---
title: "Introduction"
date: "`r format(as.Date('2025-12-01'), '%d %B, %Y')`"
author:
  - name: "Samvel B. Gasparyan"
    affiliation: https://gasparyan.co/
output:
  rmarkdown::html_document:
          theme: "darkly"
          highlight: "zenburn"
          toc: true
          toc_float: true
          link-citations: true 
bibliography: REFERENCES.bib
biblio-style: science
vignette: >
  %\VignetteIndexEntry{Introduction}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
R <- function() knitr::include_graphics("Rlogo.png", dpi = 5000)
```

## `hce` package intro

### Background

```{r echo=FALSE, out.width = '33%'}
knitr::include_graphics("hex-hce.png")
```

The purpose of the package is to simulate and analyze hierarchical composite endpoints (HCEs). The primary analysis method is win odds, but other win statistics (win ratio, net benefit)[@dong2023win] are also implemented, provided there is no censoring. Win odds uses the DeLong-DeLong-Clarke-Pearson fromula [@delong1988comparing] for the variance of the win proportion and is based on the Brunner-Munzel test [@brunner2000nonparametric]. 

The power and sample size formulas consider different alternative classes ("shifted", "ordered", and "max" for the maximum value of the standard deviation). All formulas are derived from @bamber1975area. By default, Noether's formula [@noether1987sample] is used for shifted distributions. For more information on the power calculations for shifted distributions see also @gasparyan2021power, @gasparyan2022comments. 

For a review of designing HCEs in clinical trials, see @gasparyan2022design, also @gasparyanhierarchical and @khce1. The *Basic Data Structure (BDS),* which conforms to the Analysis Data Model (ADaM) [@CDISC] principles for hierarchical composite endpoints, is detailed in @gasp2024bds. To visualize HCEs, the *maraca* plot [@karpefors2023maraca] can be utilized.

The stratified and adjusted win odds are calculated based on the randomization-based covariate adjustment theory developed in @koch1998issues (for a review, see @gasparyan2021adjusted).

All implementations are rank-based. For the review of methodology see [@brunner2018rank].

### Setup

Load the package `hce` and check the version

```{r eval = TRUE}
library(hce)
packageVersion("hce")
```

```{r eval = FALSE, include = FALSE}
devtools::load_all()
```

For citing the package, run `citation("hce")` [@hce].

### Contents

List the functions and the datasets in the package

```{r}
ls("package:hce")
```

In brief, the package contains the following:

1. Datasets: use `data(package = "hce")` for the list of all datasets included in the package.

- Simulated datasets - `HCE1 - HCE4` that contain two treatment groups and analysis values `AVAL` of a hierarchical composite endpoint. 

- The datasets `COVID19`, `COVID19b`, `COVID19plus` of COVID-19 ordinal scale outcomes [@beigel2020remdesivir; @kalil2021baricitinib].

- The datasets `ADET` (event-time), `ADLB` (laboratory), `ADSL` (subject-level baseline characteristics) for kidney events and their timing, kidney-related laboratory measurements of eGFR (estimated glomerular filtration rate), and, based on this, the derived kidney hierarchical composite endpoint dataset `KHCE` for the same patients [@khce2].

2. Functions to create `hce` objects:

- `hce(), as_hce(), simHCE()` (see @gasp2024bds). 

- The function `simKHCE()` simulates continuous eGFR (estimated Glomerular Filtration Rate) values over time and derives an `hce` object for the kidney HCE [@khce2]. 

- The function `simORD()` simulates ordinal outcomes by categorization of *beta* distributions.

3. Win odds (win ratio with ties) calculation generic functions for hierarchical composite endpoints:

- `calcWO(), summaryWO()` (see @gasparyan2021power). Win odds uses the DeLong-DeLong-Clarke-Pearson formula [@delong1988comparing] for the variance of the win proportion and is based on the Brunner-Munzel test [@brunner2000nonparametric]. 

4. Win statistics (win odds, win ratio, net benefit, Goodman Kruskal's `gamma`) and their confidence intervals calculation function:

- `calcWINS()` (see @gasparyanhierarchical). The argument `SE_WP_Type` by default is set to `biased` since it produces DeLong-DeLong-Clarke-Pearson variance (the same as in `calcWO()`) which is biased for small samples. The `SE_WP_Type` can be changed to `unbiased` to produce Brunner-Konietschke version [@brunner2025unbiased] of the Bamber [@bamber1975area] estimator for the variance of win proportion.

- Back-calculation of number of wins, losses, and ties given the win odds and win ratio using the function `propWINS()`. 

5. Adjusted win odds calculation for a single, numeric covariate:

- `regWO()` (see @gasparyan2021adjusted; @gasparyanhierarchical).

6. Stratified win odds with a possible adjustment after stratification with a single, numeric covariate:

- `stratWO()` (see @gasparyan2021adjusted; @gasparyanhierarchical).

7. Power, sample size, and minimum detectable win odds calculation functions:

- `powerWO(), sizeWO(), minWO()` provide tools for the win odds power, sample size, and minimum detectable treatment effect calculation for different alternative classes ("shifted", "ordered", and "max" for the maximum value of the standard deviation) based on the win odds. All formulas are from @bamber1975area. By default, uses Noether's formula [@noether1987sample] for shifted distributions. For shifted distributions see also @gasparyan2021power, @gasparyan2022comments.

- Win ratio sample size calculation formula `sizeWR()` [@yu2022sample]. 

8. Print and plot methods for `hce_results` objects, generated by the functions `powerWO(), sizeWO(), minWO()`. 

9. A plot method for `hce` objects (created by `as_hce()`) to provide the ordinal dominance graph [@bamber1975area].

10. The function `IWP()` to calculate patient-level individual win proportions [@gasparyan2021adjusted;@gasparyan2021power].

## `hce` Objects


### `hce()` Function

The main objects in the package are called `hce` objects, which are data frames with a specific structure matching the design of *hierarchical composite endpoints (HCE)*. These are complex endpoints that combine different clinical events into a composite, using a hierarchy to prioritize the clinically most important event for a patient. These endpoints are implemented in clinical trials across different therapeutic areas. See, for example, @gasparyan2022design for an implementation in a COVID-19 setting and some practical considerations for constructing hierarchical composite endpoints. For the Chronic Kidney Disease (CKD) outcomes see [@khce1; @khce2; @khce3]. 

HCEs are ordinal endpoints that can be thought of as having 'greater', 'less', or 'equal' defined for them, but without a definition of how much greater or less. In this sense, the ordinal outcomes can be represented as numeric vectors *as long as numeric operations (e.g., sum or division) are not performed on them.*

`hce` objects can be constructed using the helper function `hce()`, which has the following arguments:

