Surrogate endpoint analysis: an exercise in extrapolation

Intermediate endpoints as surrogates for outcomes in cancer immunotherapy: a systematic review and meta-analysis of phase 3 trials

Background

Cancer immunotherapy shows unique efficacy kinetics that differs from conventional treatment. These characteristics may lead to the prolongation of trial duration, hence reliable surrogate endpoints are urgently needed. We aimed to systematically evaluate the study-level performance of commonly reported intermediate clinical endpoints for surrogacy in cancer immunotherapy.

Methods

We searched the Embase, PubMed, and Cochrane databases, between database inception and October 18, 2022, for phase 3 randomised trials investigating the efficacy of immunotherapy in patients with advanced solid tumours. An updated search was done on July, 15, 2023. No language restrictions were used. Eligible trials had to set overall survival (OS) as the primary or co-primary endpoint and report at least one intermediate clinical endpoint including objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and 1-year overall survival. Other key inclusion and exclusion criteria included: (1) adult patients (>18 years old) with advanced solid tumour; (2) no immunotherapy conducted in the control arms; (3) follow-up is long enough to achieve OS; (4) data should be public available. A two-stage meta-analytic approach was conducted to evaluate the magnitude of the association between these intermediate endpoints and OS. A surrogate was identified if the coefficient of determination (R2) was 0.7 or greater. Leave-one-out cross-validation and pre-defined subgroup analysis were conducted to examine the heterogeneity. Potential publication bias was evaluated using the Egger's and Begg's tests. This trial was registered with PROSPERO, number CRD42022381648.

Findings

52,342 patients with 15 types of tumours from 77 phase 3 studies were included. ORR (R2 = 0.11; 95% CI, 0.00-0.24), DCR (R2 = 0.01; 95% CI, 0.00-0.01), and PFS (R2 = 0.40; 95% CI, 0.23-0.56) showed weak associations with OS. However, a strong correlation was observed between 1-year survival and clinical outcome (R2 = 0.74; 95% CI, 0.64-0.83). These associations remained relatively consistent across pre-defined subgroups stratified based on tumour types, masking methods, line of treatments, drug targets, treatment strategies, and follow-up durations. No significant heterogeneities or publication bias were identified.

Interpretation

1-year milestone survival was the only identified surrogacy endpoint for outcomes in cancer immunotherapy. Ongoing investigations and development of new endpoints and incorporation of biomarkers are needed to identify potential surrogate markers that can be more robust than 1-year survival. This work may provide important references in assisting the design and interpretation of future clinical trials, and constitute complementary information in drafting clinical practice guidelines.

Funding

None.

Comprehensive evaluation of surrogate endpoints to predict overall survival in trials with PD1/PD-L1 immune checkpoint inhibitors plus chemotherapy

BACKGROUND

PD1/PD-L1 immune checkpoint inhibitors (ICI) have revolutionized cancer treatment. Although there is controversy about the accuracy of surrogate endpoints in the ICI setting to predict overall survival (OS), these endpoints are commonly used in confirmatory trials. Here we aimed to explore the validity of classical and novel surrogate endpoints in randomised controlled trials (RCT) that combine ICI plus chemotherapy (CT) in the first-line setting.

MATERIAL AND METHODS

A systematic review was conducted to identify RCT investigating anti-PD1/PD-L1 drugs plus CT versus CT alone. We performed (i) arm-level analysis to evaluate predictors of median OS (mOS) and (ii) comparison-level analysis for OS hazard ratio (HR) estimations. Linear regression models weighted by trial size were fitted and adjusted R² values were reported.

RESULTS

Thirty-nine RCTs involving 22,341 patients met the inclusion criteria (17 non-small cell lung, 9 gastroesophageal and 13 in other cancers) with ten different ICI under study. Overall, ICI plus CT improved OS (HR = 0.76; 95%CI: 0.73-0.80). In the arm-level analysis, the best mOS prediction was obtained with a new endpoint that combines median duration of response and ORR (mDoR-ORR) and with median PFS (R² = 0.5 both). In the comparison-level analysis, PFS HR showed a moderate association with OS HR (R² = 0.52). Early OS read-outs were highly associated with final OS outcomes (R² = 0.80).

CONCLUSIONS

The association between surrogate endpoints and OS in first-line RCT combining anti-PD1/PD-L1 and CT is moderate-low. Early OS read-outs showed a good association with final OS HR while the mDOR-ORR endpoint could help to better design confirmatory trials after single-arm phase II trials.

