True Endpoint Research Articles

Surrogate endpoint meta-regression: Useful statistics for regulators and trialists

ObjectivesThe main purpose of using a surrogate endpoint is to estimate the treatment effect on the true endpoint sooner than with a true endpoint. Based on a meta-regression of historical randomized trials with surrogate and true endpoints, we discuss statistics for applying and evaluating surrogate endpoints. MethodsWe computed statistics from two types of linear meta-regressions for trial-level data: simple random effects and novel random effects with correlations among estimated treatment effects in trials with more than 2 arms. A key statistic is the estimated intercept of the meta-regression line. An intercept that is small or not statistically significant increases confidence when extrapolating to a new treatment because of consistency with a single causal pathway and invariance to labeling of treatments as controls. For a regulator applying the meta-regression to a new treatment, a useful statistic is the 95% prediction interval. For a clinical trialist planning a trial of a new treatment, useful statistics are the surrogate threshold effect proportion, the sample size multiplier adjusted for dropouts, and the novel true endpoint advantage. ResultsWe illustrate these statistics with surrogate endpoint meta-regressions involving anti-hypertension treatment, breast cancer screening, and colorectal cancer treatment. ConclusionRegulators and trialists should consider using these statistics when applying and evaluating surrogate endpoints.

Evaluating time-to-event surrogates for time-to-event true endpoints: an information-theoretic approach based on causal inference.

Putative surrogate endpoints must undergo a rigorous statistical evaluation before they can be used in clinical trials. Numerous frameworks have been introduced for this purpose. In this study, we extend the scope of the information-theoretic causal-inference approach to encompass scenarios where both outcomes are time-to-event endpoints, using the flexibility provided by D-vine copulas. We evaluate the quality of the putative surrogate using the individual causal association (ICA)-a measure based on the mutual information between the individual causal treatment effects. However, in spite of its appealing mathematical properties, the ICA may be ill defined for composite endpoints. Therefore, we also propose an alternative rank-based metric for assessing the ICA. Due to the fundamental problem of causal inference, the joint distribution of all potential outcomes is only partially identifiable and, consequently, the ICA cannot be estimated without strong unverifiable assumptions. This is addressed by a formal sensitivity analysis that is summarized by the so-called intervals of ignorance and uncertainty. The frequentist properties of these intervals are discussed in detail. Finally, the proposed methods are illustrated with an analysis of pooled data from two advanced colorectal cancer trials. The newly developed techniques have been implemented in the R package Surrogate.

Assessing the Operational Characteristics of the Individual Causal Association as a Metric of Surrogacy in the Binary Continuous Setting.

In a causal inference framework, a new metric has been proposed to quantify surrogacy for a continuous putative surrogate and a binary true endpoint, based on information theory. The proposed metric, termed the individual causal association (ICA), was quantified using a joint causal inference model for the corresponding potential outcomes. Due to the non-identifiability inherent in this type of models, a sensitivity analysis was introduced to study the behavior of the ICA as a function of the non-identifiable parameters characterizing the aforementioned model. In this scenario, to reduce uncertainty, several plausible yet untestable assumptions like monotonicity, independence, conditional independence or homogeneous variance-covariance, are often incorporated into the analysis. We assess the robustness of the methodology regarding these simplifying assumptions via simulation. The practical implications of the findings are demonstrated in the analysis of a randomized clinical trial evaluating an inactivated quadrivalent influenza vaccine.

Aeons: approximating the end of nested sampling

ABSTRACT This paper presents analytical results on the anatomy of nested sampling, from which a technique is developed to estimate the run-time of the algorithm that works for any nested sampling implementation. We test these methods on both toy models and true cosmological nested sampling runs. The method gives an order-of-magnitude prediction of the endpoint at all times, forecasting the true endpoint within standard error around the halfway point.

Proportion of treatment effect explained: An overview of interpretations.

