Date: 2025-11-26

# Analyzing Clinical Significance: The Combined Approach

#### 2025-11-26

## Introduction

The combined approach represents the most rigorous method for assessing clinical significance. It provides a more nuanced picture of patient outcomes by requiring two criteria to be met simultaneously:

1. Magnitude of Change: The change must be sufficiently large, either by being statistically reliable (distribution-based) or practically meaningful (anchor-based).
2. End-State Functioning: The patient must cross the threshold from a dysfunctional or “clinical” population to a functional or “non-clinical” one (statistical approach).

This dual requirement allows for a richer classification of outcomes, distinguishing between patients who have merely “Improved” and those who have truly “Recovered”. This vignette demonstrates how to use the `cs_combined()` function to apply these powerful models.

    library(clinicalsignificance)

## The Classic Jacobson & Truax (JT) Approach

The most common combined method, proposed by Jacobson and Truax (1991), integrates the distribution-based and statistical approaches. A patient’s change is considered clinically significant if:

1. The change is reliable (i.e., it exceeds the measurement error of the instrument).
2. The patient’s final score falls within the range of a functional population.

### Example Analysis

We will again use the `claus_2020` dataset. To perform this analysis, we need summary statistics for a functional (non-clinical) population on the BDI-II (`m_functional`, `sd_functional`) and an estimate of the instrument’s reliability.

    # Perform the JT combined analysis
    jt_combined <- claus_2020 |>
      cs_combined(
        id = id,
        time = time,
        outcome = bdi,
        pre = 1,
        post = 4,
        m_functional = 7.69,
        sd_functional = 7.52,
        reliability = 0.801
      )

    # Display the summary of results
    summary(jt_combined)
    #>
    #> ---- Clinical Significance Results ----
    #>
    #> Approach:     Distribution-based
    #> RCI Method:   JT
    #> N (original): 43
    #> N (used):     40
    #> Percent used: 93.02%
    #> Outcome:      bdi
    #> Cutoff Type:  a
    #> Cutoff:       19.16
    #> Outcome:      bdi
    #> Reliability:  0.801
    #>
    #> -- Cutoff Descriptives
    #>
    #> M Clinical | SD Clinical | M Functional | SD Functional
    #> -------------------------------------------------------
    #> 35.48      |        8.16 |         7.69 |          7.52
    #>
    #>
    #> -- Results
    #>
    #> Category     |  N | Percent
    #> ---------------------------
    #> Recovered    | 10 |  25.00%
    #> Improved     |  8 |  20.00%
    #> Unchanged    | 22 |  55.00%
    #> Deteriorated |  0 |   0.00%
    #> Harmed       |  0 |   0.00%

### Visualizing the JT Approach

The plot for a combined analysis is particularly informative. It includes both the shaded area for reliable change (from the distribution-based approach) and the cutoff lines for population status (from the statistical approach).

- Dashed Lines: The cutoff separating the clinical (right/top) and functional (left/bottom) populations.
- Shaded Area: The zone of “no reliable change”.

    plot(jt_combined, show_group = "category")
    #> Ignoring unknown labels:
    #> • colour : "Group"

The resulting categories are interpreted as follows: - Recovered (blue): Patients who showed a reliable improvement and ended up in the functional range. - Improved (light green): Patients who showed a reliable improvement but remained in the clinical range. - Unchanged (yellow): Patients with no reliable change, regardless of their population status. - Deteriorated (orange): Patients who showed a reliable worsening but remained in the clinical range. - Harmed (dark green): Patients who showed a reliable worsening and moved from the functional to the clinical range.

## The Claus, Wager & Bonnet (CWB) Approach

As an alternative, `clinicalsignificance` allows you to combine the anchor-based and statistical approaches. This method is useful when a well-established Minimal Important Difference (MID) is available and may offer a more practically interpretable criterion for change than statistical reliability.

Here, a patient’s change is clinically significant if: 1. The change is meaningful (i.e., it meets or exceeds the MID). 2. The patient’s final score falls within the range of a functional population.

### Example Analysis

We use the same functional population data as before but replace the `reliability` argument with `mid_improvement`.

    # Perform the anchor-based combined analysis
    anchor_combined <- claus_2020 |>
      cs_combined(
        id = id,
        time = time,
        outcome = bdi,
        pre = 1,
        post = 4,
        m_functional = 7.69,
        sd_functional = 7.52,
        mid_improvement = 7
      )

    # Display the summary of results
    summary(anchor_combined)
    #>
    #> ---- Clinical Significance Results ----
    #>
    #> Approach:            Distribution-based
    #> RCI Method:          CWB
    #> N (original):        43
    #> N (used):            40
    #> Percent used:        93.02%
    #> Outcome:             bdi
    #> Cutoff Type:         a
    #> Cutoff:              19.16
    #> Outcome:             bdi
    #> MID (Improvement):   7
    #> MID (Deterioration): 7
    #>
    #> -- Cutoff Descriptives
    #>
    #> M Clinical | SD Clinical | M Functional | SD Functional
    #> -------------------------------------------------------
    #> 35.48      |        8.16 |         7.69 |          7.52
    #>
    #>
    #> -- Results
    #>
    #> Category     |  N | Percent
    #> ---------------------------
    #> Recovered    | 13 |  32.50%
    #> Improved     | 12 |  30.00%
    #> Unchanged    | 11 |  27.50%
    #> Deteriorated |  4 |  10.00%
    #> Harmed       |  0 |   0.00%

Notice the subtle shift in classifications: with the MID as the criterion, more patients are categorized as “Improved” or “Recovered” compared to the stricter reliability criterion of the JT approach in this specific example.

### Visualizing the CWB Approach

The plot is interpreted similarly, but the shaded area is now defined by the MID instead of the Reliable Change Index.

    plot(anchor_combined, show_group = "category")
    #> Ignoring unknown labels:
    #> • colour : "Group"

## Summary and Next Steps

The combined approach offers a powerful and nuanced assessment of treatment outcomes by considering both the magnitude of change and the patient’s final functional status.

- The Jacobson & Truax (JT) approach is the classic standard, requiring a reliable change and a shift in population status.
- The Claus, Wager & Bonnet (CWB) approach is a valuable alternative when a meaningful MID is known, defining change in practical rather than purely statistical terms.

Choosing between them depends on the available information (reliability vs. MID) and the specific research question.