Causal Inference and Estimands in Clinical Trials

Cancer medicine continues to evolve into a more molecularly based discipline with many new treatments being developed aimed at specific cancer-related pathways and genetic alterations. In order to meet the challenges of the evolving science, new clinical trial designs and approaches for treatment trials are needed to evaluate the promise of these potential therapies. The National Cancer Institute (NCI) with the national NCI Clinical Trials Network (NCTN) Groups will launch two clinical research initiatives in lung cancer in 2014 that demonstrate innovative approaches to the design and conduct of clinical trials. The first initiative is the ALCHEMIST trial (Adjuvant Lung Cancer Enrichment Marker Identification and Sequencing Trial) which is aimed at patients with lung adenocarcinoma that has been entirely removed by surgery. In this study, samples of tumor tissue from thousands of patients will be tested for two specific genetic alterations, one in the EGFR gene, and the other in the ALK gene. These alterations are not common; about 10% of the lung cancer patient population in the US will have tumors with alterations in the EGFR gene and 5% will have alterations in the ALK gene. Rather than conduct a single trial for each alteration, the screening component of the ALCHEMIST trial will screen patients for both alterations at the same time. The ALCHEMIST is connected to 2 specific NCI-supported NCTN Group clinical treatment trials that are each testing a targeted agent specific for one of the alterations in conjunction with adjuvant therapy. Patients found to have tumors containing one of the alterations will be offered enrollment onto the appropriate clinical treatment trial. Every patient in the ALCHEMIST trial, including those without an EGFR or ALK alteration, will also be studied for cancer risk characteristics and their tumor tissue will be analyzed in a research genomics initiative conducted by the NCI Center for Cancer Genomics (CCG). This effort will employ next-generation sequencing technology to capitalize on the foundation of the CCG's earlier effort, the Cancer Genome Atlas. The second initiative, the Advanced Squamous Cell Carcinoma Lung Master Protocol, is a public-private effort made possible through collaboration with stakeholders including the NCI, NCTN Groups, FDA, NIH Foundation, the Friends of Cancer patient advocacy group, and the pharmaceutical industry. By pooling resources to screen tumor samples from about a thousand patients per year with advanced squamous cell carcinoma for multiple genetic alterations, this master protocol will enable several, randomized, phase II clinical trials with promising new agents to take place in parallel within a single study management framework. Agents will be evaluated rapidly and either be further evaluated in a phase III clinical trial or replaced by other new agents for phase II evaluation, without having to develop individual trials de novo each time. Compared with the usual process of developing clinical trials, these two new design approaches will hopefully allow more patients to be enrolled in trials with agents targeted for important molecular characteristics specific for their tumors and the trials will yield clinically useful results more rapidly. Citation Format: Margaret M. Mooney, Jack Welch, Jeffrey S. Abrams. Clinical trial design and master protocols in NCI clinical treatment trials. [abstract]. In: Proceedings of the AACR-IASLC Joint Conference on Molecular Origins of Lung Cancer; 2014 Jan 6-9; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2014;20(2Suppl):Abstract nr IA08.

A draft addendum to ICH E9 has been released for public consultation in August 2017. The addendum focuses on two topics particularly relevant for randomized confirmatory clinical trials: estimands and sensitivity analyses. The need to amend ICH E9 grew out of the realization of a lack of alignment between the objectives of a clinical trial stated in the protocol and the accompanying quantification of the "treatment effect" reported in a regulatory submission. We embed time-to-event endpoints in the estimand framework and discuss how the four estimand attributes described in the addendum apply to time-to-event endpoints. We point out that if the proportional hazards assumption is not met, the estimand targeted by the most prevalent methods used to analyze time-to-event endpoints, logrank test, and Cox regression depends on the censoring distribution. We discuss for a large randomized clinical trial how the analyses for the primary and secondary endpoints as well as the sensitivity analyses actually performed in the trial can be seen in the context of the addendum. To the best of our knowledge, this is the first attempt to do so for a trial with a time-to-event endpoint. Questions that remain open with the addendum for time-to-event endpoints and beyond are formulated, and recommendations for planning of future trials are given. We hope that this will provide a contribution to developing a common framework based on the final version of the addendum that can be applied to design, protocols, statistical analysis plans, and clinical study reports in the future.