Clinical Benefit Scales and Trial Design: Some Statistical Issues

Recently developed clinical-benefit outcome scales by the European Society for Medical Oncology (ESMO) and the American Society of Clinical Oncology (ASCO) allow standardized objective evaluation of outcomes of randomized clinical trials. However, incorporation of clinical-benefit outcome scales into trial designs highlights a number of statistical issues: the relationship between minimal clinical benefit and the target treatment-effect alternative used in the trial design, designing trials to assess long-term benefit, potential problems with using a trial endpoint that is not overall survival, and how to incorporate subgroup analyses into the trial design. Using the ESMO Magnitude of Clinical Benefit Scale as a basis for discussion, we review what these issues are and how they can guide the choice of trial-design target effects, appropriate endpoints, and pre-specified subgroup analyses to increase the chances that the resulting trial outcomes can be appropriately evaluated for clinical benefit.

Efficacy and Safety of EGFR Inhibitors in the Treatment of EGFRPositive NSCLC Patients: A Meta-Analysis

BACKGROUND

We compared the response rates, survival rates, and safety profile of epidermal growth factor receptor (EGFR) inhibitors with non-targeted chemotherapy and older EGFR inhibitors when used to treat advanced non-small cell lung cancer (NSCLC) patients with activating EGFR mutations.

METHODS

We searched PubMed, Cochrane Central Register of Controlled Trials, and clinicaltrial.- gov for randomized controlled trials published until 11-Feb-2020. Treatment outcomes were compared between EGFR inhibitor and pooled comparator; a subgroup analysis compared outcomes between EGFR inhibitor and non-targeted chemotherapy, and between newer and older EGFR inhibitors.

RESULTS

Twenty-one studies with 4,250 unique patients were included. Significantly higher objective response rate (ORR) (odds ratio (OR) 2.28; 95% CI 2.00-2.61), higher disease control rate (DCR) (OR 2.3; 95% CI 1.88-3.06), and longer progression-free survival (PFS) (Hazard ratio (HR) 0.56; 95% CI 0.52-0.60) were observed in the EGFR inhibitor group compared to the pooled comparator group. Subgroup analysis revealed that the ORR, DCR, and PFS were significantly higher with EGFR inhibitors than non-targeted chemotherapy, and only PFS (and not ORR and DCR) was significantly longer with newer EGFR inhibitors than the older EGFR inhibitors. Overall survival (OS) was not significantly different between EGFR inhibitors and pooled comparator (HR 0.91; 95% CI 0.83-1.00) as well as in either of the subgroup analyses. Adverse events ≥ grade 3 and treatment discontinuation were significantly higher with non-targeted chemotherapy compared to the EGFR inhibitors.

CONCLUSION

The benefits of prolongation of ORR, DCR, and PFS might not imply significantly improved OS after therapy with EGFR inhibitors when compared with non-targeted chemotherapy or older EGFR inhibitors.

Intermediate clinical endpoints for surrogacy in localised prostate cancer: an aggregate meta-analysis

BACKGROUND

The international Intermediate Clinical Endpoints in Cancer of the Prostate working group has established metastasis-free survival as a surrogate for overall survival in localised prostate cancer based on the findings of 19 predominantly radiotherapy-based trials. We sought to comprehensively assess aggregate trial-level performance of commonly reported intermediate clinical endpoints across all randomised trials in localised prostate cancer.

METHODS

For this meta-analysis, we searched PubMed for all trials in localised or biochemically recurrent prostate cancer published between Jan 1, 1970, and Jan 15, 2020. Eligible trials had to be randomised, therapeutic, reporting overall survival and at least one intermediate clinical endpoint, and with a sample size of at least 70 participants. Trials of metastatic disease were excluded. Intermediate clinical endpoints included biochemical failure, local failure, distant metastases, biochemical failure-free survival, progression-free survival, and metastasis-free survival. Candidacy for surrogacy was assessed using the second condition of the meta-analytical approach (ie, correlation of the treatment effect of the intermediate clinical endpoint and overall survival), using R2 weighted by the inverse variance of the log intermediate clinical endpoint hazard ratio. The intermediate clinical endpoint was deemed to be a surrogate for overall survival if R2 was 0·7 or greater.

