Hierarchical composite endpoints"
In hce: Design and Analysis of Hierarchical Composite Endpoints

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
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Introduction

Setup

knitr::include_graphics("hex-hce.png", dpi = 1000)

Load the package hce and check the version:

library(hce)
packageVersion("hce")

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

Definitions

Prioritized outcome composite endpoints (POCE) 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 the hierarchical composite endpoint (HCE). It is evaluated 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,

knitr::kable(head(ADSL), caption = "ADSL dataset structure\\label{adsl}")

ADLB laboratory measurements of kidney function,

knitr::kable(head(ADLB), caption = "ADLB dataset structure\\label{adlb}")

and ADET for the time-to-event outcomes with their timing.

knitr::kable(head(ADET), caption = "ADET dataset structure\\label{adet}")

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