Timely Completion of Scientifically Rigorous Cancer Clinical Trials: An Unfulfilled Priority

Recruitment to clinical trials remains poor, and patient knowledge of clinical trials is one barrier to recruitment. To identify knowledge deficits, we conducted and compared surveys measuring actual patient knowledge and clinical trialist priorities for patient knowledge. Consenting patients at a tertiary cancer centre answered a survey that included 2 opinion questions about their own knowledge and willingness to join a trial, and22 knowledge questions. Clinical researchers at the centre were asked 13 questions about the importance of various trials factors. Of 126 patients surveyed, 16% had joined a clinical trial, and 42% had a secondary school education or less. The mean correct response rate on the knowledge questions was 58%. Higher rates of correct responses were associated with lower age (p = 0.05), greater education (p = 0.006), prior trial participation (p < 0.001), agreement or strong agreement with perceived understanding of trials (p < 0.001), and willingness to join a clinical trial (p = 0.002). Trialists valued an understanding of the rationale for clinical trials and of randomization, placebo, and patient protection, but those particular topics were poorly understood by patients. Patient knowledge about clinical trials is poor, including knowledge of several concepts ranked important by clinical trialists. The findings suggest that when developing education interventions, emphasis should be placed on the topics most directly related to patient care, and factors such as age and education level should be considered.

The National Cancer Institute demonstrated its new Cancer Clinical Trials Education Program (CCTEP) at the American Association of Social Workers annual meeting in San Diego, California, in May 1998. The goal was to develop a cadre of social workers who could facilitate community outreach and health professional training using CCTEP. This article is an abridged version of that presentation. It raises awareness of the important role social workers can play in clinical trials recruitment, gives an overview of clinical trials, and suggests ways social workers can help facilitate accrual to clinical trials. Clinical trials are the gold standard by which new therapies are tested (Fisher, 1966; Hill, 1962; Kramer & Shapiro, 1984; Shapiro & Louis, 1983). The relatively small number of patients who are willing and eligible to participate in clinical trials and the large number of trials competing to accrue those patients (Antman, Schnipper, & Frei, 1988; Ellis, 1996; Smith & Ho, 1996) delay the flow of innovative treatments to the public. Fewer than 5 percent of patients age 20 and over participate in clinical trials (Cheson, 1991; Taylor et al, 1994). Of patients age 50 and over, the age group with the greatest cancer incidence, only 1.5 percent participate in trials (Avorn, 1997; Bugeja, Kumar, & Banerjee, 1997; Giovanazzi-Bannon, Radamaker, Lai, & Benson, 1994; McNeely & Clements, 1994; Tejeda et al., 1996). It has been estimated that if adult patients participating in clinical trials for the most common cancers could be increased to 10 percent, accrual for most studies could be completed in a year instead of the three to five years now required (personal communication with V. T., DeVita, 1998, professor, internal medical oncology, Cancer Center, University of California, San Diego). Although clinical investigators despair over the slow pace of clinical research, social workers know only too well the despair experienced by patients, for whom time is running out. In a search for successful models to increase accrual to clinical trials, the pediatric model stands out. Cancer is comparatively rare in children, but nearly every child with cancer is evaluated for clinical trial eligibility, and up to 80 percent of pediatric patients are placed in trials (Cheson, 1991; Tejeda et al., 1996). As a result, dramatic progress has been made in the treatment of many pediatric cancers (Pediatric Oncology Group, 1992). One important difference in the pediatric environment is that the entire interdisciplinary health care team is focused on reducing the pediatric oncologic death rate through clinical research. As key members of the clinical team, social workers are uniquely positioned to facilitate patients' awareness and understanding of clinical trials. The social worker holds a trusted relationship with the patient in which the concept of clinical trials can be introduced and explored and the patient directed toward more in-depth sources of information (Deininger, 1990; Deininger, Collins, & Hubbard, 1989; Edwards, 1997; Johansen, Mayer, & Hoover, 1991). There are many common concepts inherent in all clinical trials that social workers can help patients understand and encourage them to explore (Macomber & Sadler, 1997). OVERVIEW OF CLINICAL TRIALS One way to compensate for the difficult-to-control variables in human research is a highly structured and rigidly followed clinical trial protocol. The exact treatments to be offered in each arm of the study, the timetable for administration of the experimental and control interventions, the exact data-collection points, the duration and points of follow-up, and many other disease-specific characteristics are clearly delineated in the study methodology and rigidly followed (Meili, 1991). Having a large sample, a randomized trial design, and a highly sophisticated statistical analysis of the outcome data are three other ways to compensate for these difficult-to-control variables (Barnett et al. …

Better design leads to better results - Importance of virological outcome design in clinical trials for antiviral treatment of coronavirus disease 2019.

