Methods for non-proportional hazards in clinical trials: A systematic review.

For the analysis of time-to-event data, frequently used methods such as the log-rank test or the Cox proportional hazards model are based on the proportional hazards assumption, which is often debatable. Although a wide range of parametric and non-parametric methods for non-proportional hazards has been proposed, there is no consensus on the best approaches. To close this gap, we conducted a systematic literature search to identify statistical methods and software appropriate under non-proportional hazard. Our literature search identified 907 abstracts, out of which we included 211 articles, mostly methodological ones. Review articles and applications were less frequently identified. The articles discuss effect measures, effect estimation and regression approaches, hypothesis tests, and sample size calculation approaches, which are often tailored to specific non-proportional hazard situations. Using a unified notation, we provide an overview of methods available. Furthermore, we derive some guidance from the identified articles.

Sample size recalculation based on the prevalence in a randomized test-treatment study.

BACKGROUND: Randomized test-treatment studies aim to evaluate the clinical utility of diagnostic tests by providing evidence on their impact on patient health. However, the sample size calculation is affected by several factors involved in the test-treatment pathway, including the prevalence of the disease. Sample size planning is exposed to strong uncertainties in terms of the necessary assumptions, which have to be compensated for accordingly by adjusting prospectively determined study parameters during the course of the study. METHOD: An adaptive design with a blinded sample size recalculation in a randomized test-treatment study based on the prevalence is proposed and evaluated by a simulation study. The results of the adaptive design are compared to those of the fixed design. RESULTS: The adaptive design achieves the desired theoretical power, under the assumption that all other nuisance parameters have been specified correctly, while wrong assumptions regarding the prevalence may lead to an over- or underpowered study in the fixed design. The empirical type I error rate is sufficiently controlled in the adaptive design as well as in the fixed design. CONCLUSION: The consideration of a blinded recalculation of the sample size already during the planning of the study may be advisable in order to increase the possibility of success as well as an enhanced process of the study. However, the application of the method is subject to a number of limitations associated with the study design in terms of feasibility, sample sizes needed to be achieved, and fulfillment of necessary prerequisites.

Classification of Companion Diagnostics: A New Framework for Biomarker-Driven Patient Selection.

BACKGROUND: Modern personalized medicine strategies builds on therapy companion diagnostics to stratify patients into subgroups with differential benefit/risk. In general, stratification for drug response implies a treatment-by-subgroup interaction. This interaction is usually suggested by the drug's mechanism of action and investigated in pharmacological research or in clinical studies. In these candidate genes or pathway approaches, either biological reasons for a differential benefit/risk or statistical interaction regarding a pharmacological or clinical endpoint or both may be given. For successful drug approval, demonstration of a positive benefit/risk balance in the intended patient population is required. This also applies to situations with biomarker-selected populations. However, further regulatory considerations relate to the usefulness and plausibility of the selected patients and benefit/risk extrapolations or alternative therapy options in biomarker-negative populations. METHODS: To facilitate the specification of regulatory requirements and support the design of clinical development programmes, a systematic classification of biomarker-drug pairs is needed, in particular with regard to the expected underlying molecular mechanism and the clinical evidence. RESULTS: A classification of five biomarker-drug categories is proposed related to increasing evidence on the biomarker's predictive value in relation to a specific drug. We classified biomarkers into five ascending categories with increasing evidence on the predictive nature of the biomarker in relation to a specific drug according to the comparative pharmacological and clinical evidence. CONCLUSIONS: The proposed classification will facilitate regulatory decision-making and support drug development with respect to biomarker-related subgrouping, both, during clinical programme and at the time of marketing authorization application, since the grade of evidence on the differential power of the biomarker can be considered as an indicator for the usefulness of a biomarker-related subgrouping.

Effect size estimates from umbrella designs: Handling patients with a positive test result for multiple biomarkers using random or pragmatic subtrial allocation.

