Statistical Approaches to Composite Endpoints∗

JACC: CARDIOVASCULAR INTERVENTIONS

VOL. 9, NO. 22, 2016

ª 2016 BY THE AMERICAN COLLEGE OF CARDIOLOGY FOUNDATION

ISSN 1936-8798/$36.00

PUBLISHED BY ELSEVIER

http://dx.doi.org/10.1016/j.jcin.2016.08.047

EDITORIAL COMMENT

Statistical Approaches to Composite Endpoints* William S. Weintraub, MD

C

omposite endpoints have become common

endpoints can have different clinical relevance.

in clinical trials and observational studies

This is particularly relevant if 1 endpoint, in partic-

(1,2). The primary reason for this is to

ular mortality, is more common in 1 arm, but

increase statistical power and thereby avoid a type

another endpoint, such as repeat revascularization,

II error, in which a difference between treatment

is more common in the other arm, which can lead

arms is missed because of an insufficient number

to a distorted analysis that does not reflect clinical

of events (3). This has become increasingly popular

relevance. Finally, there can be competing risk,

as

has

such that mortality removes the potential for obser-

declined, making it difficult to use mortality alone

mortality

vation of additional nonfatal events (7). Competing

as an endpoint (4). Another reason to use composite

risk in particular can distort results when there is

endpoints is to account for the various effects of

increased mortality in 1 arm of a trial. This allows

a therapy on a series of components within 1

more time for nonfatal events in the arm with lower

primary endpoint. Although seemingly attractive,

mortality. The problems of composite endpoints

composite endpoints create a series of problems

have led to a series of statistical approaches, each

(2). Although not unique to composite endpoints,

with limitations.

using

from

traditional

cardiovascular

time-to-event

disease

statistics,

only

SEE PAGE 2280

the first endpoint is considered. Thus, the time to the first event may be similar in the 2 arms of a

Four different approaches to composite end-

trial, but there may be more events in 1 arm if all

points were evaluated by Capodanno et al. (8) in the

events are counted. Thus, inclusion of all events

study reported in this issue of JACC: Cardiovascular

can add to statistical power. An example of this

Interventions,

was seen in IMPROVE-IT (Improved Reduction of

(Drug

Eluting

using Stent

data for

from Left

the

Main

DELTA Coronary

Outcomes: Vytorin Efficacy International Trial), in

Artery Disease) registry, which compared coronary

which the inclusion of all endpoints, not just the

artery bypass grafting (CABG) and percutaneous

first to occur, more than doubled the number of

coronary

events available and added to statistical power (5).

nal, nonrandomized data. Two composite endpo-

Using traditional time-to-event statistics, all events

ints

are equally weighted (6). However, the various

infarction, or stroke; and 2) the first endpoint

were

intervention considered:

(PCI) 1)

using

mortality,

observatiomyocardial

plus target vessel revascularization (TVR). This study was from a registry, and thus a propensity score was used to reduce treatment selection bias and match *Editorials published in JACC: Cardiovascular Interventions reflect the

patients for subsequent analysis (9). Note that sta-

views of the authors and do not necessarily represent the views of JACC:

tistical approaches to assess composite endpoints do

Cardiovascular Interventions or the American College of Cardiology.

not address the issue of treatment selection bias in

From the Christiana Care Health System, Newark, Delaware. This work

observational studies (10). The differences between

was funded in part by an Institutional Development Award from the

the arms were evaluated using traditional time-to-

National Institute of General Medical Sciences of the National Institutes

event statistics and 4 methods to deal with the

of Health under grant number U54-GM104941. Dr. Weintraub reported that they have no relationships relevant to the contents of this paper to disclose.

problems of composite endpoints: the Anderson-Gill method (11), the win ratio (12), competing risk (7),

2290

Weintraub

JACC: CARDIOVASCULAR INTERVENTIONS VOL. 9, NO. 22, 2016 NOVEMBER 28, 2016:2289–91

Composite Endpoint Statistics

and weighted composites (13). The Anderson-Gill

methods. In the overall patient population from

method is an extension of Cox model analysis to ac-

which the matched pair was drawn, the point esti-

count for additional endpoints. It assumes that the

mate for mortality favored efficacy of CABG, whereas

risk for a subsequent endpoint is not influenced by

the point estimates of myocardial infarction or

the first endpoint. The win ratio orders the endpoints

stroke favored PCI. In this setting, competing risk

as to the most relevant and then counts each

analysis would reduce the apparent advantage in

endpoint for patients in both groups ordered as pairs.

nonfatal events for PCI. However, there was too lit-

However, each pair is counted only once when an

tle difference in mortality between the treatment

endpoint occurs. The ratio is the number of events in

arms for this to become readily apparent. The

1 arm divided by the number of events in the other

method with the greatest effect on outcome was the

arm. The win ratio approach orders events, allowing a

weighted analysis, which attenuated the advantage

subjective assessment of which events are most

of CABG for the quadruple endpoint. This seems

important, but it does not consider time to event or

quite reasonable, but as noted, the weighting is

whether there are multiple events. Competing risk

inherently somewhat arbitrary. This paper is also

assessment can account for the lost time for potential

from a registry, and thus even with propensity

events in 1 group after death, with assumptions about

analysis and the various methods to consider com-

what the hazard for such events might be. Competing

posite endpoints, the comparison between CABG and

risk assessment does not account for multiple events.