My colleague and then Society for Clinical Trials (SCT) board member Kay Dickersin called me in spring of 2003 to ask a favor: would I consider taking on the editorship of the society journal, Controlled Clinical Trials? I protested vigorously; too much time, I said, not enough help, no support, I had papers to write, etc, etc. All true, but I knew I would say yes. Editing had long been in my blood, and to be able to continue the strong journal tradition established by Curt Meinert, the founding editor, Janet Wittes and then Jim Neaton was too tempting. Now, 10 years and over 660 published articles later, I am glad I did. But had I known then what I was about to face, I might have demurred. Why? The society was in negotiation with the publisher Elsevier to renew the journal contract. A decision was soon to be made – with my concurrence – that we should jump ship. This meant that I was not about to take over Controlled Clinical Trials, but rather a new journal with a new name, no Medline listing, no impact factor, no institutional subscriptions, no articles in the queue, no staff, little money, and an empty Excel spreadsheet to manage articles that would somehow appear from somewhere. What we did have was a dedicated editorial board – all of whom moved to the new journal – and a society membership that provided the initial subscriber base and source of articles. Looking at the contents of the first issue, one sees what that meant: a lively and eclectic collection of articles and commentaries that still inform and entertain, every one written by someone in the SCT or a colleague. There was a mix of methodology; one on dynamic treatment regimens by Lavori and Dawson [1], three on data monitoring committees, two from the DAMOCLES group (Data Monitoring Committees: Lessons, Ethics, Statistics) [2–4], a delightful retrospective and accompanying editorial on the Salk polio vaccine trial [5,6], and a very nice article on the groundbreaking model of ‘coverage under evidence development’ embodied by the National Emphysema Treatment Trial [7], followed by four terrific commentaries. But my favorite, 10 years later, was a profile of a founding SCT member, Paul Meier [8], by my colleague and erstwhile associate editor Harry Marks [9], neither of whom survived the ensuing decade. That interview, which I commend to all readers who missed it, was a tribute to both Paul and Harry’s wide-ranging scholarship, knowledge, and humanity, and provided rich material for Paul’s obituaries after his death in 2011. That first issue was a harbinger of a decade spent exploring in the journal the multifaceted nature and the scholarship around the methods of human experimentation, which encompasses the fields of statistics, medicine, biology, ethics, epidemiology, economics, history, law, policy, sociology, study design, inference, philosophy, informatics, and yet other fields, with a soupcon of passion. It is hard not to enjoy playing in that sandbox. But as I was warned by a sage colleague at the time, it is not the inaugural issue that tries editors’ souls, but the second one. And so it was. This forced me to contemplate some existential questions, like ‘What is the minimum number of articles that can legitimately be considered a journal ‘‘issue?’’’. The answer had a surprisingly concrete foundation; the number of pages whose thickness was sufficient to fit the words Clinical Trials on the spine of the bound journal. That turned out to be about five articles, the precise number comprising the second issue, and several thereafter. I confess this led to occasional musing about the joys of heavier gauge paper. A medical editor colleague, puzzled when I told him I was taking on this job, asked what there was left to learn about clinical trials. The 600+ articles published since then in this journal represent just a first chapter of an answer. Amazingly, the need to develop further the theory and application of randomized clinical trials is now a pressing national priority. With health-care costs crushing the nation’s economy without concomitant return, and a clinical trial model involving the creation of a parallel care system that is similarly unsustainable,

British HIV Association (BHIVA) guidelines for the treatment of HIV-infected adults with antiretroviral therapy.