Umbrella trials have been suggested to increase trial conduct efficiency when investigating different biomarker-driven experimental therapies. An overarching platform is used for patient screening and subsequent subtrial allocation according to patients' biomarker status. Two subtrial allocation schemes for patients with a positive test result for multiple biomarkers are (i) the pragmatic allocation to the eligible subtrial with the currently fewest included patients and (ii) the random allocation to one of the eligible subtrials. Obviously, the subtrials compete for such patients which are consequently underrepresented in the subtrials. To address questions of the impact of an umbrella design in general as well as with respect to subtrial allocation and analysis method, we investigate an umbrella trial with two parallel group subtrials and discuss generalisations. First, we analytically quantify the impact of the umbrella design with random allocation on the number of patients needed to be screened, the biomarker status distribution and treatment effect estimates compared to the corresponding gold standard of an independent parallel group design. Using simulations and real data, we subsequently compare both allocation schemes and investigate weighted linear regression modelling as possible analysis method for the umbrella design. Our results show that umbrella designs are more efficient than the gold standard. However, depending on the biomarker status distribution in the disease population, an umbrella design can introduce differences in estimated treatment effects in the presence of an interaction between treatment and biomarker status. In principle, weighted linear regression together with the random allocation scheme can address this difference though it is difficult to assess if such an approach is applicable in practice. In any case, caution is required when using treatment effect estimates derived from umbrella designs for e.g. future trial planning or meta-analyses.

Blinded continuous information monitoring of recurrent event endpoints with time trends in clinical trials.

Blinded sample size re-estimation and information monitoring based on blinded data has been suggested to mitigate risks due to planning uncertainties regarding nuisance parameters. Motivated by a randomized controlled trial in pediatric multiple sclerosis (MS), a continuous monitoring procedure for overdispersed count data was proposed recently. However, this procedure assumed constant event rates, an assumption often not met in practice. Here we extend the procedure to accommodate time trends in the event rates considering two blinded approaches: (a) the mixture approach modeling the number of events by a mixture of two negative binomial distributions and (b) the lumping approach approximating the marginal distribution of the event counts by a negative binomial distribution. Through simulations the operating characteristics of the proposed procedures are investigated under decreasing event rates. We find that the type I error rate is not inflated relevantly by either of the monitoring procedures, with the exception of strong time dependencies where the procedure assuming constant rates exhibits some inflation. Furthermore, the procedure accommodating time trends has generally favorable power properties compared with the procedure based on constant rates which stops often too late. The proposed method is illustrated by the clinical trial in pediatric MS.

Enrichment designs using placebo nonresponders.

Enrichment designs that select placebo nonresponders have gained much attention during the last years in areas with high placebo response rates, eg, in depression. Proposals were made that re-randomize patients who did not respond to placebo during a first study phase as the sequential parallel design (SPD). This design uses in a second phase an enriched patient population where the treatment effect is expected to be more pronounced. This may be problematic if an effect in the overall population is claimed. Proposals were made to combine the treatment effects in the overall population from study phase 1 and the enriched population from study phase 2, alleviating but not solving the issue of a potential selection bias. This paper shows how this bias corresponding to the effect difference between the overall population and the enriched population depends on the variability of a potential subject-by-treatment interaction. Sample sizes are given, which lead to a significant result in the combining test with a given probability if actually the average effect in the overall population is zero. If, on the other hand, no subject-by-treatment interaction is given, the enrichment is shown to be inefficient. We conclude that enrichment designs using placebo nonresponders are not able to claim a positive average effect in the overall population if a subject-by-treatment interaction cannot be excluded. It cannot be used to demonstrate positive efficacy in the overall population in a pivotal phase III trial but may be used in early phases to demonstrate varying treatment effects between patients.

Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19.

The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this article, we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs.

Adaptive trial designs in diagnostic accuracy research.

The aim of diagnostic accuracy studies is to evaluate how accurately a diagnostic test can distinguish diseased from nondiseased individuals. Depending on the research question, different study designs and accuracy measures are appropriate. As the prior knowledge in the planning phase is often very limited, modifications of design aspects such as the sample size during the ongoing trial could increase the efficiency of diagnostic trials. In intervention studies, group sequential and adaptive designs are well established. Such designs are characterized by preplanned interim analyses, giving the opportunity to stop early for efficacy or futility or to modify elements of the study design. In contrast, in diagnostic accuracy studies, such flexible designs are less common, even if they are as important as for intervention studies. However, diagnostic accuracy studies have specific features, which may require adaptations of the statistical methods or may lead to specific advantages or limitations of sequential and adaptive designs. In this article, we summarize the current status of methodological research and applications of flexible designs in diagnostic accuracy research. Furthermore, we indicate and advocate future development of adaptive design methodology and their use in diagnostic accuracy trials from an interdisciplinary viewpoint. The term "interdisciplinary viewpoint" describes the collaboration of experts of the academic and nonacademic research.

