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Assessing immune responses to study vaccines as surrogates of protection plays a central role in vaccine clinical trials. Motivated by three ongoing or pending HIV vaccine efficacy trials, we consider such surrogate endpoint assessment in a randomized placebo-controlled trial with case-cohort sampling of immune responses and a time to event endpoint. Based on the principal surrogate definition under the principal stratification framework proposed by Frangakis and Rubin [Biometrics 58 (2002) 21–29] and adapted by Gilbert and Hudgens (2006), we introduce estimands that measure the value of an immune response as a surrogate of protection in the context of the Cox proportional hazards model. The estimands are not identified because the immune response to vaccine is not measured in placebo recipients. We formulate the problem as a Cox model with missing covariates, and employ novel trial designs for predicting the missing immune responses and thereby identifying the estimands. The first design utilizes information from baseline predictors of the immune response, and bridges their relationship in the vaccine recipients to the placebo recipients. The second design provides a validation set for the unmeasured immune responses of uninfected placebo recipients by immunizing them with the study vaccine after trial closeout. A maximum estimated likelihood approach is proposed for estimation of the parameters. Simulated data examples are given to evaluate the proposed designs and study their properties.

Summary

Given a randomized treatment Z, a clinical outcome Y, and a biomarker S measured some fixed time after Z is administered, we may be interested in addressing the surrogate endpoint problem by evaluating whether S can be used to reliably predict the effect of Z on Y. Several recent proposals for the statistical evaluation of surrogate value have been based on the framework of principal stratification. In this paper, we consider two principal stratification estimands: joint risks and marginal risks. Joint risks measure causal associations of treatment effects on S and Y, providing insight into the surrogate value of the biomarker, but are not statistically identifiable from vaccine trial data. While marginal risks do not measure causal associations of treatment effects, they nevertheless provide guidance for future research, and we describe a data collection scheme and assumptions under which the marginal risks are statistically identifiable. We show how different sets of assumptions affect the identifiability of these estimands; in particular, we depart from previous work by considering the consequences of relaxing the assumption of no individual treatment effects on Y before S is measured. Based on algebraic relationships between joint and marginal risks, we propose a sensitivity analysis approach for assessment of surrogate value, and show that in many cases the surrogate value of a biomarker may be hard to establish, even when the sample size is large.

The effects of vaccine on postinfection outcomes, such as disease, death, and secondary transmission to others, are important scientific and public health aspects of prophylactic vaccination. As a result, evaluation of many vaccine effects condition on being infected. Conditioning on an event that occurs posttreatment (in our case, infection subsequent to assignment to vaccine or control) can result in selection bias. Moreover, because the set of individuals who would become infected if vaccinated is likely not identical to the set of those who would become infected if given control, comparisons that condition on infection do not have a causal interpretation. In this article we consider identifiability and estimation of causal vaccine effects on binary postinfection outcomes. Using the principal stratification framework, we define a postinfection causal vaccine efficacy estimand in individuals who would be infected regardless of treatment assignment. The estimand is shown to be not identifiable under the standard assumptions of the stable unit treatment value, monotonicity, and independence of treatment assignment. Thus selection models are proposed that identify the causal estimand. Closed-form maximum likelihood estimators (MLEs) are then derived under these models, including those assuming maximum possible levels of positive and negative selection bias. These results show the relations between the MLE of the causal estimand and two commonly used estimators for vaccine effects on postinfection outcomes. For example, the usual intent-to-treat estimator is shown to be an upper bound on the postinfection causal vaccine effect provided that the magnitude of protection against infection is not too large. The methods are used to evaluate postinfection vaccine effects in a clinical trial of a rotavirus vaccine candidate and in a field study of a pertussis vaccine. Our results show that pertussis vaccination has a significant causal effect in reducing disease severity.

Summary

Frangakis and Rubin (2002, Biometrics 58, 21–29) proposed a new definition of a surrogate endpoint (a “principal” surrogate) based on causal effects. We introduce an estimand for evaluating a principal surrogate, the causal effect predictiveness (CEP) surface, which quantifies how well causal treatment effects on the biomarker predict causal treatment effects on the clinical endpoint. Although the CEP surface is not identifiable due to missing potential outcomes, it can be identified by incorporating a baseline covariate(s) that predicts the biomarker. Given case–cohort sampling of such a baseline predictor and the biomarker in a large blinded randomized clinical trial, we develop an estimated likelihood method for estimating the CEP surface. This estimation assesses the “surrogate value” of the biomarker for reliably predicting clinical treatment effects for the same or similar setting as the trial. A CEP surface plot provides a way to compare the surrogate value of multiple biomarkers. The approach is illustrated by the problem of assessing an immune response to a vaccine as a surrogate endpoint for infection.