FINDINGS

75 trials (53 631 patients) were included in our analysis. Median follow-up was 9·1 years (IQR 5·7-10·6). Biochemical failure (R2 0·38 [95% CI 0·11-0·64]), biochemical failure-free survival (R2 0·12 [0·0030-0·33]), biochemical failure and clinical failure (R2 0·28 [0·0045-0·65]), and local failure (R2 0·085 [0·00-0·37]) correlated poorly with overall survival. Progression-free survival (R2 0·46 [95% CI 0·22-0·67]) showed moderate correlation with overall survival, and metastasis-free survival (R2 0·78 [0·59-0·89]) correlated strongly.

INTERPRETATION

Intermediate clinical endpoints based on biochemical and local failure did not meet the second condition of the meta-analytical approach and are not surrogate endpoints for overall survival in localised prostate cancer. Our findings validate metastasis-free survival as the only identified surrogate endpoint for overall survival to date.

FUNDING

Prostate Cancer Foundation and National Institutes of Health.

Simple Methods for Evaluating 4 Types of Biomarkers: Surrogate Endpoint, Prognostic, Predictive, and Cancer Screening

We review simple methods for evaluating 4 types of biomarkers. First, we discuss the evaluation of surrogate endpoint biomarkers (to shorten a randomized trial) using 2 statistical and 3 biological criteria. Second, we discuss the evaluation of prognostic biomarkers (to predict the risk of disease) by comparing data collection costs with the anticipated net benefit of risk prediction. Third, we discuss the evaluation of predictive markers (to search for a promising subgroup in a randomized trial) using a multivariate subpopulation treatment effect pattern plot involving a risk difference or responders-only benefit function. Fourth, we discuss the evaluation of cancer screening biomarkers (to predict cancer in asymptomatic persons) using methodology to substantially reduce the sample size with stored specimens.

Progression-free survival and one-year milestone survival as surrogates for overall survival in previously treated advanced non-small cell lung cancer

The advent of immunotherapy leads to greater availability of effective subsequent treatments and extended survival in previously treated advanced non-small cell lung cancer (NSCLC), complicating the evaluation of overall survival (OS) in second-line NSCLC trials. Here, we aimed to assess the surrogacy of progression-free survival (PFS) and milestone survival for OS in second-line NSCLC trials investigating chemotherapy, targeted therapy and immunotherapy, respectively. We systemically searched for active-controlled, second-line NSCLC trials. The milestone time point was set at one-year based on pre-analysis. A two-stage meta-analytic validation model was adopted to assess associations between surrogate endpoint (SE) and OS and associations between treatment effects on SE and OS. Treatment effects on SE and OS were expressed as PFS hazard ratios (HR_PFS ), 1 yr-milestone ratio (Ratio_1y-SUR ) and HR_OS . Subgroup analyses stratified by treatment types and trial publication years evaluated the surrogacy in different clinical contexts. The study included 50 trials with 22,804 patients. One-year survival strongly correlated with OS (R² [95% confidence interval]: one-year survival -median OS = 0.707 [0.704-0.708]; Ratio_1y-SUR -HR_OS = 0.829 [0.828-0.831]). No correlation was established between PFS and OS (median PFS-median OS = 0.100 [0.098-0.101]; HR_PFS -HR_OS = 0.064 [0.059-0.069]), except in immunotherapy subgroup (HR_PFS -HR_OS = 0.835 [0.791-0.918]). In subgroup analyses, surrogacy of one-year survival persisted in different clinical contexts, and the disassociation between PFS and OS persisted in recent trials. One-year milestone survival showed strong surrogacy for OS in second-line NSCLC trials. Although no association was identified between PFS and OS, the strong HR_PFS -HR_OS correlation in immunotherapy trials indicates the potential of PFS as a SE in NSCLC trials involving immunotherapies.

Estimation of Study Time Reduction Using Surrogate End Points Rather Than Overall Survival in Oncology Clinical Trials

IMPORTANCE

Surrogate end points in oncology trade the advantage of reducing the time needed to conduct clinical trials for the disadvantage of greater uncertainty regarding the treatment effect on patient-centered end points, such as overall survival (OS) and quality of life.

OBJECTIVE

To quantify the amount of time saved through the acceptance of surrogate end points, including response rate (RR) and progression-free survival (PFS).