The selection of the primary endpoint in a clinical trial plays a critical role in determining the trial's success. Ideally, the primary endpoint is the clinically most relevant outcome, also termed the true endpoint. However, practical considerations, like extended follow-up, may complicate this choice, prompting the proposal to replace the true endpoint with so-called surrogate endpoints. Evaluating the validity of these surrogate endpoints is crucial, and a popular evaluation framework is based on the proportion of treatment effect explained (PTE). While methodological advancements in this area have focused primarily on estimation methods, interpretation remains a challenge hindering the practical use of the PTE. We review various ways to interpret the PTE. These interpretations-two causal and one non-causal-reveal connections between the PTE principal surrogacy, causal mediation analysis, and the prediction of trial-level treatment effects. A common limitation across these interpretations is the reliance on unverifiable assumptions. As such, we argue that the PTE is only meaningful when researchers are willing to make very strong assumptions. These challenges are also illustrated in an analysis of three hypothetical vaccine trials.

Effectiveness of Surgical Periodontal Therapy in Oral Health-Related Quality of Life.

Background and objective The objective of this study is to evaluate and compare the surrogate and true end points following surgical periodontal therapy using the Oral Health Impact Profile-14 (OHIP-14) questionnaire. Materials and methods The study included a total of 30 participants, comprising 15 males and 15 females aged between 25 and 50 years. All individuals who had undergone periodontal flap surgery for generalized chronic periodontitis at the Department of Periodontology, Saveetha Dental College and Hospitals were included in the study. The OHIP-14 questionnaire was used to assess the patient-centered outcomes (true end points) pre- and post-flap surgery at baseline and six months. Surrogate end points such as the clinical attachment level (CAL), probing pocket depth (PPD), and gingival index (GI) were recorded at baseline and six months pre- and post-flap surgery. Results Clinical parameters such as the GI (p=0.03*), CAL (p=0.03), and PPD (p=0.02*) showed a statistically significant improvement after surgery. Patient-centered outcomes showed statistically significant differences in terms of taste perception, reduction in pain sensation, improvement in self-consciousness and reduction in anxiety levels, diminution of the feeling of embarrassment and enhancement in the ability to relax due to problems associated with gums, and improvement in the workplace (p<0.05) post-operatively. Conclusion Surgical periodontal therapy plays a pivotal role in improving oral health-related quality of life (OHRQoL) among patients with chronic periodontal disease. Utilizing OHIP-14 as an assessment tool enables a comprehensive evaluation of treatment outcomes, encompassing various dimensions of oral health impact. Patient-centered outcomes such as psychological discomfortand functional limitations can be achieved only by an interdisciplinary approach.

Enhancing medical image object detection with collaborative multi-agent deep Q-networks and multi-scale representation

Object detection holds a crucial role in medical diagnostics. Tasks like organ segmentation and malignancy diagnosis typically necessitate preliminary localization of corresponding anatomical structures. Precise positioning ensures that only pertinent regions require processing, leading to a potential reduction in computational and storage demands. Conventional image detection approaches necessitate numerous candidate boxes, resulting in redundant computations. Developing techniques capable of accurately detecting medical image objects without reliance on candidate boxes holds substantial practical significance. This paper introduces a 2D method for detecting medical image objects, which leverages multi-agent deep Q-network reinforcement learning and a multi-scale image representation. The method constructs a collaborative environment for multiple agents. These agents individually govern the upper-right corner and lower-left corner positions of the object detection frame, progressively converging toward the actual endpoint through iterative interactions. To expedite the detection process, a multi-scale image representation technique is employed. This method segments the process into three scales. Initially, within the coarse-scale space, the agent approximates the region containing the true endpoint, subsequently executing oscillatory movements. Progressively, it refines its approach within the fine-scale space, advancing toward the genuine endpoint with smaller iterative steps. The detection results demonstrate that collaborative detection among agents yields a 2.45%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} higher intersection over union compared to non-collaborative detection. Agents exhibit varying step sizes and fields of view in different scale spaces, leading to a reduction in detection time by 0.12 s compared to single-scale comparison. Experimental outcomes demonstrate the superiority of the medical image target detection method proposed in this study over prevailing mainstream detection algorithms.

Understanding the Brain-based Determination of Death When Organ Recovery Is Performed With DCDD In Situ Normothermic Regional Perfusion.

Understanding the Brain-based Determination of Death When Organ Recovery Is Performed With DCDD In Situ Normothermic Regional Perfusion.