If a vaccine does not protect individuals completely against infection, it could still reduce infectiousness of infected vaccinated individuals to others. Typically, vaccine efficacy for infectiousness is estimated based on contrasts between the transmission risk to susceptible individuals from infected vaccinated individuals compared with that from infected unvaccinated individuals. Such estimates are problematic, however, because they are subject to selection bias and do not have a causal interpretation. Here, we develop causal estimands for vaccine efficacy for infectiousness for four different scenarios of populations of transmission units of size two. These causal estimands incorporate both principal stratification, based on the joint potential infection outcomes under vaccine and control, and interference between individuals within transmission units. In the most general scenario, both individuals can be exposed to infection outside the transmission unit and both can be assigned either vaccine or control. The three other scenarios are special cases of the general scenario where only one individual is exposed outside the transmission unit or can be assigned vaccine. The causal estimands for vaccine efficacy for infectiousness are well defined only within certain principal strata and, in general, are identifiable only with strong unverifiable assumptions. Nonetheless, the observed data do provide some information, and we derive large sample bounds on the causal vaccine efficacy for infectiousness estimands. An example of the type of data observed in a study to estimate vaccine efficacy for infectiousness is analyzed in the causal inference framework we developed.

Pearl’s article provides a useful springboard for discussing further the benefits and drawbacks of principal stratification and the associated discomfort with attributing effects to post-treatment variables. The basic insights of the approach are important: pay close attention to modification of treatment effects by variables not observable before treatment decisions are made, and be careful in attributing effects to variables when counterfactuals are ill-defined. These insights have often been taken too far in many areas of application of the approach, including instrumental variables, censoring by death, and surrogate outcomes. A novel finding is that the usual principal stratification estimand in the setting of censoring by death is by itself of little practical value in estimating intervention effects.

Summary

Data analysis for randomized trials including multi-treatment arms is often complicated by subjects who do not comply with their treatment assignment. We discuss here methods of estimating treatment efficacy for randomized trials involving multi-treatment arms subject to non-compliance. One treatment effect of interest in the presence of non-compliance is the complier average causal effect (CACE) (Angrist et al. 1996), which is defined as the treatment effect for subjects who would comply regardless of the assigned treatment. Following the idea of principal stratification (Frangakis & Rubin 2002), we define principal compliance (Little et al. 2009) in trials with three treatment arms, extend CACE and define causal estimands of interest in this setting. In addition, we discuss structural assumptions needed for estimation of causal effects and the identifiability problem inherent in this setting from both a Bayesian and a classical statistical perspective. We propose a likelihood-based framework that models potential outcomes in this setting and a Bayes procedure for statistical inference. We compare our method with a method of moments approach proposed by Cheng & Small (2006) using a hypothetical data set, and further illustrate our approach with an application to a behavioral intervention study (Janevic et al. 2003).

Pearl (2011) asked for the causal inference community to clarify the role of the principal stratification framework in the analysis of causal effects. Here, I argue that the notion of principal stratification has shed light on problems of non-compliance, censoring-by-death, and the analysis of post-infection outcomes; that it may be of use in considering problems of surrogacy but further development is needed; that it is of some use in assessing “direct effects”; but that it is not the appropriate tool for assessing “mediation.” There is nothing within the principal stratification framework that corresponds to a measure of an “indirect” or “mediated” effect.

When the true end points (T) are difficult or costly to measure, surrogate markers (S) are often collected in clinical trials to help predict the effect of the treatment (Z). There is great interest in understanding the relationship among S, T, and Z. A principal stratification (PS) framework has been proposed by Frangakis and Rubin (2002) to study their causal associations. In this paper, we extend the framework to a multiple trial setting and propose a Bayesian hierarchical PS model to assess surrogacy. We apply the method to data from a large collection of colon cancer trials in which S and T are binary. We obtain the trial-specific causal measures among S, T, and Z, as well as their overall population-level counterparts that are invariant across trials. The method allows for information sharing across trials and reduces the nonidentifiability problem. We examine the frequentist properties of our model estimates and the impact of the monotonicity assumption using simulations. We also illustrate the challenges in evaluating surrogacy in the counterfactual framework that result from nonidentifiability.

