Hierarchical composite endpoints"

In hce: Design and Analysis of Hierarchical Composite Endpoints

knitr::opts_chunk$set(
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Introduction

Setup

knitr::include_graphics("hex-hce.png")

Load the package hce and check the version:

library(hce)
packageVersion("hce")

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

Definitions

Hierarchical composite endpoints (HCE) are a general class of endpoints combining different clinical outcomes of patients into a composite so as to preserve their different natures. A particular case of these endpoints is defined in a fixed follow-up period and accounts for the patient’s clinically most important outcome for the analysis. HCEs are analyzed using win odds and other win statistics [@gasparyanhierarchical].

Examples

Here we provide examples of HCE using in clinical trials from different therapeutic areas. General considerations for creating HCEs can be found in @gasparyan2022design.

COVID-19

The DARE-19 [@kosiborod2021effects;@kosiborod2021dapagliflozin] trial used an HCE to assess outcomes in patients hospitalized for COVID-19 and treated for 30 days. The COVID-19 HCE is presented below. It combines death, in hospital organ dysfunction events with clinical status at Day 30 for patients alive, still hospitalized but without previous organ dysfunction events, and hospital discharge as the most favorable outcome for patients discharging without organ dysfunction events and being alive at Day 30.

Below a higher category signifies a better outcome. Patients are ranked into one and only one category based on their clinically most severe event. For example, patients experiencing an in-hospital new or worsening organ dysfunction event then dying will be included in the category I.

HCE <- data.frame(Order = c("I", "II", "III", "IV", "V"), 
                  Category = c("Death", 
                               "More than one new or worsened organ dysfunction events", 
                               "One new or worsened organ dysfunction event", 
                               "Hospitalized at the end of follow-up (Day 30)", 
                               "Discharged from hospital before Day 30")
)
HCE

Patients in the category I are compared using the timing of the event, with an earlier event being a worse outcome (are assigned a lower rank). Similarly, in the category III the timing of the event is used for ranking patients within this category. In the category II patients are compared using the number of events with a higher number signifying a worse outcome. Patients in the category IV - hospitalized at the end of follow-up without previous worsening events - are further ranked according to oxygen support requirements at the hospital (IV.1 on high flow oxygen devices, IV.2 requiring supplemental oxygen, IV.3 not requiring supplemental oxygen, with a higher rank being a better outcome). Patients in the category V are compared using the timing of the event, but, the hospital discharge being a favorable outcome, here the earlier event signifies a better outcome than the late event (reverse of the ranking in categories I and III).

The simplest case of a COVID-19 HCE is an endpoint with ordinal scale outcomes assessed at a given timepoint. The endpoint uses 1-8 categories for assessing the physical limitations of hospitalized patients with COVID-19 after 15 or 30 days of treatment. But unlike the DARE-19 HCE, within each category it does not use the timing of events to reduce the ties in a paiwrise comparison of patients in the active group with patients in the control group. See, for example, COVID-19 and COVID-19b datasest ordinal scale outcomes [@beigel2020remdesivir].

table(COVID19)

The function hce::summaryWO() provides the number of wins, losses, and ties by categories. We can calculate the probability of ties from the provided numbers.

COVID19HCE <- hce(GROUP = COVID19$GROUP, TRTP = COVID19$TRTP)
SUM <- summaryWO(COVID19HCE, ref = "Placebo")$summary
SUM$Ptie <- round(SUM$TIE/SUM$TOTAL, 2)
SUM

Kidney HCE

The kidney HCE defined in @khce2 has the following ordinal outcomes (for the review of the topic see @khce1).

HCE2 <- data.frame(Order = c("I", "II", "III", "IV", "V", "VI", "VII"), 
                  Category = c("Death", 
                               "Dialysis or kidney transplantation", 
                               "Sustained GFR < 15 ml/min per 1.73 m2", 
                               "Sustained GFR decline from baseline of >= 57%", 
                               "Sustained GFR decline from baseline of >= 50%",
                               "Sustained GFR decline from baseline of >= 40%",
                               "Individual GFR slope")
)
HCE2

The dataset KHCE contains data on a kidney HCE outcomes

dat <- KHCE
Order <- c("Death (adj)", "Chronic dialysis (adj) >=90 days", 
           "Sustained eGFR<15 (mL/min/1.73 m2)", "Sustained >=57% decline in eGFR", 
           "Sustained >=50% decline in eGFR", "Sustained >=40% decline in eGFR", "eGFR slope")   
dat$GROUP <- factor(dat$GROUP, levels = Order)
table(dat$GROUP, dat$TRTP)

This dataset is derived from ADSL which contains baseline characteristics, ADLB laboratory measurements of kidney function, and ADET for the time-to-event outcomes with their timing. For the detailed derivation see the Technical Appendix in @khce2.