Background: During the COVID-19 pandemic, clinical trial recruitment cannot be carried out due to travel restrictions, transmission risks and other factors, resulting in the stagnation of a large number of ongoing or upcoming clinical trials. Objective: An intelligent screening app was developed using artificial intelligence technology to rapidly pre-screen potential patients for phase I solid tumor drug clinical trials. Methods: A total of 429 screening process records were collected from 27 phase I solid tumor drug clinical trials at the First Affiliated Hospital of Bengbu Medical College from April 2018 to May 2021. Features of the experimental data were analyzed, and the collinearity (principal component analysis) and strong correlation (χ2 test) among features were eliminated. XGBoost, Random Forest, and Naive Bayes were used to sort the weight importance of features. Finally, the pre-screening models were constructed using classification machine learning algorithm, and the optimal model was selected. Results: Among the 429 screening records, 33 were data generated by repeated subject participation in different clinical trials, and of the remaining 396 screening records, 246 (62.12%) were screened successfully. The gold standard for subject screening success is the final judgment made by the principal investigator (PI) based on the clinical trial protocol. A Venn diagram was used to identify the important feature intersections of machine learning algorithms. After intersecting the top 15 characteristic variables of different feature screening models, 9 common variables were obtained: age, sex, distance from residence to the central institution, tumor histology, tumor stage, tumorectomy, the interval from diagnosis/postoperative to screening, chemotherapy, and ECOG (Eastern Cooperative Oncology Group, ECOG) score. To select the optimal subset, the 9 important feature variables were expanded to 12 and 15 feature subsets, and the performance of different feature subsets under different machine learning models was validated. The results showed that optimal performance, accuracy and practicability were achieved using XGBoost with the 12 feature subset. The final model could accurately predict the screening success rates in both internal (AUC =3D 0.895) and external (AUC =3D 0.796) validation, and has been transformed into a convenient tool to facilitate its application in the clinical settings. Subjects with a probability exceeding or equaling to the threshold in the final model had a higher probability to be successfully screened. Conclusion: Based on the optimal model, we created an online prediction calculator and visualization app -- ISSP (Intelligent Screening Service Platform), which can rapidly screen patients for phase I solid tumor drug clinical trials. ISSP can effectively solve the problem of space and time interval. On the mobile terminal, it realizes the matching between clinical trial projects and patients, and completes the rapid screening of clinical trial subjects, so as to obtain more clinical trial subjects. As an auxiliary tool, ISSP optimizes the screening process of clinical trials and provides more convenient services for clinical investigators and patients.

The International Committee of Medical Journal Editors (ICMJE) believes there is an ethical obligation to responsibly share data generated by interventional clinical trials because trial participants have put themselves at risk. In January 2016 we published a proposal aimed at helping to create an environment in which the sharing of deidentified individual participant data becomes the norm. In response to our request for feedback we received many comments from individuals and groups. (1) Some applauded the proposals while others expressed disappointment they did not more quickly create a commitment to data sharing. Many raised valid concerns regarding the feasibility of the proposed requirements, the necessary resources, the real or perceived risks to trial participants, and the need to protect the interests of patients and researchers. It is encouraging that data sharing is already occurring in some settings. Over the past year, however, we have learned that the challenges are substantial and the requisite mechanisms are not in place to mandate universal data sharing at this time. Although many issues must be addressed for data sharing to become the norm, we remain committed to this goal. Therefore, ICMJE will require the following as conditions of consideration for publication of a clinical trial report in our member journals: i) As of July 1, 2018 manuscripts submitted to ICMJE journals that report the results of clinical trials must contain a data sharing statement as described below. ii) Clinical trials that begin enrolling participants on or after January 1, 2019 must include a data sharing plan in the trials registration. The ICMJE's policy regarding trial registration is explained at www.icmje.org/recommendations/browse/ publishing-and-editorial-issues/clinical-trial-registration.html. If the data sharing plan changes after registration this should be reflected in the statement submitted and published with the manuscript, and updated in the registry record. Data sharing statements must indicate the following: whether individual deidentified participant data (including data dictionaries) will be shared; what data in particular will be shared; whether additional, related documents will be available (e.g. study protocol, statistical analysis plan, etc.); when the data will become available and for how long; by what access criteria data will be shared (including with whom, for what types of analyses and by what mechanism). Illustrative examples of data sharing statements that would meet these requirements are in the Table 1. These initial requirements do not yet mandate data sharing, but investigators should be aware that editors may take into consideration data sharing statements when making editorial decisions. These minimum requirements are intended to move the research enterprise closer to fulfilling our ethical obligation to participants. Some ICMJE member journals already maintain, or may choose to adopt, more stringent requirements for data sharing. Sharing clinical trial data is one step in the process articulated by the World Health Organization (WHO) and other professional organizations as best practice for clinical trials: universal prospective registration; public disclosure of results from all clinical trials (including through journal publication); and data sharing. Although universal compliance with the requirement to prospectively register clinical trials has not yet been achieved and requires continued emphasis, we must work towards fulfilling the other steps of best practice as well-including data sharing. As we move forward into this new norm where data are shared, greater understanding and collaboration among funders, ethics committees, journals, trialists, data analysts, participants, and others will be required. We are currently working with members of the research community to facilitate practical solutions to enable data sharing. …