SUMMARY

Evaluation of HIV vaccine candidates in non-human primates (NHPs) is a critical step toward developing a successful vaccine to control the HIV pandemic. Historically, HIV vaccine regimens have been tested in NHPs by administering a single high dose of the challenge virus. More recently, evaluation of candidate HIV vaccines has entailed repeated low-dose challenges which more closely mimic typical exposure in natural transmission settings. In this paper, we consider evaluation of the type and magnitude of vaccine efficacy from such experiments. Based on the principal stratification framework, we also address evaluation of potential immunological surrogate endpoints for infection.

Summary

There has been a recent emphasis on the identification of biomarkers and other biologic measures that may be potentially used as surrogate endpoints in clinical trials. We focus on the setting of data from a single clinical trial. In this paper, we consider a framework in which the surrogate must occur before the true endpoint. This suggests viewing the surrogate and true endpoints as semi-competing risks data; this approach is new to the literature on surrogate endpoints and leads to an asymmetrical treatment of the surrogate and true endpoints. However, such a data structure also conceptually complicates many of the previously considered measures of surrogacy in the literature. We propose novel estimation and inferential procedures for the relative effect and adjusted association quantities proposed by Buyse and Molenberghs (1998, Biometrics, 1014 – 1029). The proposed methodology is illustrated with application to simulated data, as well as to data from a leukemia study.

The federal and provincial governments have undertaken a universal immunization program to protect school-aged girls against cervical cancer using the new human papillomavirus vaccine Gardasil®. While the vaccine appears to be effective and safe, there are a number of important unanswered questions regarding it and the effects of the immunization program. Here we briefly review key literature about the vaccine and then use the Erickson criteria, which offer an evidence basis for decision-making regarding national immunization strategies, to evaluate whether the program is congruent with sound public health policy. Our analysis of the national decision to recommend and fund a vaccination program using Gardasil® raises significant questions about the basis for this program.

SUMMARY

Identification of an immune response to vaccination that reliably predicts protection from clinically significant infection, i.e. an immunological surrogate endpoint, is a primary goal of vaccine research. Using this problem of evaluating an immunological surrogate as an illustration, we describe a hierarchy of three criteria for a valid surrogate endpoint and statistical analysis frameworks for evaluating them. Based on a placebo-controlled vaccine efficacy trial, the first level entails assessing the correlation of an immune response with a study endpoint in the study groups, and the second level entails evaluating an immune response as a surrogate for the study endpoint that can be used for predicting vaccine efficacy for a setting similar to that of the vaccine trial. We show that baseline covariates, innovative study design, and a potential outcomes formulation can be helpful for this assessment. The third level entails validation of a surrogate endpoint via meta-analysis, where the goal is to evaluate how well the immune response can be used to predict vaccine efficacy for new settings (building bridges). A simulated vaccine trial and two example vaccine trials are presented, one supporting that certain anti-influenza antibody levels are an excellent surrogate for influenza illness and another supporting that certain anti-HIV antibody levels are not useful as a surrogate for HIV infection.

The RENAAL (Reduction of Endpoints in NIDDM with the Angiotensin II Antagonist Losartan) study is a multinational, double-blind, randomized, placebo controlled trial which was recently published. It was aimed to evaluate the effect of the angiotensin receptor blocker losartan in patients with diabetic nephropathy. The primary efficacy measure was the time to the first event of the composite end point of a doubling of serum creatinine, end-stage renal disease, or death. The conclusion was that losartan led to significant improvement in renal outcomes, that was beyond that attributable to blood pressure control in patients with type 2 diabetes and nephropathy.

The perusal of the report raises concern, regarding to both the patient population as well as the outcome measures. At randomization, the placebo group included more patients with angina, myocardial infarction and lipid disorders than the losartan group. Information on glucose metabolism was disregarded, and data on antihyperglycemic therapy – which may have undesirable influences on cardiac performance – were not included in a multivariate analysis. In addition, only data on first hospitalization were reported, whilst information on total specific-cause hospitalizations was disregarded, thus potentially masking further unfavorable events. Furthermore, creatinine seems not to be a reliable surrogate end point. Based on its mechanism of action, losartan may possess favorable renoprotective properties. However, due to the methodological flaws and the incomplete data in the RENAAL study, the question of the effectiveness and safety of this drug in diabetic nephropathy remains yet unanswered.