Feeling lucky? prospective and retrospective cues for sensorimotor confidence.

On a daily basis, humans interact with the outside world using judgments of sensorimotor confidence, constantly evaluating our actions for success. We ask, what sensory and motor-execution cues are used in making these judgements and when are they available? Two sources of temporally distinct information are prospective cues, available prior to the action (e.g., knowledge of motor noise and past performance), and retrospective cues specific to the action itself (e.g., proprioceptive measurements). We investigated the use of these two cues in two tasks, a secondary motor-awareness task and a main task in which participants reached toward a visual target with an unseen hand and then made a continuous judgment of confidence about the success of the reach. Confidence was reported by setting the size of a circle centered on the reach-target location, where a larger circle reflects lower confidence. Points were awarded if the confidence circle enclosed the true endpoint, with fewer points returned for larger circles. This incentivized accurate reaches and attentive reporting to maximize the score. We compared three Bayesian-inference models of sensorimotor confidence based on either prospective cues, retrospective cues, or both sources of information to maximize expected gain (i.e., an ideal-performance model). Our findings showed two distinct strategies: participants either performed as ideal observers, using both prospective and retrospective cues to make the confidence judgment, or relied solely on prospective information, ignoring retrospective cues. Thus, participants can make use of retrospective cues, evidenced by the behavior observed in our motor-awareness task, but these cues are not always included in the computation of sensorimotor confidence.

Comparison of the Quality of Phase Ⅲ Non-commercial Clinical Trials in Japan, the United States, and the United Kingdom before the Implementation of the New Japanese Clinical Trials Act

Recent research misconduct, represented by the valsartan scandal, has raised questions about the quality of non-commercial trials not for regulatory approval in Japan. Unlike in the US and Europe, non-commercial clinical trials are not regulated by law in Japan, which was believed to be a reason why quality could not be guaranteed. However, until now, few studies have compared the quality of clinical studies among countries. In this study, the quality of Japanese non-commercial phase Ⅲ trials before the implementation of the Clinical Trials Act (CTA) in 2018 was compared with Western non-commercial trials registered in the database using study design as quality indicators. The study found a lower proportion of randomized, controlled trials (RCTs) (70％ in Japan, 89％ in the US, and 88％ in the UK) and fewer blinded designs (46％ in Japan, 59％ in the US, and 52％ in the UK) in non-commercial trials in Japan. Single-arm trials not for either cancer or rare diseases, which are usually considered low quality because of a lack of fair comparison, were more common in Japan (82％) than in the US (76％) and the UK (50％). In terms of endpoint setting, most non-commercial trials in Japan had surrogate endpoints, with only a few trials using true endpoints (5％) compared to the US (24％) and the UK (40％). A comparison of these quality indicators in Japanese non-commercial trials before and after the CTA implementation showed no increases in the proportion of RCT and blinded designs, but an increase in the proportion of trials using true endpoints and appropriate use of single-arm design. In conclusion, the lack of legal regulation in Japan may have indirectly affected the quality of non-commercial phase Ⅲ trials in Japan at the time. Implementation of the CTA has contributed to some quality improvements, but the effect is limited at present.

Assessment of Surrogate End Point Trends in Clinical Trials to Approve Oncology Drugs From 2001 to 2020 in Japan