Efficient adaptive designs for clinical trials of interventions for COVID-19

The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this paper we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive design

Blood-based systems biology biomarkers for next-generation clinical trials in Alzheimer's disease
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Alzheimer's disease (AD)-a complex disease showing multiple pathomechanistic alterations-is triggered by nonlinear dynamic interactions of genetic/epigenetic and environmental risk factors, which, ultimately, converge into a biologically heterogeneous disease. To tackle the burden of AD during early preclinical stages, accessible blood-based biomarkers are currently being developed. Specifically, next-generation clinical trials are expected to integrate positive and negative predictive blood-based biomarkers into study designs to evaluate, at the individual level, target druggability and potential drug resistance mechanisms. In this scenario, systems biology holds promise to accelerate validation and qualification for clinical trial contexts of use-including proof-of-mechanism, patient selection, assessment of treatment efficacy and safety rates, and prognostic evaluation. Albeit in their infancy, systems biology-based approaches are poised to identify relevant AD "signatures" through multifactorial and interindividual variability, allowing us to decipher disease pathophysiology and etiology. Hopefully, innovative biomarker-drug codevelopment strategies will be the road ahead towards effective disease-modifying drugs.
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La Enfermedad de Alzheimer (EA) es una enfermedad compleja que presenta múltiples alteraciones patomecánicas, que se desencadena por interacciones dinámicas no lineales de factores de riesgo genéticos / epigenéticos y ambientales, los que, en definitiva, convergen en una enfermedad biológicamente heterogénea. Para hacer frente a la carga de la EA durante las etapas preclínicas tempranas, actualmente se están desarrollando biomarcadores sanguíneos de fácil accesibilidad. Específicamente, se espera que los ensayos clínicos de próxima generación integren biomarcadores sanguíneos predictivos tanto positivos como negativos en los diseños de los estudios para evaluar, a nivel individual, la capacidad de la droga objetivo y los posibles mecanismos de resistencia a los medicamentos. En este contexto, la biología de sistemas promete acelerar la validación y la calificación de su empleo en los ensayos clínicos, incluida la prueba del mecanismo, la selección de pacientes, la evaluación de la eficacia del tratamiento y los porcentajes de seguridad, y la evaluación pronóstica. A pesar de estar en sus comienzos, los enfoques basados en la biología de sistemas están preparados para identificar "firmas" de EA relevantes a través de la variabilidad multifactorial e interindividual, lo que nos permite descifrar la fisiopatología y la etiología de la enfermedad. Ojalá, las estrategias innovadoras conjuntas del desarrollo de biomarcadores y de medicamentos sean el camino adecuado para conseguir fármacos eficaces que modifiquen la enfermedad.

La maladie d'Alzheimer (MA) ­ maladie complexe présentant des altérations nombreuses pathomécaniques ­ est déclenchée par des interactions dynamiques non linéaires entre des facteurs de risques génétiques et épigénétiques et environnementaux qui, au bout du compte, aboutissent à une maladie biologiquement hétérogène. Pour réduire la charge de morbidité de la MA durant ses premiers stades précliniques, des biomarqueurs sanguins sont actuellement développés. Spécifiquement, la prochaine génération d'essais cliniques devrait intégrer ces biomarqueurs sanguins positifs ou négatifs prédictifs de la maladie dans des études qui auront pour but d'évaluer, à un niveau individuel, des cibles pouvant être traitées par des candidats médicaments et de potentiels mécanismes de résistance à ces médicaments. Dans ce contexte, la biologie des systèmes devrait permettre d'accélérer la validation et la qualification de leur utilisation dans les études cliniques ­ incluant la preuve du mécanisme d'action, la sélection des patients, la confirmation de l'efficacité du traitement et son niveau de sécurité, ainsi que l'évaluation pronostique. Bien que nous en soyons au tout début, les approches reposant sur la biologie des systèmes sont sur le point d'identifier des « signatures ¼ pertinentes de la MA grâce à des variables multifactorielles et interindividuelles, qui nous permettront d'élucider la pathophysiologie et l'étiologie de la maladie. Avec un peu de chance, les stratégies innovantes de codéveloppement de biomarqueurs et de médicaments nous mèneront vers des médicaments efficaces pour lutter contre la maladie.