Summary

There has been substantive interest in the assessment of surrogate endpoints in medical research. These are measures which could potentially replace “true” endpoints in clinical trials and lead to studies that require less follow-up. Recent research in the area has focused on assessments using causal inference frameworks. Beginning with a simple model for associating the surrogate and true endpoints in the population, we approach the problem as one of endogenous covariates. An instrumental variables estimator and general two-stage algorithm is proposed. Existing surrogacy frameworks are then evaluated in the context of the model. In addition, we define an extended relative effect estimator as well as a sensitivity analysis for assessing what we term the treatment instrumentality assumption. A numerical example is used to illustrate the methodology.

SUMMARY

Using multiple historical trials with surrogate and true endpoints, we consider various models to predict the effect of treatment on a true endpoint in a target trial in which only a surrogate endpoint is observed. This predicted result is computed using (1) a prediction model (mixture, linear, or principal stratification) estimated from historical trials and the surrogate endpoint of the target trial and (2) a random extrapolation error estimated from successively leaving out each trial among the historical trials. The method applies to either binary outcomes or survival to a particular time that is computed from censored survival data. We compute a 95% confidence interval for the predicted result and validate its coverage using simulation. To summarize the additional uncertainty from using a predicted instead of true result for the estimated treatment effect, we compute its multiplier of standard error. Software is available for download.

Purpose

We investigated the putative surrogate endpoints (PSEs) of best response (BR), complete response (CR), confirmed response (CoR), and progression-free survival (PFS) for associations with Overall Survival (OS), and as possible surrogate endpoints for OS.

Methods

Individual patient (pt) data from 870 untreated ES-SCLC pts participating in 6 single-arm (274 pts) and 3 randomized trials (596 pts) were pooled. Patient-level associations between PSEs and OS were assessed by Cox models using landmark analyses. Trial-level surrogacy of PSEs assessed by the association of treatment effects on OS and individual PSEs. Trial-level surrogacy measures included: R2 from weighted least squares regression model (WLS R2), Spearman's correlation coefficient, and R2 from bivariate survival model (Copula R2).

Results

Median OS and PFS were 9.6 (95% CI: 9.1-10.0) and 5.5 (95% CI: 5.2-5.9) months, respectively; BR, CR, and CoR rates were 44%, 22%, and 34%, respectively. Patient-level associations showed that PFS status at 4 months was a strong predictor of subsequent survival (HR=0.42 (95% CI: 0.35-0.51); concordance index=0.63; p<0.01), with 6-month PFS being the strongest (HR=0.41 (95% CI: 0.35-0.49); concordance index=0.66; p<0.01). At the trial-level, PFS showed the highest level of surrogacy for OS (WLS R2=0.79; Copula R2=0.80), explaining 79% of the variance in OS. Tumor response endpoints showed lower surrogacy levels (WLS R2≤0.48).

Conclusion

PFS was strongly associated with OS at both the patient and trial-level. PFS also shows promise as a potential surrogate for OS, but further validation is needed using data from a larger number of randomized phase III trials.

Summary

In clinical trials, a biomarker (S) that is measured after randomization and is strongly associated with the true endpoint (T) can often provide information about T and hence the effect of a treatment (Z) on T. A useful biomarker can be measured earlier than T and cost less than T. In this paper we consider the use of S as an auxiliary variable and examine the information recovery from using S for estimating the treatment effect on T, when S is completely observed and T is partially observed. In an ideal but often unrealistic setting, when S satisfies Prentice’s definition for perfect surrogacy, there is the potential for substantial gain in precision by using data from S to estimate the treatment effect on T. When S is not close to a perfect surrogate, it can provide substantial information only under particular circumstances. We propose to use a targeted shrinkage regression approach that data-adaptively takes advantage of the potential efficiency gain yet avoids the need to make a strong surrogacy assumption. Simulations show that this approach strikes a balance between bias and efficiency gain. Compared with competing methods, it has better mean squared error properties and can achieve substantial efficiency gain, particularly in a common practical setting when S captures much but not all of the treatment effect and the sample size is relatively small. We apply the proposed method to a glaucoma data example.