DESIGN, SETTING, AND PARTICIPANTS

This retrospective study of US Food and Drug Administration (FDA) oncology approvals and their drug registration trials based on actual publication analyzed the original and updated clinical trials data that led to FDA-approved drug indications in oncology from 2006 to 2017 by using existing publications, conference abstracts, and package inserts from the FDA. Data related to cancer type, line of therapy (first-line, second-line, and third- or later-line treatment of advanced or metastatic disease), FDA approval type, end point basis for approval (RR, PFS, or OS/quality of life), sample size, accrual rate, and drug RR were extracted by March 23, 2018. All data were analyzed by July 13, 2018.

MAIN OUTCOMES AND MEASURES

The main outcome was the study duration needed to complete the primary end point analysis used for each drug indication approval. This was estimated from reported enrollment dates, analysis cutoff dates, time to response, median duration of response, median PFS, and median OS.

RESULTS

In total, 188 distinct indications among 107 cancer drugs were identified. The RR was more often used for FDA approval in subsequent lines of therapy (17 of 71 drug indications [24%] in first-line therapy vs 34 of 77 drug indications [44%] in second-line therapy vs 19 of 24 drug indications [79%] in third- or later-line therapy, P < .001). Study duration for PFS (median, 31 [range, 10-104] months) was similar to that for OS (median, 33 [range, 12-117] months; P = .31), whereas study duration for RR (median, 25 [range, 11-54] months) was shorter than that for OS (P = .001). In multivariate analysis, compared with using OS, use of PFS as the end point was associated with study durations that were shorter by a mean of 11 months (95% CI, 5-17 months), and the use of RR as the end point was associated with study durations that were shorter by a mean of 19 months (95% CI, 13-25 months).

CONCLUSIONS AND RELEVANCE

From the findings of this study, an estimated 11 months appeared to be needed (ie, approximately 12% longer in the drug development cycle) to assess the OS benefit of a cancer drug. This study's findings suggest that this must be weighed against the downside of increased uncertainty of clinical benefit arising from using surrogate end points.

The assessment and appraisal of regenerative medicines and cell therapy products: an exploration of methods for review, economic evaluation and appraisal

BACKGROUND

The National Institute for Health and Care Excellence (NICE) commissioned a 'mock technology appraisal' to assess whether changes to its methods and processes are needed. This report presents the findings of independent research commissioned to inform this appraisal and the deliberations of a panel convened by NICE to evaluate the mock appraisal.

METHODS

Our research included reviews to identify issues, analysis methods and conceptual differences and the relevance of alternative decision frameworks, alongside the development of an exemplar case study of chimeric antigen receptor (CAR) T-cell therapy for treating acute lymphoblastic leukaemia.

RESULTS

An assessment of previous evaluations of regenerative medicines found that, although there were a number of evidential challenges, none was unique to regenerative medicines or was beyond the scope of existing methods used to conceptualise decision uncertainty. Regarding the clinical evidence for regenerative medicines, the issues were those associated with a limited evidence base but were not unique to regenerative medicines: small non-randomised studies, high variation in response and the intervention subject to continuing development. The relative treatment effects generated from single-arm trials are likely to be optimistic unless it is certain that the historical data have accurately estimated the efficacy of the control agent. Pivotal trials may use surrogate end points, which, on average, overestimate treatment effects. To reduce overall uncertainty, multivariate meta-analysis of all available data should be considered. Incorporating indirectly relevant but more reliable (more mature) data into the analysis can also be considered; such data may become available as a result of the evolving regulatory pathways being developed by the European Medicines Agency. For the exemplar case of CAR T-cell therapy, target product profiles (TPPs) were developed, which considered the 'curative' and 'bridging to stem-cell transplantation' treatment approaches separately. Within each TPP, three 'hypothetical' evidence sets (minimum, intermediate and mature) were generated to simulate the impact of alternative levels of precision and maturity in the clinical evidence. Subsequent assessments of cost-effectiveness were undertaken, employing the existing NICE reference case alongside additional analyses suggested within alternative frameworks. The additional exploratory analyses were undertaken to demonstrate how assessments of cost-effectiveness and uncertainty could be impacted by alternative managed entry agreements (MEAs), including price discounts, performance-related schemes and technology leasing. The panel deliberated on the range of TPPs, evidence sets and MEAs, commenting on the likely recommendations for each scenario. The panel discussed the challenges associated with the exemplar and regenerative medicines more broadly, focusing on the need for a robust quantification of the level of uncertainty in the cost-effective estimates and the potential value of MEAs in limiting the exposure of the NHS to high upfront costs and loss associated with a wrong decision.