A comparison of subgroup identification methods in clinical drug development: Simulation study and regulatory considerations.

With advancement of technologies such as genomic sequencing, predictive biomarkers have become a useful tool for the development of personalized medicine. Predictive biomarkers can be used to select subsets of patients, which are most likely to benefit from a treatment. A number of approaches for subgroup identification were proposed over the last years. Although overviews of subgroup identification methods are available, systematic comparisons of their performance in simulation studies are rare. Interaction trees (IT), model-based recursive partitioning, subgroup identification based on differential effect, simultaneous threshold interaction modeling algorithm (STIMA), and adaptive refinement by directed peeling were proposed for subgroup identification. We compared these methods in a simulation study using a structured approach. In order to identify a target population for subsequent trials, a selection of the identified subgroups is needed. Therefore, we propose a subgroup criterion leading to a target subgroup consisting of the identified subgroups with an estimated treatment difference no less than a pre-specified threshold. In our simulation study, we evaluated these methods by considering measures for binary classification, like sensitivity and specificity. In settings with large effects or huge sample sizes, most methods perform well. For more realistic settings in drug development involving data from a single trial only, however, none of the methods seems suitable for selecting a target population. Using the subgroup criterion as alternative to the proposed pruning procedures, STIMA and IT can improve their performance in some settings. The methods and the subgroup criterion are illustrated by an application in amyotrophic lateral sclerosis.

[Ethics committees in clinical trials involving medicinal products]. / Die Rolle von Ethikkommissionen bei der Bewertung klinischer Arzneimittelprüfungen.

Over the years, the role of ethics committees (ECs) in the review process of clinical trial applications (CTAs) has changed from being a collegial advisory body to a patient protection organisation with an authority character. While the law governing the medical profession in Germany only provides for an obligation for physicians to ask for an EC review in biomedical research on human beings, a negative opinion on the CTA does not lead to the inadmissibility of the research project from a legal point of view. In contrast, the German Medicinal Product Act (Arzneimittelgesetz, AMG) requires a favourable opinion as an approving assessment by the competent EC.The AMG defines both the elements of a clinical trial application to be reviewed by the EC as well as the principle grounds of non-acceptance to reject a favourable opinion. ECs that assess CTAs must be constituted in accordance with the state law and must be composed of interdisciplinary medical specialists, lawyers and methodologists. The main assessment criteria are a medically acceptable risk-benefit ratio, the appropriateness of the methods used, including biometric aspects, the requirements to be met by the study participants, such as their ability to give consent, the suitability of the investigators and trial facilities as well as the appropriateness of the written information with which the study participants are to be informed and give their consent.In spite of the already high degree of regulation, the applicability of the European Clinical Trial Regulation will result in even more detailed legal requirements for the composition and working procedures of an EC with the aim of further harmonising the assessment of CTAs in the EU.

On estimands and the analysis of adverse events in the presence of varying follow-up times within the benefit assessment of therapies.

The analysis of adverse events (AEs) is a key component in the assessment of a drug's safety profile. Inappropriate analysis methods may result in misleading conclusions about a therapy's safety and consequently its benefit-risk ratio. The statistical analysis of AEs is complicated by the fact that the follow-up times can vary between the patients included in a clinical trial. This paper takes as its focus the analysis of AE data in the presence of varying follow-up times within the benefit assessment of therapeutic interventions. Instead of approaching this issue directly and solely from an analysis point of view, we first discuss what should be estimated in the context of safety data, leading to the concept of estimands. Although the current discussion on estimands is mainly related to efficacy evaluation, the concept is applicable to safety endpoints as well. Within the framework of estimands, we present statistical methods for analysing AEs with the focus being on the time to the occurrence of the first AE of a specific type. We give recommendations which estimators should be used for the estimands described. Furthermore, we state practical implications of the analysis of AEs in clinical trials and give an overview of examples across different indications. We also provide a review of current practices of health technology assessment (HTA) agencies with respect to the evaluation of safety data. Finally, we describe problems with meta-analyses of AE data and sketch possible solutions.