Seven randomized trials published in the last six years have shown that warfarin reduces the risk of ischaemic strokes and death in patients with atrial fibrillation. The annual rates of major bleeding episodes in all these trials were low and, as a result, doctors in primary and secondary care are being encouraged to consider using warfarin for patients with atrial fibrillation unless there are obvious contraindications. However, the populations used in these studies were highly selected and rigorously monitored throughout the trial period to minimize the risk of bleeding in a way which probably could not be expected in routine primary care. Although the rates of major bleeding episodes were uniformly low, the rates of minor bleeding episodes were much higher and these could impact substantially on patients' views of the treatment and on the workload of the primary care team. Evidence is now at hand which allows the stratification of risk in patients with atrial fibrillation which should enable those who are at greatest risk to be considered for this form of treatment. Patients may develop risk factors over time which could render them unsuitable for continuation of warfarin therapy. The general practitioner is centrally placed to make the decision about initiating or continuing treatment or indeed stopping it. Several models for decision making in warfarin treatment from primary and secondary care are proposed.

The paired availability design for historical controls postulated four classes corresponding to the treatment (old or new) a participant would receive if arrival occurred during either of two time periods associated with different availabilities of treatment. These classes were later extended to other settings and called principal strata. Judea Pearl asks if principal stratification is a goal or a tool and lists four interpretations of principal stratification. In the case of the paired availability design, principal stratification is a tool that falls squarely into Pearl's interpretation of principal stratification as “an approximation to research questions concerning population averages.” We describe the paired availability design and the important role played by principal stratification in estimating the effect of receipt of treatment in a population using data on changes in availability of treatment. We discuss the assumptions and their plausibility. We also introduce the extrapolated estimate to make the generalizability assumption more plausible. By showing why the assumptions are plausible we show why the paired availability design, which includes principal stratification as a key component, is useful for estimating the effect of receipt of treatment in a population. Thus, for our application, we answer Pearl's challenge to clearly demonstrate the value of principal stratification.

Background

Overall survival (OS) is the gold standard for the demonstration of a clinical benefit in cancer trials. Replacement of OS by a surrogate endpoint allows to reduce trial duration. To date, few surrogate endpoints have been validated in digestive oncology. The aim of this study was to draw up an ordered list of potential surrogate endpoints for OS in digestive cancer trials, by way of a survey among clinicians and methodologists. Secondary objective was to obtain their opinion on surrogacy and quality of life (QoL).

Methods

In 2007 and 2008, self administered sequential questionnaires were sent to a panel of French clinicians and methodologists involved in the conduct of cancer clinical trials. In the first questionnaire, panellists were asked to choose the most important characteristics defining a surrogate among six proposals, to give advantages and drawbacks of the surrogates, and to answer questions about their validation and use. Then they had to suggest potential surrogate endpoints for OS in each of the following tumour sites: oesophagus, stomach, liver, pancreas, biliary tract, lymphoma, colon, rectum, and anus. They finally gave their opinion on QoL as surrogate endpoint. In the second questionnaire, they had to classify the previously proposed candidate surrogates from the most (position #1) to the least relevant in their opinion.

Frequency at which the endpoints were chosen as first, second or third most relevant surrogates was calculated and served as final ranking.

Results

Response rate was 30% (24/80) in the first round and 20% (16/80) in the second one. Participants highlighted key points concerning surrogacy. In particular, they reminded that a surrogate endpoint is expected to predict clinical benefit in a well-defined therapeutic situation. Half of them thought it was not relevant to study QoL as surrogate for OS.

DFS, in the neoadjuvant settings or early stages, and PFS, in the non operable or metastatic settings, were ranked first, with a frequency of more than 69% in 20 out of 22 settings. PFS was proposed in association with QoL in metastatic primary liver and stomach cancers (both 81%). This composite endpoint was ranked second in metastatic oesophageal (69%), colorectal (56%) and anal (56%) cancers, whereas QoL alone was also suggested in most metastatic situations.

Other endpoints frequently suggested were R0 resection in the neoadjuvant settings (oesophagus (69%), stomach (56%), pancreas (75%) and biliary tract (63%)) and response. An unexpected endpoint was metastatic PFS in non operable oesophageal (31%) and pancreatic (44%) cancers. Quality and results of surgical procedures like sphincter preservation were also cited as eligible surrogate endpoints in rectal (19%) and anal (50% in case of localized disease) cancers. Except for alpha-FP kinetic in hepatocellular carcinoma (13%) and CA19-9 decline (6%) in pancreas, few endpoints based on biological or tumour markers were proposed.

Conclusion

The overall results should help prioritise the endpoints to be statistically evaluated as surrogate for OS, so that trialists and clinicians can rely on endpoints that ensure relevant clinical benefit to the patient.