A surrogate end point (SEP) is an end point used in clinical trials as an alternative for measuring the true clinical benefit. The use of SEPs in trials shortens their duration. To investigate the use of SEPs in clinical trials to support the approval of anticancer drugs and to determine whether confirmatory studies that use overall survival (OS) as an end point are being conducted in Japan. In this cross-sectional study, drug approvals and background information were obtained from publicly available information, such as the Pharmaceuticals and Medical Devices Agency website, for anticancer drugs approved in Japan from January 2001 to December 2020. Data analysis was performed from September 2021 to March 2022. Characteristics of approved oncology drugs in Japan, end points for pivotal clinical trials, and outcomes of confirmatory trials using OS as an end point following drug approval. There were 299 anticancer drugs approved in Japan during the study period. Of these, 142 (47.5%) were molecular-targeted drugs, the most common of which targeted non-small cell lung cancer. There were 111 (37.1%) anticancer drugs with orphan designation. From 2001 to 2005, OS was used as an end point in 1 approval (3.6%); however, from 2006 to 2020, OS was used in 86 approvals (31.7%). Of the 212 anticancer drugs approved on the basis of SEPs, confirmatory studies with OS as the end point were conducted for only 37 approvals (17.5%); for the remaining 175 approvals, studies are under way for 35 approvals (16.5%), were waivered for 75 approvals (35.4%), and were not conducted for 65 approvals (30.7%). Furthermore, in 20 drug approvals (9.4%), the conducted confirmatory studies were not effective in determining the OS, but the drugs were approved following re-examination. The findings of this study suggest that starting from 2005, the use of OS as an end point has increased in studies supporting the approval of anticancer drugs in Japan. However, even after 2005, approximately two-thirds of these approvals were SEP based. Postmarketing surveillance studies of the true end points are necessary to validate the use of SEPs.

Biochemical recurrence (BCR) surrogacy for clinical outcomes after radiotherapy for adenocarcinoma of the prostate (BCRSCRAP): A meta-analysis from MARCAP Consortium.

391 Background: Event-free survival, a PSA-driven endpoint, was shown to not be surrogate endpoint for overall survival (OS) in the ICECAP two-stage meta-analytic approach. However, time to biochemical recurrence (TTBCR) in NRG/RTOG 9202 met Prentice criteria for surrogacy. We performed an individual patient data (IPD) meta-analysis of 11 randomized controlled trials evaluating RT dose escalation, ADT use, and adjuvant ADT prolongation to evaluate the surrogacy of time to BCR (TTBCR), censoring for non-prostate cancer deaths, using both approaches to evaluate surrogacy. Methods: This individual patient level meta-analysis was performed using data from the MARCAP consortium, and 11 radiotherapy trials were included. TTBCR was defined as time to developing a BCR or experiencing prostate cancer-specific mortality (PCSM), with censoring at time of other-cause death or loss to follow-up. Landmark analyses were used to test the Prentice criteria for surrogacy. For patient level correlation between TTBCR and OS, we applied a bivariate Copula model to estimate the Kendall’s τ. For trial level correlation of the treatment effect on TTBCR and true endpoints, a weighted linear regression model was applied between the effects of treatment (natural log of hazard ratio [log-HR]) on OS versus TTBCR using a weightage that was inverse variance of BCR log-HR estimate. Results: Based on Prentice criteria, BCR at the landmark time point of 48 months was associated with increased risk of mortality in trials that compared treatment intensification with adjuvant ADT prolongation (HR 2.18 [95% CI 1.95-2.42]), the addition of ADT (HR 1.38 [1.25-1.54]), and RT dose escalation (HR 2.12 [1.83-2.46]) on uni- and multi-variable analyses. At the patient level, there was a low to moderate level correlation between BCR and OS with Kendall’s τ of 0.34 and a R2 of 0.55 for correlation of treatment effect on TTBCR and OS. At the trial level, there was a poor correlation between treatment effect on TTBCR and OS (R2=0.16). Conclusions: This IPD meta-analysis demonstrates that while BCR is prognostic, it is not a surrogate endpoint for OS in localized prostate cancer for patients treated with a diverse array of radiotherapeutic strategies. This highlights the importance of other cause mortality in prostate cancer. Our results highlight the differences in interpretability of Prentice criteria and the two-stage meta-analytic approach and suitability of endpoints for clinical trial design.

Incorporating Covariates into Measures of Surrogate Paradox Risk.

Clinical trials often collect intermediate or surrogate endpoints other than their true endpoint of interest. It is important that the treatment effect on the surrogate endpoint accurately predicts the treatment effect on the true endpoint. There are settings in which the proposed surrogate endpoint is positively correlated with the true endpoint, but the treatment has opposite effects on the surrogate and true endpoints, a phenomenon labeled "surrogate paradox". Covariate information may be useful in predicting an individual's risk of surrogate paradox. In this work, we propose methods for incorporating covariates into measures of assessing the risk of surrogate paradox using the meta-analytic causal association framework. The measures calculate the probability that a treatment will have opposite effects on the surrogate and true endpoints and determine the size of a positive treatment effect on the surrogate endpoint that would reduce the risk of a negative treatment effect on the true endpoint as a function of covariates, allowing the effects of covariates on the surrogate and true endpoint to vary across trials.