Recent advances in methodology for clinical trials in small populations: the InSPiRe project.

Where there are a limited number of patients, such as in a rare disease, clinical trials in these small populations present several challenges, including statistical issues. This led to an EU FP7 call for proposals in 2013. One of the three projects funded was the Innovative Methodology for Small Populations Research (InSPiRe) project. This paper summarizes the main results of the project, which was completed in 2017.The InSPiRe project has led to development of novel statistical methodology for clinical trials in small populations in four areas. We have explored new decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework to compare costs with potential benefits, developed approaches for targeted treatment trials, enabling simultaneous identification of subgroups and confirmation of treatment effect for these patients, worked on early phase clinical trial design and on extrapolation from adult to pediatric studies, developing methods to enable use of pharmacokinetics and pharmacodynamics data, and also developed improved robust meta-analysis methods for a small number of trials to support the planning, analysis and interpretation of a trial as well as enabling extrapolation between patient groups. In addition to scientific publications, we have contributed to regulatory guidance and produced free software in order to facilitate implementation of the novel methods.

Precision pharmacology for Alzheimer's disease.

The complex multifactorial nature of polygenic Alzheimer's disease (AD) presents significant challenges for drug development. AD pathophysiology is progressing in a non-linear dynamic fashion across multiple systems levels - from molecules to organ systems - and through adaptation, to compensation, and decompensation to systems failure. Adaptation and compensation maintain homeostasis: a dynamic equilibrium resulting from the dynamic non-linear interaction between genome, epigenome, and environment. An individual vulnerability to stressors exists on the basis of individual triggers, drivers, and thresholds accounting for the initiation and failure of adaptive and compensatory responses. Consequently, the distinct pattern of AD pathophysiology in space and time must be investigated on the basis of the individual biological makeup. This requires the implementation of systems biology and neurophysiology to facilitate Precision Medicine (PM) and Precision Pharmacology (PP). The regulation of several processes at multiple levels of complexity from gene expression to cellular cycle to tissue repair and system-wide network activation has different time delays (temporal scale) according to the affected systems (spatial scale). The initial failure might originate and occur at every level potentially affecting the whole dynamic interrelated systems within an organism. Unraveling the spatial and temporal dynamics of non-linear pathophysiological mechanisms across the continuum of hierarchical self-organized systems levels and from systems homeostasis to systems failure is key to understand AD. Measuring and, possibly, controlling space- and time-scaled adaptive and compensatory responses occurring during AD will represent a crucial step to achieve the capacity to substantially modify the disease course and progression at the best suitable timepoints, thus counteracting disrupting critical pathophysiological inputs. This approach will provide the conceptual basis for effective disease-modifying pathway-based targeted therapies. PP is based on an exploratory and integrative strategy to complex diseases such as brain proteinopathies including AD, aimed at identifying simultaneous aberrant molecular pathways and predicting their temporal impact on the systems levels. The depiction of pathway-based molecular signatures of complex diseases contributes to the accurate and mechanistic stratification of distinct subcohorts of individuals at the earliest compensatory stage when treatment intervention may reverse, stop, or delay the disease. In addition, individualized drug selection may optimize treatment safety by decreasing risk and amplitude of side effects and adverse reactions. From a methodological point of view, comprehensive "omics"-based biomarkers will guide the exploration of spatio-temporal systems-wide morpho-functional shifts along the continuum of AD pathophysiology, from adaptation to irreversible failure. The Alzheimer Precision Medicine Initiative (APMI) and the APMI cohort program (APMI-CP) have commenced to facilitate a paradigm shift towards effective drug discovery and development in AD.

Regulatory issues with multiplicity in drug approval: Principles and controversies in a changing landscape.

