combined-approach.Rmd

---
title: "Combined Approach"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Combined Approach}
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editor_options: 
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```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# Introduction
In this tutorial, we will explore the combined approach to clinical significance using R. The combined approaches use the anchor- or distribution-based approach in addition to the statistical approach. This is obviously stricter than the two methods on their own, but offers a more detailed interpretation. We will be working with the `antidepressants` and the `claus_2020` datasets, and the `cs_combined()` function to demonstrate various aspects of this approach.


## 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.

```{r}
library(clinicalsignificance)

antidepressants
claus_2020
```


# Claus, Wager & Bonnet Approach
This approach combines the statistical and anchor-based approach. 
The `cs_combinedl()` function is a tool for assessing clinical significance in this way. It allows you to determine if changes in patient outcomes are practically significant. Let's go through the basic usage and some advanced features of this function.


## Basic Analysis
Let's start with a basic statistical clinical significance analysis using the `antidepressants` dataset. We are interested in the Mind over Mood Depression Inventory (`mom_di`) measurements. For the statistical approach, a functional population must be defined. Suppose, we collected data from a non-clinical sample and determined a mean of 7 points and a standard deviation of also 7 points. Furthermore, an MID of 8 points will be regarded as minimally important.

```{r}
combined_results <- antidepressants |> 
  cs_combined(
    id = patient,
    time = measurement,
    outcome = mom_di,
    m_functional = 7,
    sd_functional = 7,
    cutoff_type = "c",
    mid_improvement = 8
  )
```


## Handling Warnings
Sometimes, as in the example above, 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.


```{r}
# Turning off the warning by specifying pre-measurement time
combined_results <- antidepressants |> 
  cs_combined(
    id = patient,
    time = measurement,
    outcome = mom_di,
    pre = "Before",
    m_functional = 7,
    sd_functional = 7,
    cutoff_type = "c",
    mid_improvement = 8
  )
```

Here's a breakdown of the code:

- `patient`, `measurement`, and `mom_di` represent the patient identifier, assessment time points, and HAM-D scores, respectively.
- `pre` and `post` specify the time points for the pre and post-assessment.
- `m_functional` and `sd_functional` define the functional population's mean and standard deviation. This information is used to calculate the population cutoff.
- `"c"` specifies the population cutoff of choice.
- `mid_improvement` sets the  for improvement to 8.


## Printing and Summarizing the Results
```{r}
# Print the results
combined_results

# Get a summary
summary(combined_results)
```


## Visualizing the Results
Visualizing the results can help you better understand the clinical significance of changes in patient outcomes.
```{r}
# Plot the results
plot(combined_results)

# Show clinical significance categories
plot(combined_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.

```{r}
# Clinical significance distribution analysis with more than two measurements
cs_results <- claus_2020 |>
  cs_combined(
    id = id,
    time = time,
    outcome = bdi,
    pre = 1,
    post = 4,
    m_functional = 7,
    sd_functional = 7,
    cutoff_type = "c",
    mid_improvement = 8
  )

# Display the results
cs_results
summary(cs_results)
plot(cs_results)
```


## Grouped Analysis
You can also perform a grouped analysis by providing a group column from the data. This is useful when comparing treatment groups or other categories.
```{r}
cs_results_grouped <- claus_2020 |>
  cs_combined(
    id = id,
    time = time,
    outcome = bdi,
    pre = 1,
    post = 4,
    m_functional = 7,
    sd_functional = 7,
    cutoff_type = "c",
    mid_improvement = 8,
    group = treatment
  )

# Display and visualize the results
cs_results_grouped
plot(cs_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.

```{r}
# Clinical significance analysis for outcomes where higher values are better
cs_results_who <- claus_2020 |>
  cs_combined(
    id,
    time,
    who,
    pre = 1,
    post = 4,
    m_functional = 7,
    sd_functional = 7,
    cutoff_type = "c",
    mid_improvement = 8,
    better_is = "higher"
  )

# Display the results
cs_results_who
```


# Jacobson & Truax Approach
The Jacobson & Truax approach combines the statistical with the distribution-based approach. For this, let's suppose that the reliability of the MoM-DI is 0.80.

```{r}
jt_results <- antidepressants |> 
  cs_combined(
    id = patient,
    time = measurement,
    outcome = mom_di,
    pre = "Before",
    m_functional = 7,
    sd_functional = 7,
    cutoff_type = "c",
    reliability = 0.80
  )

# Summarize and visualize the results
summary(jt_results)
plot(jt_results)
plot(jt_results, show = category)
```



# Conclusion
In this tutorial, you've learned how to perform clinical significance analysis using the `cs_combined()` function in R. This analysis may be crucial for determining the practical importance of changes in patient outcomes. By adjusting thresholds and considering grouped analyses, you can gain valuable insights for healthcare and clinical research applications.