PCI will remain limited by residual treatment selec-

Weighted composites can count all events, but the

tion bias (10).

weights are somewhat arbitrary. This approach does

Composite endpoints in trials and registries are common, and we should expect to see them as a part

not account for competing risk. The attributes of each type of analysis are clearly

of many if not most endpoint trials in cardiovascular

and succinctly shown in Table 1 of the paper by

medicine. We should also expect to see the appli-

Capodanno et al. (8). In this study of 602 matched

cation of these and other methods to deal with

pairs at a median of 1,295 days of follow-up, there

multiple endpoints of varying clinical relevance. The

was no difference between arms in the composite of

methods will also be modified and improved. For

death,

using

instance, if in the Anderson-Gill approach, the

traditional time-to-event statistics or any of the ap-

assumption of independence of subsequent event

myocardial

infarction,

and

stroke

proaches to considering composites and multiple

occurrence from the first event does not hold,

events. With the inclusion of TVR in the composite,

statistical approaches can be used to correct for this

the traditional time-to-event, Andersen-Gill, win

(14). Yet more complicated approaches have also

ratio, and competing risk methods all showed CABG

been developed (14). All of these approaches to

to be statistically superior to PCI. Incorporating the

assessing composite endpoints have limitations,

clinical relevance of the component endpoints,

and at least at present these methods seem unlikely

in

to entirely replace traditional time-to-event statis-

marked attenuation of the benefit of CABG with

tics. However, at least as secondary approaches,

TVR in the composite. Although this seems entir-

these methods are likely to become more common.

ely

attenuation

Problems in interpretation may ensue when the

depends on the weighing of TVR as an endpoint,

results are inconsistent with time-to-event statistics.

which is subjective.

The presence of multiple statistical approaches

the

weighted

reasonable,

composite

the

approach

extent

of

this

resulted

The investigators are to be congratulated for this

requires

added

care

in

design.

When

these

analysis and for bringing these methods to the

approaches are used, it will be most appropriate to

clinical

easily

specify the methods in advance. Capodanno et al. (8)

understandable presentation. Although it is inter-

have done a service to the cardiovascular commu-

esting to explore the issue, the dataset is not ideal

nity by helping make these advanced statistical

for considering the problems. First of all, there were

approaches accessible to the community of clinical

few patients with multiple outcomes. This limits the

investigators.

readership

in

a

compelling

and

value of an approach such as Anderson-Gill in not

REPRINT REQUESTS AND CORRESPONDENCE: Dr.

apparent with traditional time-to-event methods.

William S. Weintraub, Christiana Care Health System,

There was also little or no difference in mortality,

4755 Ogletown-Stanton Road, Newark, Delaware 19718.

somewhat limiting the value of competing risk

E-mail: [email protected].

revealing

treatment

differences

that

were

Weintraub

JACC: CARDIOVASCULAR INTERVENTIONS VOL. 9, NO. 22, 2016 NOVEMBER 28, 2016:2289–91

Composite Endpoint Statistics

REFERENCES 1. Cordoba G, Schwartz L, Woloshin S, Bae H, Gotzsche PC. Definition, reporting, and interpretation of composite outcomes in clinical trials: systematic review. BMJ 2010;341:c3920.

6. Tong BC, Huber JC, Ascheim DD, et al. Weighting composite endpoints in clinical trials: essential evidence for the heart team. Ann Thorac Surg 2012;94:1908–13.

2. Tomlinson G, Detsky AS. Composite end points in randomized trials: there is no free lunch. JAMA 2010;303:267–8.

7. Wolbers M, Koller MT, Stel VS, et al. Competing risks analyses: objectives and approaches. Eur Heart J 2014;35:2936–41.

3. Freiman JA, Chalmers TC, Smith H Jr., Kuebler RR. The importance of beta, the type II error and sample size in the design and interpre-

8. Capodanno D, Gargiulo G, Buccheri S, et al. Computing methods for composite clinical endpoints in unprotected left main coronary artery

tation of the randomized control trial. Survey of 71 “negative” trials. N Engl J Med 1978;299:690–4.

revascularization: a post hoc analysis of the DELTA registry. J Am Coll Cardiol Intv 2016;9: 2280–8.

Evaluation of early percutaneous coronary intervention vs. standard therapy after fibrinolysis for ST-segment elevation myocardial infarction: contribution of weighting the composite endpoint. Eur Heart J 2013;34:903–8.

9. Rubin DB. Estimating causal effects from large data sets using propensity scores. Ann Intern Med 1997;127:757–63.

14. Amorim LD, Cai J. Modelling recurrent events: a tutorial for analysis in epidemiology. Int J Epidemiol 2015;44:324–33.

10. Weintraub WS, Grau-Sepulveda MV, Weiss JM, et al. Comparative effectiveness of revascularization strategies. N Engl J Med 2012;366: 1467–76.

KEY WORDS composite endpoints, outcomes, statistics

4. Nabel EG, Braunwald E. A tale of coronary artery disease and myocardial infarction. N Engl J Med 2012;366:54–63. 5. Murphy SA, Cannon CP, Blazing MA, et al. Reduction in total cardiovascular events with ezetimibe/simvastatin post-acute coronary syndrome: the IMPROVE-IT trial. J Am Coll Cardiol 2016;67:353–61.

11. Andersen P, Gill R. Cox’s regression model for counting processes: a large sample study. Ann Stat 1982;10:1100–20. 12. Pocock SJ, Ariti CA, Collier TJ, Wang D. The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities. Eur Heart J 2012;33:176–82. 13. Bakal JA, Westerhout CM, Cantor WJ, et al.

2291