Recently, new draft guidelines on multiplicity issues in clinical trials have been issued by European Medicine Agency (EMA) and Food and Drug Administration (FDA), respectively. Multiplicity is an issue in clinical trials, if the probability of a false-positive decision is increased by insufficiently accounting for testing multiple hypotheses. We outline the regulatory principles related to multiplicity issues in confirmatory clinical trials intended to support a marketing authorization application in the EU, describe the reasons for an increasing complexity regarding multiple hypotheses testing and discuss the specific multiplicity issues emerging within the regulatory context and being relevant for drug approval.

Commentary on the MID3 Good Practices Paper.

During the last 10 years the European Medicines Agency (EMA) organized a number of workshops on modeling and simulation, working towards greater integration of modeling and simulation (M&S) in the development and regulatory assessment of medicines. In the 2011 EMA - European Federation of Pharmaceutical Industries and Associations (EFPIA) Workshop on Modelling and Simulation, European regulators agreed to the necessity to build expertise to be able to review M&S data provided by companies in their dossier. This led to the establishment of the EMA Modelling and Simulation Working Group (MSWG). Also, there was agreement reached on the need for harmonization on good M&S practices and for continuing dialog across all parties. The MSWG acknowledges the initiative of the EFPIA Model-Informed Drug Discovery and Development (MID3) group in promoting greater consistency in practice, application, and documentation of M&S and considers the paper is an important contribution towards achieving this objective.

Time-matched analysis of DNA adduct formation and early gene expression as predictive tool for renal carcinogenesis in methylazoxymethanol acetate treated Eker rats.

Genotoxic carcinogens pose great hazard to human health. Uncertainty of current risk assessment strategies and long latency periods between first carcinogen exposure and diagnosis of tumors have raised interest in predictive biomarkers. Initial DNA adduct formation is a necessary step for genotoxin induced carcinogenesis. However, as DNA adducts not always translate into tumorigenesis, their predictive value is limited. Here we hypothesize that the combined analysis of pro-mutagenic DNA adducts along with time-matched gene expression changes could serve as a superior prediction tool for genotoxic carcinogenesis. Eker rats, heterozygous for the tuberous sclerosis (Tsc2) tumor suppressor gene and thus highly susceptible towards genotoxic renal carcinogens, were continuously treated with the DNA alkylating carcinogen methylazoxymethanol acetate (MAMAc). Two weeks of MAMAc treatment resulted in a time-dependent increase of O6-methylguanine and N7-methylguanine adducts in the kidney cortex, which was however not reflected by significant expression changes of cyto-protective genes involved in DNA repair, cell cycle arrest or apoptosis. Instead, we found a transcriptional regulation of genes involved in the tumor-related MAPK, FoxO and TGF-beta pathways. Continuous MAMAc treatment for up to 6 months resulted in a mild but significant increase of cancerous lesions. In summary, the combined analysis of DNA adducts and early gene expression changes could serve as a suitable predictive tool for genotoxicant-induced carcinogenesis.

Disentangling estimands and the intention-to-treat principle.

Randomized controlled trials (RCTs) aim at providing reliable estimates of treatment benefit. Missing data and nonadherence to treatment are distinct problems that can substantially impede this task. In practice, the fact that the handling of missing data due to nonadherence affects the question that is addressed is often ignored. Estimands allow precisely predefining the question of interest. Estimands are definitions of that which is being estimated with regard to population, endpoint, and handling of postrandomization events (eg, nonadherence). Depending on the situation, different estimands are of relevance. Therefore, it is important that the intention-to-treat (ITT) principle, which is considered the gold standard for analyzing RCTs, does not restrict an RCT's primary objective to only one of several relevant estimands. Although much ambiguity is involved around what is considered to constitute the ITT principle, many associate ITT with completely following up all patients and including all data of all randomized patients as allocated into the analysis. This would restrict primary objectives to estimating the effect of treatment policy and is certainly not warranted in all situations. To maintain the advantage of having the clear recommendation to follow the ITT principle while allowing for various relevant estimands as primary objective, we argue that the appropriate way forward is to define ITT as including all randomized patients into the analysis set and each patient is to be allocated to his or her randomized treatment. This definition comprises the actual intent of ITT and can be fully implemented also in settings where complete follow-up is impossible.