```{r}
args("hce")
```
 
We see that the required arguments are `GROUP`, which specifies the clinically most important event of a patient to be included in the analysis, and `TRTP`, which specifies the (planned) treatment group of a patient (exactly two treatment groups should be present). Note that:

- The `hce` structure assumes that only one event per patient is present for the analysis, meaning that the resulting `hce` object created by the `hce()` function is a patient-level dataset. The function `hce()` does not select the clinically most important event of the patient but requires it to be already done when calling it.

- The argument `TRTP` should have exactly two levels.

Consider the following example of ordinal outcomes 'I', 'II', and 'III':

```{r}
set.seed(2022)
n <- 100
dat <- hce(GROUP = rep(x = c("I", "II", "III"), each = 100), 
           TRTP = sample(x = c("Active", "Control"), size = n*3, replace = TRUE))
class(dat)
```

This dataset has the appropriate structure of `hce` objects, but its class inherits from an object of class `data.frame`. This means that all functions available for data frames can be applied to `hce` objects, for example, the function `head()`:

```{r}
head(dat)
```

We see that the dataset has a very specific structure. The column `PARAMN` shows how the function `hce()` generated the order of given events (it uses usual alphabetic order for the unique values in the `GROUP` column to determine the clinical importance of events):

```{r}
unique(dat[, c("GROUP", "PARAMN")])
```

In the class `hce`, higher values for the ordering mean clinically less important events. For example, death, which is the most important event, should always get the lowest ordinal value. If there is a need to specify the order of outcomes, then the `GROUP` argument can be provided as a factor with the levels specifying the necessary order:

```{r}
set.seed(2022)
n <- 100
GROUP = rep(x = c("I", "II", "III"), each = 100) 
GROUP <- factor(GROUP, levels = c("III", "II", "I"))
dat <- hce(GROUP = GROUP, 
           TRTP = sample(x = c("Active", "Control"), size = n*3, replace = TRUE))
unique(dat[, c("GROUP", "PARAMN")])
```

This means that the clinically most important event is 'III' instead of 'I'. The argument `AVAL0` is meant to help in cases where we want to introduce sub-ordering within each `GROUP` category. For example, if two events in the group 'I' can be compared based on other parameters, then the `AVAL0` argument can be specified to take that into account.

Below we use the built-in data frame `HCE1` to construct an `hce` object. Before specifying the order of events, it is a good idea to check what are the unique events included in the `GROUP` column:


