Assessing correlates of protection in vaccine trials.

Readers interested in knowing how well fold rise in antibody titers serves as a correlate of protection (CoP) for the herpes zoster (HZ) vaccine Zostavax will find a clear answer in the article by Gilbert et al [1]: fold rise is an excellent CoP. The real strength of the article, however, is its answer to a more general question: how can one identify when a biomarker is a useful CoP? Embedded sotto voce in that question is another, not-yet-addressed important practical problem that faces vaccine development: is there a way to learn enough about the likelihood that a vaccine will be effective to obviate the need for certain field trials? In particular, can one exploit knowledge gained through the use of 1 or more CoPs to infer vaccine efficacy? By extension, the problem is relevant to surrogate outcomes outside of vaccine development, for a CoP is just a surrogate in the vaccine world. Gilbert et al use “CoP” to refer to any variable that is “reliably predictive of outcome,” whether or not it serves as a mechanism of protection That nomenclature, if used more generally, will clarify the literature. They advocate, when useful, denoting a mechanistic and a nonmechanistic CoP as “mCoP” and “nCoP,” respectively. The suggestion that a surrogate can possibly stand in for a clinical outcome may send a chill down the backs of clinical trialists. I can hear the reader exclaim, “Have we not learned enough from trials involving hormone replacement in women, high-density lipoprotein levels in men and women, and antioxidants in smokers to beware of the surrogate route? ” But Gilbert et al are leading us on a way different from the usual surrogate path—they are walking us gingerly through the principal stratification method of causal inference [2]. This approach differs so much from the typical analysis used in clinical trials that it is worth pausing to describe its basic philosophy. Conventional strata in clinical trials are those that are defined at the time of randomization, such as age category, race, prior exposure to an organism of interest, or baseline severity. Restricting attention to baseline variables allows unbiased assessment of the effect of an intervention by strata. For example, one can ask whether Zostavax is effective in males or whether the effect varies by sex. Other subgroups, called “improper” by Yusuf [3], are defined after randomization, such as people who adhere to the protocol regimen and those who do not. Because such postrandomization subgroups are inextricably tied to outcome, they cannot form the basis of unbiased inference concerning the effect of treatment. Gilbert et al discuss neither of these types of strata. They address, instead, principal stratification of posttreatment variables, a method that cross-classifies participants in trials by the potential values of those variables under each of the treatments being studied. Here the variables are titer or fold rise and the treatments are Zostavax and placebo. Because treatment assignment does not affect these variables, they can be used like any baseline variable. Principal strata address conditions contrary to fact: they posit what participants in a trial would have experienced had they been assigned the study group to which they had not been randomized. One can view populations in clinical trials comparing a treatment to control as comprising 4 types of participants: those who would be likely to fail, regardless of whether they received the experimental or control treatment; those who would be likely to succeed, regardless of whether they received the experimental or control treatment; those who would be more likely to succeed if they received the experimental treatment; and those who would be more likely to succeed if they received the control treatment. Depending on the study, “failure” might be a clinical outcome (eg, death from a gram-negative bacterial infection in an antibiotic trial or having a stroke in a trial of an antithrombotic drug) or it might be, as in the Gilbert et al analysis, achieving a specific value on a surrogate outcome or on a marker. Received 5 May 2014; accepted 7 May 2014; electronically published 13 May 2014. Correspondence: Janet Wittes, PhD, Statistics Collaborative, 1625 Massachusetts Ave NW, Ste 600, Washington, DC 20036 (janet@statcollab.com). The Journal of Infectious Diseases 2014;210:1523–5 © The Author 2014. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals. permissions@oup.com. DOI: 10.1093/infdis/jiu283