The International Committee of Medical Journal Editors (ICMJE) believes there is an ethical obligation to responsibly share data generated by interventional clinical trials because trial participants have put themselves at risk. In January 2016 we published a proposal aimed at helping to create an environment in which the sharing of deidentified individual participant data becomes the norm. In response to our request for feedback we received many comments from individuals and groups. (1) Some applauded the proposals while others expressed disappointment they did not more quickly create a commitment to data sharing. Many raised valid concerns regarding the feasibility of the proposed requirements, the necessary resources, the real or perceived risks to trial participants, and the need to protect the interests of patients and researchers. It is encouraging that data sharing is already occurring in some settings. Over the past year, however, we have learned that the challenges are substantial and the requisite mechanisms are not in place to mandate universal data sharing at this time. Although many issues must be addressed for data sharing to become the norm, we remain committed to this goal. Therefore, ICMJE will require the following as conditions of consideration for publication of a clinical trial report in our member journals: i) As of July 1, 2018 manuscripts submitted to ICMJE journals that report the results of clinical trials must contain a data sharing statement as described below. ii) Clinical trials that begin enrolling participants on or after January 1, 2019 must include a data sharing plan in the trials registration. The ICMJE's policy regarding trial registration is explained at www.icmje.org/recommendations/browse/ publishing-and-editorial-issues/clinical-trial-registration.html. If the data sharing plan changes after registration this should be reflected in the statement submitted and published with the manuscript, and updated in the registry record. Data sharing statements must indicate the following: whether individual deidentified participant data (including data dictionaries) will be shared; what data in particular will be shared; whether additional, related documents will be available (e.g. study protocol, statistical analysis plan, etc.); when the data will become available and for how long; by what access criteria data will be shared (including with whom, for what types of analyses and by what mechanism). Illustrative examples of data sharing statements that would meet these requirements are in the Table 1. These initial requirements do not yet mandate data sharing, but investigators should be aware that editors may take into consideration data sharing statements when making editorial decisions. These minimum requirements are intended to move the research enterprise closer to fulfilling our ethical obligation to participants. Some ICMJE member journals already maintain, or may choose to adopt, more stringent requirements for data sharing. Sharing clinical trial data is one step in the process articulated by the World Health Organization (WHO) and other professional organizations as best practice for clinical trials: universal prospective registration; public disclosure of results from all clinical trials (including through journal publication); and data sharing. Although universal compliance with the requirement to prospectively register clinical trials has not yet been achieved and requires continued emphasis, we must work towards fulfilling the other steps of best practice as well-including data sharing. As we move forward into this new norm where data are shared, greater understanding and collaboration among funders, ethics committees, journals, trialists, data analysts, participants, and others will be required. We are currently working with members of the research community to facilitate practical solutions to enable data sharing. …