Heart Failure

In the Heart Failure population (see @kondo2023use) the following HCE was considered

HCE3 <- data.frame(Order = c("I", "II", "III", "IV"), 
                  Category = c("Cardiovascular death", 
                               "Total (first and recurrent) HF hospitalizations", 
                               "Total urgent HF visits", 
                               "Improvement/deterioration in KCCQ-TSS"))
HCE3

Dependent outcomes

To model dependent outcomes, several methods are available:

1. Joint Distribution Modeling Using Copulas: This method employs copulas to model the joint distribution of outcomes, capturing their dependence.

2. Random Frailty Modeling: This approach captures patient-level dependence between outcomes using a random frailty model.

3. Conditional Distribution Specification Through Multi-State Modeling: This technique uses multi-state models to describe the conditional distribution of outcomes.

Joint Distribution Modeling Using Copulas

Sklar's theorem [@sklar1959fonctions] shows that multivariate distribution functions can be expressed using a copula and univariate distributions. For a random vector $X^d=(X_1,\cdots,X_d)$ with a multivariate distribution function $H(x_1,\cdots,x_d),$ Sklar's theorem states that there is a copula $C(\cdot)$ such that: $$H(x_1,\cdots,x_d)=C(F_1(x_1),\cdots,F_d(x_d)),$$ where each component $X_j$ has the univariate distribution $F_j.$ A copula is essentially a multivariate distribution function where each univariate marginal distribution is uniform, describing the dependency structure of the multivariate distribution function $H(\cdot)$. To construct the multivariate distribution function, one combines each variable's univariate distributions $F_j$ with the copula.

If $X_j$ has distribution function $F_j,$ then $U_j=F_j(X_j)$ is uniformly distributed, allowing random variables $X_j\sim F_j$ to be simulated by generating uniform random variables $U_j$ and applying the inverse transformation:

$$F_j^{-1}(y)=\inf{x\in {\mathbf R}: \ \ F_j(x)\geq y}, \ \ \inf\varnothing=\infty.$$ Thus, if one has simulated a uniform random vector $U^d=(U_1,\cdots, U_d)$ from the copula $C(\cdot)$, the random vector $X^d=(X_1,\cdots,X_d)$ can be simulated as: $$(X_1,\cdots,X_d)=(F_1^{-1}(U_1),\cdots,F_d^{-1} (U_d)).$$ The main challenge remains in simulating from the given copula.

An Archimedean copula [@nelsen2006introduction] is one where:

$$C(u^d;\varphi)=\varphi(\varphi^{-1}(u_1)+\cdots+\varphi^{-1}(u_d)).$$ The function $\varphi:[0,+\infty]\rightarrow [0,1]$ is a generator - continuous, decreasing, with $\varphi(0)=1$ and $\lim_{t\rightarrow+\infty}\varphi(t)=0.$ When $\varphi(t)=e^{-t^\theta},\ \ \theta>1,$ the copula is called a Gumbel copula.

The Marshall-Olkin algorithm

By Bernstein's theorem, completely monotone Archimedean generators coincide with Laplace-Stieltjes transforms of distribution functions $F,$ determined by $\varphi=LS[F].$ The Marshall-Olkin algorithm [@marshall1988families] for sampling from an Archimedean copula involves:

1. Sampling $V\sim F=LS^{-1}[\varphi].$
2. Sampling $R_j\sim Exp(1),\,j\in{1,\cdots,d}.$
3. Setting $U_j=\varphi\left(\frac{R_j}{V}\right),\,j\in{1,\cdots,d}.$

The vector $U=(U_1,\cdots, U_d)$ is then a random vector from the Archimedean copula with generator $\varphi$. For the Gumbel copula, one needs to use the inverse Laplace-Stieltjes transform of a stable distribution [@nolan2020univariate; @hofert2011nested]: $$F\sim S(1/\theta, 1, \cos^\theta(\pi/(2\theta)), {\mathbf I}_{{\theta=1}},1)$$ modifying the first step to sample from a stable distribution.