```{r}
data(HCE1)
unique(HCE1$GROUP)
```

Therefore, we can construct the following object using the `hce()` function:

```{r}
HCE <- hce(GROUP = factor(HCE1$GROUP, levels = c("TTE1", "TTE2", "TTE3", "TTE4", "C")), 
           TRTP = HCE1$TRTP, AVAL0 = HCE1$AVAL0, PADY = 1080)
class(HCE)
head(HCE)
```

The object's class is `adhce`, which inherits from `hce`. This design indicates that the object includes additional columns.

### Create an `hce` Object from a Data Frame

Consider the dataset `HCE1`, which is part of the package `hce`:

```{r}
data(HCE1, package = "hce")
class(HCE1)
head(HCE1)
```

This dataset has the appropriate structure of `hce` objects, but its class is `data.frame.` A generic way of coercing data structures to an `hce` object is to use the function `as_hce()`. This function performs checks (using an internal validator function) and creates an `hce` object from the given data structure (using an internal constructor function). If coercion is not possible, it will throw an error explaining the issue.

```{r}
dat1 <- as_hce(HCE2)
str(dat1)
```


### Simulate `hce` Objects Using `simHCE()`

To simulate values from a hierarchical composite endpoint, we use the function `simHCE()`, which has the following arguments:

```{r}
args("simHCE")
```

- The vector arguments `TTE_A` and `TTE_P` specify the event rates per year for time-to-event outcomes in the active and control groups, respectively. The function assumes a Weibull survival function with the same shape parameter for simulating all time-to-event outcomes in both treatment groups (by default `shape = 1`, which assumes an exponential survival function). These two vectors should have the same length, which indicates the number of time-to-event outcomes. 

- By default, the event rates are presented per 100 patient-years (`pat = 100`), which can be changed using the argument `pat`. The function simulates event times in days, and the `yeardays = 360` argument can be used to change the number of days in a year (e.g., 365 or 365.25).

- The function simulates events during a fixed follow-up period only, and the `fixedfy` argument can be used to change the length of the follow-up (in years).

- The function simulates the continuous outcome from a normal (default) or log-normal (if `logC = TRUE`) distribution with given means and standard deviations for two treatment groups.

```{r}
Rates_A <- c(1.72, 1.74, 0.58, 1.5, 1) 
Rates_P <- c(2.47, 2.24, 2.9, 4, 6) 
dat3 <- simHCE(n = 2500, n0 = 1500, TTE_A = Rates_A, 
               TTE_P = Rates_P, 
               CM_A = -3, CM_P = -6, 
               CSD_A = 16, CSD_P = 15, 
               fixedfy = 3, seed = 2023)
```

```{r}
class(dat3)
head(dat3)
```




## Generics for `hce` Objects

As we see, the function `simHCE()` creates an object of type `hce`, which inherits from the built-in class `data.frame`. We can check all implemented methods for this new class as follows:
```{r}
methods(class = "hce")
```

The function `calcWO()` calculates the win odds and its confidence interval, while `summaryWO()` provides a more detailed calculation of win odds, including the number of wins, losses, and ties by `GROUP` categories.

```{r}
HCE <- hce(GROUP = factor(HCE3$GROUP, levels = c("TTE1", "TTE2", "TTE3", "TTE4", "C")), 
           TRTP = HCE3$TRTP, AVAL0 = HCE3$AVAL0, PADY = 1080)
calcWO(HCE)
calcWINS(HCE)  
HCE$TRTP <- factor(HCE$TRTP, levels = c("P", "A"))
plot(HCE, fill = TRUE, col = "#865A4F", type = 'l', lwd = 2)
abline(a = 0, b = 1, lwd = 2, col = "#999999", lty = 2)
```

To check the generic functions available for the `adhce` class by running the following command:

```{r}
methods(class = "adhce")
```

The main difference between `summaryWO.hce()` and `summaryWO.adhce()` is that `summaryWO.adhce()` also provides a group-wise summary, in addition to the overall summary:

```{r}
summaryWO(HCE)
```

An important addition to `summaryWO.adhce()` is the `cumsummary_by_GROUP` output, which reports cumulative wins, losses, and ties as outcomes are added sequentially according to the priority order:

```{r}
res0 <- summaryWO(HCE, ref = "P")
res <- res0$cumsummary_by_GROUP
barplot(PROP ~ WINS + GROUPN, data = res, 
col = c("darkgreen", "darkred", "darkblue"), 
xlab = "Proportions", xlim = c(0, 1), 
ylab = "Cumulative components by prioritization", 
legend.text = unique(res$WINS), beside = TRUE, horiz = TRUE)
grid()
```


## References


