Composite Endpoint and Competing Risk Model

A competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. For example, when the primary outcome is death due to cardiovascular causes, then death due to non-cardiovascular causes serves as a competing risk, because subjects who die of non-cardiovascular causes (e.g., death due to cancer) are no longer at risk of death due to a cardiovascular cause. However, when the primary outcome is all-cause mortality, then competing risks are absent, as there are no events whose occurrence precludes the occurrence of death due to any cause. In event-driven clinical trials, if a study subject drops out from the study prior to occurrence of the event in interest, the event of dropout precludes the occurrence of the event in interest, this is also a competing risk.

Competing risk issue occurs in clinical trials with a composite endpoint or an endpoint with composite outcome. A composite outcome consists of two or more component outcomes. Patients who have experienced any one of the events specified by the components are considered to have experienced the composite outcome. The main advantages supporting the use of a composite outcome are that it increases statistical efficiency because of higher event rates, which reduces sample size requirement, costs, and time; it helps investigators avoid an arbitrary choice between several important outcomes that refer to the same disease process; and it is a means of assessing the effectiveness of a patient reported outcome that addresses more than one aspect of the patient’s health status

It is common to use a composite endpoint in clinical trials, especially in clinical trials where the primary interest is to reduce the adverse outcomes, but the occurrence of these adverse outcomes may not be frequent enough. If we do a study with each individual component as the endpoint, the sample size required will be too large.

MACE (major adverse cardiac events) is a composite endpoint frequently used in clinical trials assessing the treatment effect in cardiac health. MACE is defined as any event of all-cause mortality, myocardial infarction, or stroke. If a patient died during the study, the MI or stroke will not be observed. If a MI or Stroke event occurred and the subject is discontinued from the study once one of these events occurred, the death event will not be observed – one component is a competing risk for another component.

In clinical trials in pulmonary arterial hypertension, the composite endpoint is used to evaluate the treatment effect in reducing the mortality and morbidity events. EMA guidance “GUIDELINE ON THE CLINICAL INVESTIGATIONS OF MEDICINAL PRODUCTS FOR THE TREATMENT OF PULMONARY ARTERIAL HYPERTENSION “ suggested the time to clinical worsening as the primary efficacy endpoint where the clinical worsening is defined as a composite endpoint consisting of:

1. All-cause death.
2. Time to non-planned PAH-related hospitalization.
3. Time to PAH-related deterioration identified by at least one of the following parameters:

• increase in WHO FC;
• deterioration in exercise testing
• signs or symptoms of right-sided heart failure

In a phase III study “Selexipag for the Treatment of Pulmonary

Arterial Hypertension”, the primary end point in a time-to-event analysis was a composite of death or a complication related to pulmonary arterial hypertension, whichever occurred first, up to the end of the treatment period. The composite endpoint includes the following events:

• death (all-cause mortality)
• hospitalization for worsening of PAH based on criteria defined in the study protocol
• worsening of PAH resulting in need for lung transplantation or balloon atrial septostomy initiation of parenteral (subcutaneous or intravenous) prostanoid therapy or chronic oxygen therapy due to worsening of PAH
• disease progression (patients in modified NYHA/WHO functional class II or III at Baseline) confirmed by a decrease in 6MWD from Baseline (≥ 15%, confirmed by 2 tests on different days within 2 weeks) and worsening of NYHA/WHO functional class
• disease progression (patients in modified NYHA/WHO functional class III or IV at Baseline) confirmed by a decrease in 6MWD from Baseline (≥ 15%, confirmed by 2 tests on different days within 2 weeks) and need for additional PAH-specific therapy.

There is a competing risk issue here, for example, lung transplantation and death are competing each other. If patient has a lung transplantation, the disease course will be changed, and the chance of death and occurrence of other events will be altered. 

A common approach to avoid the competing risk issue is to analyze the time to first event (any one of the components defined in the composite endpoint) as the primary efficacy endpoint even though this approach is often criticized because the importance / severity of these components is not equal (death should be given way more weight than other non-fatal events). FDA seems to be totally comfortable with the time to first event approach in both composite endpoint situation (as evidenced by the approval ofSelexipeg) and recurrent event situation (as evidenced by the FDA advisorycommittee meeting discussion). In a panel discussion at the regulatory-industry workshop in 2017 on the topic of Better Characterization of Disease Burden by Using Recurrent Event Endpoints (View Presentation), Drs Bob Temple and Norman Stockbridge both commented that FDA is fine with the time to fist event analysis as long as further analyses  are performed to evaluate the treatment effect on each individual component.

Competing risk model may be used in statistical analysis of the clinical trial data either as the primary method or as sensitivity analysis. In Schaapveld et al (2015) Second Cancer Risk Up to 40 Years after Treatment for Hodgkin’s Lymphoma, the competing risk model was used for analyzing the cumulative incidence of second cancers.

The cumulative incidence of second cancers was estimated with death treated as a competing risk, and trends over time were evaluated in competing-risk models, with adjustment for the effects of sex, age, and smoking status when appropriate

Competing risk model is more likely to be used as a sensitivity analysis, for example, in SPRINT study “A Randomized Trial of Intensive versus Standard Blood-Pressure Control”, The Fine–Gray model for the competing risk of death was used as a sensitivity analysis.

There are quite some discussions about the competing risk model in clinical trials:

• Peter C. Austin and Jason P. Fine (2017) Accounting for competing risks in randomized controlled trials: a review and recommendations for improvement
• Loren Mell and Jong-Hyeon Jeong (2010) Pitfalls of Using Composite Primary End Points in the Presence of Competing Risks
• Noordzij et al (2013) When do we need competing risks methods for survival analysis in nephrology?
• R. Sapir-Pichhadze et al (2016) Survival Analysis in the Presence of Competing Risks: The Example of Waitlisted Kidney Transplant Candidates

In the situation where there is a competing risk issue, the Grey’s method or Fine and Gray method can be used. These methods are based on the paper below:

• Gray, R. J. (1988), “A Class of K-Sample Tests for Comparing the Cumulative Incidence of a Competing  Risk,” Annals of Statistics, 16, 1141–1154.
• Fine, J. P. and Gray, R. J. (1999), “A Proportional Hazards Model for the Subdistribution of a Competing Risk,” Journal of the American Statistical Association, 94, 496–509.

There are SAS macros for Gray’s method. Recently, Gray’s method and Fine and Gray methods are built in SAS PHREG and SAS PHREG can be handily used for performing the competing risk model. Here are some SAS papers regarding competing risk model analysis.

• Competing Risk Survival AnalysisUsing PHREG in SAS 9.4
• Using the PHREG Procedure to Analyze Competing-Risks Data
• Nonparametric Analysis of Competing-Risks Data
• Analyzing Survival Data with Competing Risks Using SAS® Software
• Two Shades of Gray: Implementing Gray’s Test for Equivalence of CIF in SAS 9.4
• Computing the ‘Competing Risks’ Modeling Survival Data with Competing Risk Events using SAS Macros