Un-yielding: Evidence for the agriculture transformation we need.

There has been a seismic shift in the center of gravity of scientific writing and thinking about agriculture over the past decades, from a prevailing focus on maximizing yields toward a goal of balancing trade-offs and ensuring the delivery of multiple ecosystem services. Maximizing crop yields often results in a system where most benefits accrue to very few (in the form of profits), alongside irreparable environmental harm to agricultural ecosystems, landscapes, and people. Here, we present evidence that an un-yielding, which we define as de-emphasizing the importance of yields alone, is necessary to achieve the goal of a more Food secure, Agrobiodiverse, Regenerative, Equitable and just (FARE) agriculture. Focusing on yields places the emphasis on one particular outcome of agriculture, which is only an intermediate means to the true endpoint of human well-being. Using yields as a placeholder for this outcome ignores the many other benefits of agriculture that people also care about, like health, livelihoods, and a sense of place. Shifting the emphasis to these multiple benefits rather than merely yields, and to their equitable delivery to all people, we find clear scientific evidence of win-wins for people and nature through four strategies that foster FARE agriculture: reduced disturbance, systems reintegration, diversity, and justice (in the form of securing rights to land and other resources). Through a broad review of the current state of agriculture, desired futures, and the possible pathways to reach them, we argue that while trade-offs between some ecosystem services in agriculture are unavoidable, the same need not be true of the end benefits we desire from them.

Validating ORR and PFS as surrogate endpoints in phase II and III clinical trials for NSCLC patients: difference exists in the strength of surrogacy in various trial settings

ObjectiveThis study aims to systematically validate the performance of surrogate endpoints in phase II and III clinical trials for NSCLC patients under various trial settings.MethodsA literature search retrieved all registered phase II and III trials of NSCLC patients in which OS, with at least one of ORR and PFS, were reported. Associations between surrogate and true endpoints were assessed on two levels. On the arm level, three pairs of correlations, i.e., ORR vs. median OS, ORR vs. median PFS, and median PFS vs. median OS, were analysed using Spearman’s rho. On the trial level, similarly, three pairs of correlations, i.e., ΔORR vs. HR of OS, ΔORR vs. HR of PFS, and HR of PFS vs. HR of OS, were analysed using Spearman’s rho and weighted linear regression model respectively. Finally, sensitivity analyses were performed to explore surrogacy under various trial settings.ResultsAt arm level, three pairs of correlations are all high (Spearman’s rho = 0.700, 0.831, 0.755, respectively). At trial level, there is a low correlation between ΔORR and HR of OS, a high correlation between ΔORR and HR of PFS and a moderate correlation between HR of PFS and HR of OS (Spearman’s rho = 0.462, 0.764, 0.584, respectively). In the sensitivity analysis, we find correlations between surrogate and true endpoints vary by different trial settings. It is noteworthy that the strength of surrogacy of these intermediate endpoints in targeted therapy is greater than that in immunotherapy.ConclusionAccording to the arm-level and trial level-analysis, we suggest that in phase II and III trials of targeted therapy and immunotherapy for NSCLC patients: 1) ORR lacks validity for the surrogacy of OS, excluding in first-line therapy, and 2) ORR may be an appropriate surrogate endpoint for PFS, and 3) PFS may be considered a modest surrogacy for OS, with better performance in first-line therapy trials. Moreover, to provide more convincing evidence of surrogacy of the surrogate endpoints, patient-level analyses are in desperate need.

