Based Approach

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.

Performing the Analysis

anchor_results_group_level <- claus_2020 |> 
  cs_anchor(
    id = id,
    time = time,
    outcome = bdi,
    pre = 1,
    post = 4,
    mid_improvement = 7,
    target = "group"
  )

Summarizing the Results

summary(anchor_results_group_level)

Analysis for Different Groups

claus_2020 |> 
  cs_anchor(
    id = id,
    time = time,
    outcome = bdi,
    pre = 1,
    post = 4,
    mid_improvement = 7,
    target = "group",
    group = treatment
  )

Comparing Groups

claus_2020 |> 
  cs_anchor(
    id = id,
    time = time,
    outcome = bdi,
    post = 4,
    mid_improvement = 7,
    target = "group",
    group = treatment,
    effect = "between"
  )

Conclusion

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.

clinicalsignificance documentation built on April 4, 2025, 12:19 a.m.