CONCLUSIONS

It is to be expected that there will be a significant level of uncertainty in determining the clinical effectiveness of regenerative medicines and their long-term costs and benefits, but the existing methods available to estimate the implications of this uncertainty are sufficient. The use of risk sharing and MEAs between the NHS and manufacturers of regenerative medicines should be investigated further.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

Five criteria for using a surrogate endpoint to predict treatment effect based on data from multiple previous trials

A surrogate endpoint in a randomized clinical trial is an endpoint that occurs after randomization and before the true, clinically meaningful, endpoint that yields conclusions about the effect of treatment on true endpoint. A surrogate endpoint can accelerate the evaluation of new treatments but at the risk of misleading conclusions. Therefore, criteria are needed for deciding whether to use a surrogate endpoint in a new trial. For the meta-analytic setting of multiple previous trials, each with the same pair of surrogate and true endpoints, this article formulates 5 criteria for using a surrogate endpoint in a new trial to predict the effect of treatment on the true endpoint in the new trial. The first 2 criteria, which are easily computed from a zero-intercept linear random effects model, involve statistical considerations: an acceptable sample size multiplier and an acceptable prediction separation score. The remaining 3 criteria involve clinical and biological considerations: similarity of biological mechanisms of treatments between the new trial and previous trials, similarity of secondary treatments following the surrogate endpoint between the new trial and previous trials, and a negligible risk of harmful side effects arising after the observation of the surrogate endpoint in the new trial. These 5 criteria constitute an appropriately high bar for using a surrogate endpoint to make a definitive treatment recommendation.

Transfusion Decision Making in Pediatric Critical Illness

Transfusion decision making (TDM) in the critically ill requires consideration of: (1) anemia tolerance, which is linked to active pathology and to physiologic reserve, (2) differences in donor RBC physiology from that of native RBCs, and (3) relative risk from anemia-attributable oxygen delivery failure vs hazards of transfusion, itself. Current approaches to TDM (e.g. hemoglobin thresholds) do not: (1) differentiate between patients with similar anemia, but dissimilar pathology/physiology, and (2) guide transfusion timing and amount to efficacy-based goals (other than resolution of hemoglobin thresholds). Here, we explore approaches to TDM that address the above gaps.

Objective response rate is a possible surrogate endpoint for survival in patients with advanced, recurrent ovarian cancer

OBJECTIVE

Evaluate literature to assess response rate as a surrogate endpoint of survival in ovarian cancer (OC).

METHODS

Systematic review consistent with PRISMA criteria, identified randomized, controlled trials reporting overall survival (OS), progression-free survival (PFS), and objective response rate (ORR) in recurrent OC. MEDLINE® and Embase® searches (year 2000-March 23, 2015) were augmented by bibliographic screening. Proposed surrogate measures (independent variables) were ORR and disease control rate. True clinical outcomes (dependent variables) were median OS and PFS. Analyses were performed on unweighted and weighted data using correlation analysis, linear regression, and surrogate threshold effect (STE). Smaller STE indicates greater predictive precision with magnitude of STE dependent on variance of prediction.

RESULTS

Thirty-nine studies were included for review, representing 9223 platinum-sensitive and resistant patients. Objective response rate (r=0.82; P<0.001) was a better predictor than disease control rate (r=0.58; P<0.001) and strongly correlated with PFS (r=0.85; P<0.0001). Weighted-regression analysis demonstrated that for each 10% increase in ORR, PFS increased by 1.20months and OS by 2.83months. Regression analysis of treatment effects (odds ratio of response, hazard ratio of survival) suggests that a 10% increase in odds ratio of ORR would result in 2.5% reduction in the hazard ratio of OS. Based on weighted data, STE indicated that an ORR of ≥1% is needed to achieve nonzero OS benefit.

CONCLUSION

This systematic review supports ORR as a possible surrogate clinical trial endpoint for OS in recurrent OC with at least second-line therapy.

Use of surrogate outcomes in US FDA drug approvals, 2003-2012: a survey

OBJECTIVE

To evaluate, across a spectrum of diseases, how often surrogate outcomes are used as a basis for drug approvals by the US Food and Drug Administration (FDA), and whether and how the rationale for using treatment effects on surrogates as predictors of treatment effects on patient-centred outcomes is discussed.

