---
title: "Distribution-Based Approach"
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vignette: >
  %\VignetteIndexEntry{Distribution-Based 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 percentage-change approach to clinical significance using R. The distribution-based approach is centered around the distribution of the outcome of interest. This is used to calculate the minimal detectable change (MDC), which is the change that has to be achieved to be (likely) greater than the inherent measurement error of the used instrument. We will be working with the `claus_2020` dataset and the `cs_percentage()` 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.

```{r}
library(clinicalsignificance)

antidepressants
claus_2020
```


# Distribution-Based Approach
The `cs_distribution()` function allows us to analyze the clinical significance by considering the distribution of patient measurements. We can specify various parameters to customize the analysis. Let's explore the key aspects of this function through examples.


## 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 a reliability threshold of 0.80.


```{r}
# Basic clinical significance distribution analysis
antidepressants |>
  cs_distribution(patient, measurement, mom_di, reliability = 0.80)
```


## 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.


```{r}
# Turning off the warning by specifying pre-measurement time
cs_results <- antidepressants |>
  cs_distribution(
    patient,
    measurement,
    mom_di,
    pre = "Before",
    reliability = 0.80
  )
```


## Summarize and plot the results
```{r}
summary(cs_results)
plot(cs_results)
plot(cs_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_distribution(
    id,
    time,
    hamd,
    pre = 1,
    post = 4,
    reliability = 0.80
  )

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


## Changing the RCI Method
You can change the Reliable Change Index (RCI) method by specifying the `rci_method` parameter. In this example, we use the "HA" method.

```{r}
# Clinical significance distribution analysis with a different RCI method
cs_results_ha <- claus_2020 |>
  cs_distribution(
    id,
    time,
    hamd,
    pre = 1,
    post = 4,
    reliability = 0.80,
    rci_method = "HA"
  )

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

## Grouped Analysis
You can also perform a grouped analysis by providing a grouping variable. This is useful when comparing different treatment groups or categories.

```{r}
# Grouped analysis
cs_results_grouped <- claus_2020 |>
  cs_distribution(
    id,
    time,
    hamd,
    pre = 1,
    post = 4,
    group = treatment,
    reliability = 0.80
  )

# Display the results
summary(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. Suppose the reliability for the WHO-5 is 0.85.

```{r}
distribution_results_who <- claus_2020 |> 
  cs_distribution(
    id = id,
    time = time,
    outcome = who,
    pre = 1,
    post = 4,
    reliability = 0.85,
    better_is = "higher"
  )

distribution_results_who

# And plot the groups
plot(distribution_results_who)
```

## Using More Than Two Measurements with HLM Method
If you have more than two measurements and want to use the Hierarchical Linear Modeling (HLM) method, you can specify the `rci_method` argument accordingly.

```{r}
# Clinical significance distribution analysis with HLM method
cs_results_hlm <- claus_2020 |>
  cs_distribution(
    id,
    time,
    hamd,
    rci_method = "HLM"
  )

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

# Conclusion
In this tutorial, you've learned how to perform clinical significance distribution analysis using the cs_distribution function in R. This analysis is valuable for understanding the practical significance of changes in patient measurements. By customizing parameters and considering group-level analysis, you can gain valuable insights for healthcare and clinical research applications.