An efficient algorithm to assess multivariate surrogate endpoints in a causal inference framework

Multivariate surrogate endpoints can improve the efficiency of the drug development process, but their evaluation raises many challenges. Recently, the so-called individual causal association (ICA) has been introduced for validation purposes in the causal-inference paradigm. The ICA is a function of a partially identifiable correlation matrix (R) and, hence, it cannot be estimated without making untestable assumptions. This issue has been addressed via a simulation-based analysis. Essentially, the ICA is assessed across a set of values for the non-identifiable entries in R that lead to a valid correlation matrix and this has been implemented using a fast algorithm based on partial correlations (PC). Using theoretical arguments and simulations, it is shown that, in spite of its computational efficiency, the PC algorithm may lead to the spurious effect that adding non-informative surrogates, i.e., surrogates that convey no information on the treatment effect on the true endpoint, seemingly reduces the ICA range. To address this, a modified PC algorithm (MPC) is proposed. Based on simulations, it is shown that the MPC algorithm removes this nuisance effect and increases computational efficiency.

Abstract P2-12-09: Correlation between pathologic complete response (pCR), event-free survival (EFS)/disease-free survival (DFS) and overall survival (OS) in neoadjuvant and/or adjuvant (NAdj/Adj) hormone receptor positive (HR+) and human epidermal growth factor receptor-2 negative (HER2-) breast cancer (BC)

Abstract Background OS, the “gold standard” endpoint in oncology studies, is challenging to measure in earlier stages of BC because of considerably long survival. Further, during extended patient follow-up, treatment effect on OS can become impacted by multiple factors such as non-malignant causes of death, treatment cross-over, and post-progression treatments, making it difficult to isolate the true treatment effect. As an alternative, surrogate endpoints (SE) such as DFS, EFS, and pCR can be used to ascertain meaningful benefit in true endpoint (TE). While studies have previously explored the association of such SEs with OS in different BC populations, there is a dearth in literature of studies reporting on correlation in the NAdj/Adj settings in the HR+/HER2- BC population. The aim of this study is to conduct a correlation analysis between various SE-TE pairs (DFS/EFS vs OS, pCR vs OS and pCR vs DFS/EFS) in this population, from data reported in studies identified from a systematic literature review (SLR). For each pair of endpoints, both absolute and relative treatment effects were evaluated. Methods An SLR was conducted in NAdj/Adj setting for HR+/HER2- BC to identify studies that reported any two of pCR, DFS/EFS and OS. The search was carried out using MEDLINE, Embase, Cochrane Library databases (2000 onward), four BC-related conference proceedings (2018-2020) and clinical trial registries. Relative treatment effect sizes were quantified in terms of HRs for DFS/EFS and OS, and odds ratios (ORs) for pCR. Absolute treatment effects were analyzed in terms of survival probability of EFS/DFS and OS at landmark time points and pCR rates. A weighted least squares regression analysis was carried out to determine the strength of association between the various TE-SE pairs. The regression analysis was carried out on log scale for relative treatment effects and on linear scale for absolute treatment effects. Pearson correlation coefficient (r) was used to quantify the strength of association; coefficient of determination (R2) was also reported to evaluate goodness-of-fit. Sensitivity analyses were performed by considering linear scale (for relative outcomes only), using inverse of variance as weights, and by removing outliers. Results A total of 135 publications were identified in the SLR. Regression analysis showed that there is a statistically significant positive association (r= 0.91; 95%CI, 0.83-0.96, p &amp;lt;0.0001) between log(HROS) and log (HRDFS/EFS), with R2 = 0.83. The correlation between log (HRDFS/EFS) and log(ORpCR) was not strong (r= 0.24; 95% CI, -0.63-0.84; p, 0.603). Correlation of ORpCR with HROS was not conducted due to inadequate data points (n=3). For the absolute outcomes, landmark DFS/EFS at 1 year and 2 years were each found to be moderately correlated with OS at 4 and 5 years. The correlation between pCR rate and landmark DFS/EFS and OS was not strong. The association between the pairs of the endpoints was consistent across all the sensitivity analyses. Conclusions Results of this regression analysis suggest that relative treatment effect on DFS/EFS is strongly associated with relative treatment effect on OS in HR+HER2- BC and could be used as a surrogate for OS. Correlation analysis for HR(DFS/EFS) vs. HR(OS) and OR(pCR) vs. HR(DFS/EFS)Model# studies# observationsPearson coefficient, r (95% CI)p-valueCoefficient of determination, R2 (95% CI)Slope (95% CI)P-valueHRDFS/EFS vs. HROS24320.91 (0.83-0.96)&amp;lt;0.00010.83 (0.69-0.89)0.99 (0.83-1.16)&amp;lt;.0001ORpCR vs. HRDFS/EFS570.24 (-0.63-0.84)0.60.06 (0-0.47)0.23 (-0.86-1.31)0.6099 Citation Format: Anagha Gogate, Inkyu Kim, Sandip Ranjan, Amit Kumar, Eros Papademetriou, Hitesh Bhandari, Ravi Potluri. Correlation between pathologic complete response (pCR), event-free survival (EFS)/disease-free survival (DFS) and overall survival (OS) in neoadjuvant and/or adjuvant (NAdj/Adj) hormone receptor positive (HR+) and human epidermal growth factor receptor-2 negative (HER2-) breast cancer (BC) [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-12-09.