This article refers to 'Cardiovascular and non-cardiovascular death distinction: the utility of troponin beyond N-terminal pro-B-type natriuretic peptide. Findings from the BIOSTAT-CHF study' by J.P. Ferreira et al., published in this issue on pages 81–89. In nearly every issue of nearly every heart failure (HF) journal, one may find scientific reports regarding biomarker testing. Indeed, over the past 20 years, the role of 'biomarker science' has taken on a life of its own in the HF literature: analyses have focused on a broad range of topics including the role of HF markers to support clinical judgement, provide information regarding mechanism of disease, and to predict complications of the diagnosis. Increasingly, HF biomarker studies have focused on the role of circulating blood substances to reliably predict adverse outcomes such as death or HF progression; these applications may have unique uses, one of which is a growing role as a tool in HF clinical trials. As we have recently discussed,1 measurement of B-type natriuretic peptide (BNP) or its amino-terminal pro-peptide equivalent (NT-proBNP) is now ubiquitous in development programmes for HF therapeutics. Given associations with presence or severity of HF, concentrations of both peptides are routinely used (i) as an inclusion criterion to ensure enrolment of appropriate patients; (ii) as a measure of drug toxicity (especially in oncology trials); (iii) as an outcome or endpoint measure; (iv) to explain efficacy of therapeutics; and (v) as a target for therapy.1 Natriuretic peptides are by far the most frequently used for these applications.1 That said, other biomarkers such as high-sensitivity cardiac troponin are also increasingly used. Of these, the first – namely to include appropriate patients in studies – is of great interest. In the present issue of the Journal, Ferreira and colleagues report results from a substudy out of the BIOSTAT-CHF (A systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) trial.2 In this analysis, the investigators sought to develop risk models to predict cardiovascular and non-cardiovascular events. Both models included clinical and laboratory variables; and interestingly, both models incorporated NT-proBNP, however, high-sensitivity cardiac troponin T (hs-cTnT) helped predict cardiovascular death only. These findings are important and potentially informative to clinical trialists regarding trial design when it comes to enrolment criteria in particular, for the achievement of desired outcomes. When used to include patients in trials, a biomarker that is considered prognostic is 'used to identify likelihood of a clinical event, disease recurrence or progression in patients who have the disease or medical condition of interest'.3 The ability of biomarkers such as hs-cTnT to predict risk allows for enrolment of study patients with higher event rates, minimizing Type II error in underpowered studies,1 as elevated concentrations are associated with worse prognosis.4, 5 And so, as Ferreira and colleagues demonstrated,2 a clinical trialist interested in cardiovascular death as a clinical trial endpoint may opt to use hs-cTnT concentrations as an inclusion criterion in combination with BNP and NT-proBNP. The potential for use of biomarker inclusion criteria concentration to attain a certain event rate is vast, and intuition would tell us that higher concentration cut-offs are associated with higher event rates, but this has yet to be examined across the universe of HF clinical trials. Importantly, biomarkers are a continuous variable, with higher concentrations less commonly found, but perhaps more tightly associated with risk. When considering biomarker cut-points for risk enrichment it is imperative to choose concentrations that (i) identify high-risk patients, but (ii) that risk is not 'too high' that the patients are not representative of the general population. Hitting the sweet spot to ensure enrolment of patients likely to benefit is important: in the Prospective Angiotensin–Neprilysin Inhibition versus Enalapril in Heart Failure (PARADIGM-HF) trial, HF with reduced ejection fraction (EF) patients were enrolled based on a modestly elevated BNP or NT-proBNP; with high event rates, this allowed for more timely completion of this event-driven study.1, 6 In the Spironolactone for Heart Failure with Preserved Ejection Fraction (TOPCAT) trial, in HF with preserved EF patients enrolled based on elevated BNP (≥100 pg/mL) or NT-proBNP (≥360 pg/mL), significant benefit was seen (hazard ratio 0.65; 95% confidence interval 0.49–0.87, P = 0.003).7 Interestingly, varying responses to spironolactone as a function of natriuretic peptide values was seen in TOPCAT, such that patients with intermediate baseline concentrations of BNP or NT-proBNP benefitted most, while those with considerably higher concentrations were not treatment responsive. Thus, biomarkers may also identify patients 'too sick to benefit' or those with a treatment non-responsive disease: the lack of response to spironolactone in those with very elevated natriuretic peptide concentrations might reflect a sinister unrecognized diagnosis such as cardiac amyloidosis. Though biomarkers may provide utility for clinical trials, caveats abound, and a note of caution is needed: it is not so simple as to merely include a biomarker in the design of a study and expect similar performance across patient types. For example, several populations need special consideration whose natriuretic peptide or troponin concentrations are higher or lower than would be expected based on the severity of HF. The former includes patients with atrial fibrillation,8 chronic kidney disease,9 and elderly patients.10 Conversely, Black patients reportedly have lower (in some cases undetectable) concentrations of BNP and NT-proBNP than White patients; reasons for which remain unclear.11 As well, concentrations of natriuretic peptides are lower in obese patients.10 Lastly, effects of therapies such as sacubitril/valsartan may result in unpredictable changes in certain biomarkers.12 Thus, while we strongly advocate for use of biomarkers in clinical trial programmes, we also emphasize the need for caution and attention to detail when doing so. The data by Ferreira and colleagues suggest different utilities of NT-proBNP and hs-cTnT to predict different outcomes. These results suggest clinical trialists might leverage each biomarker for different purposes, which is an appealing option. More widespread use of accurate biomarkers to enhance trial enrolment would be expected to improve inclusion of correct patients, allow for greater representation of the general population, include patients normally underrepresented in clinical trials, and strengthen the science being delivered. That is interesting and exciting. There is enthusiasm, to be sure, about the use of biomarkers to enrich trials and speed their conclusion through enrolment of patients at higher risk for events but little has been done to standardize the approaches used in trials that use biomarkers. Furthermore, despite a literal universe of biomarkers that might predict cause-specific outcomes (such as cardiac vs. non-cardiac death), this application has not been leveraged with most studies only using BNP or NT-proBNP. Standardization of when and how biomarkers are included as a tool for clinical trial design would be a welcome – and much needed – step. Dr. Januzzi is supported in part by the Hutter Family Professorship (Boston, MA, USA). Dr. Ibrahim is supported in part by the Dennis and Marilyn Barry Fund in Cardiology Research (Boston, MA, USA). Conflict of interest: J.L.J. is a Trustee of the American College of Cardiology, a Board member of Imbria Pharmaceuticals, has received grant support from Novartis Pharmaceuticals, Roche Diagnostics, Abbott, Singulex and Prevencio, consulting income from Abbott, Janssen, Novartis, Pfizer, Merck, and Roche Diagnostics, and participates in clinical endpoint committees/data safety monitoring boards for Abbott, AbbVie, Amgen, Boehringer-Ingelheim, Janssen, and Takeda. N.E.I. has received consulting income from Novartis.