Chambers-Mallows-Stuck method for simulating stable random variables

Chambers-Mallows-Stuck method [@chambers1976method] efficiently simulates stable variables with:

1. Generating independent uniform and exponential random variables $$\Theta\sim U\left[-\frac{\pi}{2},\frac{\pi}{2}\right] \text{ and } W\sim Exp(1).$$

2. Defining $\alpha=1/\theta,$ setting $b_{\tan}=\beta\tan\left(\frac{\alpha\pi}{2}\right),$ and $\theta_0=\arctan(b_{\tan})/\alpha,$ with $$C_{\tan}=(1+b_{\tan}^2)^\frac{1}{2\alpha}.$$

3. Utilizing the transformations: $$Z(\theta) = \frac{\sin(a_0) C_{\tan}}{\cos(\Theta)^\frac{1}{\alpha}}\left(\frac{\cos(a_0-\Theta)}{W}\right)^\frac{1-\alpha}{\alpha}, \ \ a_0=\alpha(\Theta+\theta_0),\,\theta>1.$$ $$Z(1)=\frac{2}{\pi}\left(\pi_\beta\tan(\Theta)-\beta\log\left(\frac{\pi}{2}W\frac{\cos(\Theta)}{\pi_\beta}\right)\right), \ \ \pi_\beta=\frac{\pi}{2}+\beta\Theta.$$ Finally, $\gamma Z + \delta$ has the desired distribution with $\gamma =[\cos(\pi/(2\theta))]^\theta$ and $\delta={\mathbf I}_{{\theta=1}}$ (and one needs to set $\beta=1$).

Note

In the original Chambers-Mallows-Stuck formula, the term $$\frac{1}{[\cos(\alpha\theta_0)\cos(\Theta)]^\frac{1}{\alpha}}$$ is replaced by $\frac{C_{\tan}}{[\cos(\Theta)]^\frac{1}{\alpha}},$ as suggested by the copula package [@hofert2025copula], which is based on the fact that $C_{\tan} = 1/(\cos(\alpha\theta_0))^{1/\alpha}.$ Indeed, one needs to show that

$$(1+b_{\tan}^2)^\frac{1}{2\alpha}=1/(\cos(\alpha\theta_0))^{1/\alpha},$$ which is equivalent to showing that $$1+\left[\beta\tan\left(\frac{\alpha\pi}{2}\right)\right]^2=\frac{1}{[\cos(\alpha\theta_0)]^2}.$$ And this is true because of the trigonometric identity $$1+[\tan(y)]^2=\frac{1}{[\cos(y)]^2}.$$ Here we have set $y=\alpha\theta_0=\arctan(b_{\tan})=\arctan\left(\beta\tan\left(\frac{\alpha\pi}{2}\right)\right)$ and hence $\tan(y)=\beta\tan\left(\frac{\alpha\pi}{2}\right).$

Implementation

The function simHCE() provides the implementation above with the argument theta specifying the dependence of outcomes.

Rates_A <- c(10, 20)
Rates_P <- c(20, 20)
dat1 <- simHCE(n = 2500, TTE_A = Rates_A, TTE_P = Rates_P, 
CM_A = -3, CM_P = -6, CSD_A = 15, fixedfy = 3, theta = 1, seed = 1)
dat2 <- simHCE(n = 2500, TTE_A = Rates_A, TTE_P = Rates_P, 
CM_A = -3, CM_P = -6, CSD_A = 15, fixedfy = 3, theta = 1.0001, seed = 1)
dat3 <- simHCE(n = 2500, TTE_A = Rates_A, TTE_P = Rates_P, 
CM_A = -3, CM_P = -6, CSD_A = 15, fixedfy = 3, theta = 10, seed = 1)
calcWO(dat1)
calcWO(dat2)
calcWO(dat3)

References

hce documentation built on June 8, 2025, 11:43 a.m.