Conditional copula models for correlated survival endpoints: Individual patient data meta-analysis of randomized controlled trials.

Correlations among survival endpoints are important for exploring surrogate endpoints of the true endpoint. With a valid surrogate endpoint tightly correlated with the true endpoint, the efficacy of a new drug/treatment can be measurable on it. However, the existing methods for measuring correlation between two endpoints impose an invalid assumption: correlation structure is constant across different treatment arms. In this article, we reconsider the definition of Kendall's concordance measure (tau) in the context of individual patient data meta-analyses of randomized controlled trials. According to our new definition of Kendall's tau, its value depends on the treatment arms. We then suggest extending the existing copula (and frailty) models so that their Kendall's tau can vary across treatment arms. Our newly proposed model, a joint frailty-conditional copula model, is the implementation of the new definition of Kendall's tau in meta-analyses. In order to facilitate our approach, we develop an original R function condCox.reg(.) and make it available in the R package joint.Cox (https://CRAN.R-project.org/package=joint.Cox). We apply the proposed method to a gastric cancer dataset (3288 patients in 14 randomized trials from the GASTRIC group). This data analysis concludes that Kendall's tau has different values between the surgical treatment arm and the adjuvant chemotherapy arm (p-value<0.001), whereas disease-free survival remains a valid surrogate at individual level for overall survival in these trials.

Incorporating baseline covariates to validate surrogate endpoints with a constant biomarker under control arm.

A surrogate endpoint S in a clinical trial is an outcome that may be measured earlier or more easily than the true outcome of interest T. In this work, we extend causal inference approaches to validate such a surrogate using potential outcomes. The causal association paradigm assesses the relationship of the treatment effect on the surrogate with the treatment effect on the true endpoint. Using the principal surrogacy criteria, we utilize the joint conditional distribution of the potential outcomes T, given the potential outcomes S. In particular, our setting of interest allows us to assume the surrogate under the placebo, , is zero-valued, and we incorporate baseline covariates in the setting of normally distributed endpoints. We develop Bayesian methods to incorporate conditional independence and other modeling assumptions and explore their impact on the assessment of surrogacy. We demonstrate our approach via simulation and data that mimics an ongoing study of a muscular dystrophy gene therapy.

The individual-level surrogate threshold effect in a causal-inference setting with normally distributed endpoints.

In the meta-analytic surrogate evaluation framework, the trial-level coefficient of determination quantifies the strength of the association between the expected causal treatment effects on the surrogate (S) and the true (T) endpoints. Burzykowski and Buyse supplemented this metric of surrogacy with the surrogate threshold effect (STE), which is defined as the minimum value of the causal treatment effect on S for which the predicted causal treatment effect on T exceeds zero. The STE supplements with a more direct clinically interpretable metric of surrogacy. Alonso et al. proposed to evaluate surrogacy based on the strength of the association between the individual (rather than expected) causal treatment effects on S and T. In the current paper, the individual-level surrogate threshold effect (ISTE) is introduced in the setting where S and T are normally distributed variables. ISTE is defined as the minimum value of the individual causal treatment effect on S for which the lower limit of the prediction interval around the individual causal treatment effect on T exceeds zero. The newly proposed methodology is applied in a case study, and it is illustrated that ISTE has an appealing clinical interpretation. The R package surrogate implements the methodology and a web appendix (supporting information) that details how the analyses can be conducted in practice is provided.