Real World Use of IDH2- Targeted Inhibitors in a Single Academic Medical Center Experience Since Enasidenib FDA-Approval

The field of ostracism research is witnessing a growing interest in understanding ostracism – being excluded and ignored – as a lived experience outside of the laboratory context. How do researchers draw valid causal conclusions about naturally occurring experiences of ostracism without relying on experimental designs? In this article, we draw on insights from the well-established causal inference framework to emphasize a critical step for strengthening causal rigor: stating the causal estimand. Using an intuitive example, we illustrate what a causal estimand is, how to define it, and why it matters. With this article, we encourage readers to think clearly about causal estimands before conducting any data analysis. This conceptual step holds the potential for enhancing the rigor and precision of research studying ostracism as a naturally occurring phenomenon.

Patient adherence to the protocol in clinical trials is a critical factor in determining the outcome of the trial. An investigational eFlow nebulizer was used with an eTrack controller featuring adherence-monitoring capability (PARI Pharma, Germany) to deliver drug to the lungs via inhalation and to objectively monitor adherence. The inhalation data were available via the PARItrack web portal and allowed evaluation and graphical visualization of each patient's adherence. The clinical trial involved 10 patients who self-administered drug in their homes twice a day for 6 weeks. The eTrack controller automatically transferred data of each nebulization via a Bluetooth connection to a 2netHub (Qualcomm) and via GSM to a server. The data could be accessed through the PARItrack web portal. The clinical trial nurses, investigators and CRO could remotely monitor adherence and an alert system was established for information on missed inhalations. This allowed individual adherence issues to be addressed immediately and corrective actions to be implemented to ensure ongoing adherence. This setup resulted in a high adherence rate over the duration of the clinical trial. The overall mean adherence rate was 96 %. Seven out of ten patients had an adherence rate of 100 %, two had 95 %, and one had 64 %. This compares with a reported adherence rate of 78 % in the first 4 weeks of another clinical trial using a monitoring nebulizer without remote data access and intervention [Fuchs et al. ECFC 2011]. The high adherence rates achieved in this study demonstrate the utility of remote monitoring and immediate intervention to achieve good adherence in a clinical trial.