STUDY DESIGN AND SETTING

We used the Drugs@FDA website to identify drug approvals produced from 2003 to 2012 by the FDA. We focused on four diseases (chronic obstructive pulmonary disease (COPD), type 1 or 2 diabetes, glaucoma and osteoporosis) for which surrogates are commonly used in trials. We reviewed the drug labels and medical reviews to provide empirical evidence on how surrogate outcomes are handled by the FDA.

RESULTS

Of 1043 approvals screened, 58 (6%) were for the four diseases of interest. Most drugs for COPD (7/9, 78%), diabetes (26/26, 100%) and glaucoma (9/9, 100%) were approved based on surrogates while for osteoporosis, most drugs (10/14, 71%) were also approved for patient-centred outcomes (fractures). The rationale for using surrogates was discussed in 11 of the 43 (26%) drug approvals based on surrogates. In these drug approvals, we found drug approvals for diabetes are more likely than the other examined conditions to contain a discussion of trial evidence demonstrating that treatment effects on surrogate outcomes predict treatment effects on patient-centred outcomes.

CONCLUSIONS

Our results suggest that the FDA did not use a consistent approach to address surrogates in assessing the benefits and harms of drugs for COPD, type 1 or 2 diabetes, glaucoma and osteoporosis. For evaluating new drugs, patient-centred outcomes should be chosen whenever possible. If the use of surrogate outcomes is necessary, then a consistent approach is important to review the evidence for surrogacy and consider surrogate's usage in the treatment and population under study.

Self-Management of Anticoagulant Therapy in Mechanical Heart Valve Patients: A Matched Cohort Study

BACKGROUND

Patient-self-management (PSM) of oral anticoagulant therapy with vitamin K antagonists for mechanical heart valves has demonstrated efficacy in randomized controlled trials. However, the effectiveness of PSM in clinical practice has only been investigated in small trials. Our aim was to evaluate the effectiveness of PSM of oral anticoagulant therapy in mechanical heart valve patients.

METHODS

We conducted a matched cohort study: cases were patients with a mechanical heart valve performing PSM affiliated with Aarhus University Hospital or Aalborg University Hospital, Denmark, in the period 1996 to 2012 (n = 615). Prospectively registered patient data were obtained from databases at two hospitals, and cross linkage between these databases and national patient registries provided detailed information on comorbidities and events. Control patients were matched (on sex, date of birth, year of first valve surgery, and grouped valve position) in a ratio of 5:1 (n = 3,075) with patients receiving conventional management who were randomly selected within the match group. The effectiveness and safety was estimated using major bleeding and thromboembolic events and death as outcomes.

RESULTS

We observed low event rates in the PSM group. After 5 years, PSM was associated with a lower risk of all-cause mortality compared with conventional management (adjusted hazard ratio of 0.49, 95% confidence interval: 0.34 to 0.71). The hazard ratios for thromboembolism and major bleeding were 0.91 (95% confidence interval: 0.66 to 1.24) and 0.83 (95% confidence interval: 0.56 to 1.22).

CONCLUSIONS

Owing to superior clinical effectiveness, self-managed oral anticoagulant therapy may potentially improve the standard of care for patients with mechanical heart valves.

Evaluating surrogate endpoints, prognostic markers, and predictive markers: Some simple themes

BACKGROUND

A surrogate endpoint is an endpoint observed earlier than the true endpoint (a health outcome) that is used to draw conclusions about the effect of treatment on the unobserved true endpoint. A prognostic marker is a marker for predicting the risk of an event given a control treatment; it informs treatment decisions when there is information on anticipated benefits and harms of a new treatment applied to persons at high risk. A predictive marker is a marker for predicting the effect of treatment on outcome in a subgroup of patients or study participants; it provides more rigorous information for treatment selection than a prognostic marker when it is based on estimated treatment effects in a randomized trial.

METHODS

We organized our discussion around a different theme for each topic.

RESULTS

"Fundamentally an extrapolation" refers to the non-statistical considerations and assumptions needed when using surrogate endpoints to evaluate a new treatment. "Decision analysis to the rescue" refers to use the use of decision analysis to evaluate an additional prognostic marker because it is not possible to choose between purely statistical measures of marker performance. "The appeal of simplicity" refers to a straightforward and efficient use of a single randomized trial to evaluate overall treatment effect and treatment effect within subgroups using predictive markers.