This report summarizes the key findings of the Third Tegernsee Conference on Immunotherapy of Cancer held recently in Feldafing, Germany.

Cancer immunotherapy broadly includes active immunization, as in the use of cancer vaccines, passive immunization, such as the use of adoptive cell therapy and antibodies that modulate tumor function, and immunostimulation, using antibodies and small molecules to treat malignancy by activating or unleashing an endogenous immune response against tumor cells. Currently, >100 different monoclonal antibodies are in use or under evaluation for use as therapeutic agents in various malignancies. Active stimulation of the host's immune system holds promise for achieving durable remission of malignant disease and represents a nontoxic method of therapy if tumor-specific effector cells can be selectively targeted. However, no active-specific treatment strategy (i.e., a therapeutic cancer vaccine) has yet found its way into the clinical armamentarium, although several promising recent reports suggest that, for follicular lymphoma, prostate cancer, and melanoma, clinical benefit was shown for the first time in randomized trials with a vaccine approach. Here, we report on the key findings of the Third Tegernsee Conference on Immunotherapy of Cancer (Feldafing, Germany, July 2–4, 2009) and provide short commentaries on data presented at this meeting regarding the future role of cancer vaccines, recent developments in adoptive cellular therapy, ways to improve immunotherapeutic treatment modalities (e.g., by manipulating the tumor microenvironment), and some novel targeted therapies that are well advanced in clinical testing, all of which have implications for future oncology practice.

Two randomized clinical trials, conducted independently, have reported results of neonates transfused with red blood cells (RBCs) given per either liberal (relatively high pretransfusion blood hematocrit levels) or restrictive (relatively low pretransfusion blood hematocrit levels) transfusion programs. Both found fewer RBC transfusions given per restrictive programs and comparable outcomes for several clinical endpoints. However, the Iowa trial found significantly more problems with apnea, intraparenchymal brain hemorrhage and periventricular leukomalacia in infants transfused per the restrictive program – findings not found by the Canadian trial. A critical analysis of both trials and possible reasons for the discrepant findings are discussed. Until definitive data are reported by additional studies, it seems prudent not to severely restrict/limit allogeneic RBC transfusions to neonates – except in approved investigational settings.

Since the beginning of the human genome project there has been considerable speculation about how this resource and the knowledge creation it enabled would change therapeutic discovery, development, and delivery. As the project neared completion, considerable claims and predictions were made about the changes that soon would be forthcoming. Many of these early predictions failed to materialize, however, leading to further speculation about the reasons, including the role of the pharmaceutical industry in realizing the promise of “genomic medicine”. During this same period, considerable strides were made in other areas of molecular biology and medicine, and in response scientific thinking naturally evolved. Researchers and regulators moved from a genotype-centric view to a view that all biomarkers are potential tools to improve drug development and therapeutic decision making. Molecular biology is now seen as encouraging more “personalized medicine”—the closer alignment of biological information (derived from molecular diagnostics) and therapy selection. Meanwhile, there are growing concerns that increasing expenditures in pharmaceutical research and development are not sustainable and not reaping sufficient gains for shareholders or society at large. Thus, there is new speculation about how biomarkers, personalized medicine, and the industry will interact and create value for patients. This overview seeks to explore the issues driving pharmaceutical productivity and the likely contribution of biomarkers in the future.

In this issue of Academic Medicine, Kitterman and colleagues report the results of an evaluation of the prevalence and cost of low-enrolling studies (zero or one participant enrolled) conducted at Oregon Health & Science University (OHSU). They found that one third of all studies terminated between 2005 and 2009 at OHSU had low enrollment and that these low-enrolling studies cost the institution almost $1 million annually. The recruitment of research participants is critical to conducting clinical and translational research. Failure to recruit research participants has a negative financial impact, but, more importantly, under-enrolled studies do not contribute to scientific or clinical knowledge. In this commentary, the authors describe four areas in which academic medical centers (AMCs) could invest more effort and resources to improve the recruitment of research participants. First, more planning and resources should be put into determining the feasibility of participant recruitment. Second, studies that are under-enrolling should be terminated early to prevent unethical research, to save financial and other resources, and to allow these resources to be applied to successful research. Third, AMCs should professionalize, centralize, and automate participant recruitment. Fourth, AMCs should take a leadership role in partnering with the public to improve participation in clinical research. Participant recruitment must be improved if clinical and translational research is to meet its promise of improving health.