CONCLUSION

The simple themes provide a general guideline for evaluation of surrogate endpoints, prognostic markers, and predictive markers.

Outcomes and endpoints in trials of cancer treatment: the past, present, and future

Cancer treatment should allow patients to live better or longer lives, and ideally, both. Trial endpoints should show clinically meaningful improvements in patient survival or quality of life. Alternative endpoints such as progression-free survival, disease-free survival, and objective response rate have been used to identify benefit earlier, but their true validity as surrogate endpoints is controversial. In this Review we discuss the measurement, assessment, and benefits and limitations of trial endpoints in use for cancer treatment. Many stakeholders are affected, including regulatory agencies, industry partners, clinicians, and most importantly, patients. In an accompanying Review, reflections from individual stakeholders are incorporated into a discussion of what the future holds for clinical trial endpoints and design.

Cancer chemoprevention: Much has been done, but there is still much to do. State of the art and possible new approaches

Over the past three decades great efforts have been made in search of cancer chemoprevention strategies. The increase in knowledge of the long process from normal to cancer cell has enabled interventions in terms of lifestyle modifications, natural compounds or drugs to block or reverse the process. Great successes have been achieved, especially for breast and colorectal cancer. However, these strategies have yet to find clinical application on a large scale. In this article we identify the achievements, the pitfalls and the next steps to be taken to improve the efficacy and applicability of chemoprevention strategies. Among the crucial key points to be implemented are educational activities for physicians to appropriately disseminate the aim and indeed the culture of chemoprevention. It is essential to improve the risk-benefit balance, seeking the minimal active doses, intermittent schedules, a better characterization of the risk categories via a more personalized intervention based on individual characteristics, and ensure the containment of costs of public and private health prevention programs.

Methodologies for measurement of cardiac markers

Antibody-based tests are the primary technology used for clinical measurement of cardiac biomarkers in peripheral circulation. This article focuses on the principles of immunometric methods that have been applied to cardiac biomarkers of widespread clinical utility (CKMB, Troponins, and B-type natriuretic proteins) and of more recent clinical testing (ST2, Galectin-2, and myosin binding protein C). How these principles are applied in the design of immunometric assays and how they influence assay performance in quantifying cardiac biomarkers in biologic fluids (serum or plasma) is discussed.

Suitable trial designs and cohorts for preventive breast cancer agents

Effective chemoprevention of oestrogen receptor (ER)-positive breast cancer has been shown convincingly using several selective ER modulators and the aromatase inhibitor exemestane. Although these agents are well tolerated and the numbers needed-to-treat in the prevention setting are similar to other established preventive interventions, uptake has been poor in clinical practice because of difficulties in visualizing risk, predicting individual outcomes and measuring treatment benefit. In addition, new agents targeting ER-negative breast cancer are urgently needed. The development of new agents is hampered by the lack of suitable biomarkers and targets, as well as regulatory and financial considerations. Establishing breast cancer chemoprevention in standard clinical practice will require advances in many different fields, including biomarker research, the development of more powerful tools to predict and communicate the risks and benefits of treatments and establishing innovative trial designs. Furthermore, changes in regulatory procedures could reduce the size and cost of trials needed in the prevention setting. Identification of biomarkers for risk and efficacy that are easily accessible, such as blood-based biomarkers, will be key to future chemoprevention strategies.

Causal inference, probability theory, and graphical insights

Causal inference from observational studies is a fundamental topic in biostatistics. The causal graph literature typically views probability theory as insufficient to express causal concepts in observational studies. In contrast, the view here is that probability theory is a desirable and sufficient basis for many topics in causal inference for the following two reasons. First, probability theory is generally more flexible than causal graphs: Besides explaining such causal graph topics as M-bias (adjusting for a collider) and bias amplification and attenuation (when adjusting for instrumental variable), probability theory is also the foundation of the paired availability design for historical controls, which does not fit into a causal graph framework. Second, probability theory is the basis for insightful graphical displays including the BK-Plot for understanding Simpson's paradox with a binary confounder, the BK2-Plot for understanding bias amplification and attenuation in the presence of an unobserved binary confounder, and the PAD-Plot for understanding the principal stratification component of the paired availability design.